release: v3.24.0 - Agent Evaluation Framework
Major addition: Complete agent evaluation framework with production-ready template. ## Added - **Resource Evaluation**: nao framework (score 3/5) - Identified critical gap: agent evaluation not documented - Technical challenge adjusted score 2/5 → 3/5 - All claims fact-checked (TypeScript 58.9%, Python 38.5%) - **Guide Section**: Agent Evaluation (guide/agent-evaluation.md, ~3K tokens) - Metrics: response quality, tool usage, performance, satisfaction - Patterns: logging hooks, unit tests, A/B testing, feedback loops - Example: analytics agent with built-in metrics - Tools: nao framework reference, Claude Code hooks integration - **AI Ecosystem**: Section 8.2 Domain-Specific Agent Frameworks - nao (Analytics Agents): Database-agnostic, built-in evaluation - Transposable patterns: context builder, evaluation hooks, DB integrations - **Template**: Analytics Agent with Evaluation (5 files, ~1K lines) - README: setup, usage, troubleshooting - Agent: SQL generator with evaluation criteria, safety rules - Hook: automated metrics logging (safety, performance, errors) - Script: analysis with stats, safety reports, recommendations - Report template: monthly evaluation format ## Changed - Agent Evaluation Guide: updated template references, verified links - Landing Site: templates count 110 → 114 - Version: 3.23.5 → 3.24.0 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Florian BRUNIAUX
parent
1fb783ebb8
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ef7cdd899e
15 changed files with 1782 additions and 16 deletions
48
CHANGELOG.md
48
CHANGELOG.md
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@ -8,7 +8,53 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
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<!-- New entries go here -->
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<!-- New entries go here -->
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## [3.24.0] - 2026-02-10
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### Added
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- **Resource Evaluation**: nao framework (`docs/resource-evaluations/nao-framework.md`)
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- Evaluated open-source framework for building analytics agents
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- Score: 3/5 (Moderate - Useful Complement)
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- Identified critical gap: Agent evaluation not documented in guide
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- Technical challenge by technical-writer agent adjusted score from 2/5 to 3/5
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- All technical claims fact-checked (TypeScript 58.9%, Python 38.5%, stack verified)
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- **New Guide Section**: Agent Evaluation (`guide/agent-evaluation.md`, ~3000 tokens)
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- **Why Evaluate Agents**: Quantify quality, compare configurations, build feedback loops
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- **Metrics to Track**: Response quality, tool usage, performance, user satisfaction
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- **Implementation Patterns**: Logging hooks, unit tests, A/B testing, feedback loops
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- **Example**: Analytics agent with built-in metrics collection
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- **Tools & References**: nao framework as reference, Claude Code hooks integration
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- Addresses critical gap identified in nao evaluation
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- Navigation: After `guide/ultimate-guide.md` Section 4 (Agents)
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- **AI Ecosystem Update**: Section 8.2 Domain-Specific Agent Frameworks (`guide/ai-ecosystem.md`)
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- New subsection after "Multi-Agent Orchestration Systems"
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- **nao (Analytics Agents)**: Database-agnostic framework with built-in evaluation
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- Transposable patterns: Context builder architecture, evaluation hooks, database integrations
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- Links to new `guide/agent-evaluation.md` for implementation details
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- Location: guide/ai-ecosystem.md lines 1612-1638
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- **Template**: Analytics Agent with Evaluation (`examples/agents/analytics-with-eval/`, 5 files)
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- **README.md**: Complete setup, usage, troubleshooting (production-ready)
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- **analytics-agent.md**: SQL query generator with evaluation criteria and safety rules
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- **hooks/post-response-metrics.sh**: Automated metrics logging (safety, performance, errors)
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- **eval/metrics.sh**: Analysis script for aggregating collected metrics
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- **eval/report-template.md**: Monthly evaluation report template
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- Demonstrates patterns from `guide/agent-evaluation.md` in complete implementation
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- Includes safety checks (destructive operations), performance monitoring, feedback loops
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### Changed
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- **Agent Evaluation Guide**: Updated template reference (line 434)
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- Changed "(coming soon)" to "with hooks, scripts, and report template"
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- Added reference to complete template in "Example" section (line 277)
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- All links verified and functional
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- **Landing Site**: Templates count synchronized
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- Updated index.html: 110 → 114 templates
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- Updated examples/index.html: 110 → 114 templates
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- Reflects addition of analytics-with-eval template (5 new files)
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## [3.23.5] - 2026-02-10
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@ -509,7 +509,7 @@ See [CONTRIBUTING.md](./CONTRIBUTING.md) for guidelines.
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---
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*Version 3.23.5 | February 2026 | Crafted with Claude*
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*Version 3.24.0 | February 2026 | Crafted with Claude*
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<!-- SEO Keywords -->
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<!-- claude code, claude code tutorial, anthropic cli, ai coding assistant, claude code mcp,
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2
VERSION
2
VERSION
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@ -1 +1 @@
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3.23.5
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3.24.0
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202
docs/resource-evaluations/nao-framework.md
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202
docs/resource-evaluations/nao-framework.md
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@ -0,0 +1,202 @@
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# Resource Evaluation: nao Framework
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**URL**: https://github.com/getnao/nao/
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**Type**: Open-source framework for building analytics agents
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**Evaluation Date**: 2026-02-10
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**Evaluator**: Claude Code (with technical-writer challenge)
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**Target Guide**: Claude Code Ultimate Guide
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---
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## Summary
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**nao** is an open-source framework for building and deploying analytics agents with a two-step architecture: build agent context via CLI tools, then deploy chat UI for end users to query data conversationally.
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**Key Features**:
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- Context builder supporting flexible data, metadata, documentation, and tool integrations
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- Database agnostic (PostgreSQL, BigQuery, Snowflake, Databricks)
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- Built-in evaluation framework with unit testing capabilities
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- Self-hosted deployment with Docker containerization
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- Native data visualization within chat interface
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- Tech stack: Fastify, Drizzle ORM, tRPC, React, shadcn UI (TypeScript 58.9%, Python 38.5%)
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---
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## Relevance Score: 3/5 (Moderate - Useful Complement)
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### Initial Score: 2/5 → Adjusted to 3/5 after technical challenge
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**Justification for 3/5**:
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**Relevance (+2 points)**:
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- ✅ **Transposable architecture patterns** to Claude Code agents (.claude/agents/)
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- ✅ **Evaluation framework** addresses critical gap in guide (no section on agent evaluation)
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- ✅ **Database context patterns** applicable to agents with DB integrations
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**Limitations (-2 points)**:
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- ❌ No direct integration with Claude Code CLI (not a plugin/MCP server)
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- ❌ Deployment scope (production chat UI) differs from guide's dev CLI focus
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**Partial overlap**: Agent concepts and evaluation patterns are transposable, but final product (deployed analytics UI) is not guide's focus.
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---
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## Comparative Analysis
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| Aspect | nao | Current Guide | Gap? |
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|--------|-----|---------------|------|
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| **Custom Agents** | ✅ Complete build + deploy framework | ✅ Extensive docs (.claude/agents/) | ➖ No |
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| **Agent Architecture** | ➕ Structured context builder pattern | ⚠️ No complex context patterns | ✅ **Minor gap** |
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| **Agent Evaluation** | ➕ Integrated framework (metrics, unit tests, feedback) | ❌ No mention | ✅ **Critical gap** |
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| **Database Integrations** | ➕ Native support: BigQuery, Snowflake, Databricks, PostgreSQL | ✅ Mentioned (database-branch-setup.md) | ➖ No |
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| **Agent Deployment** | ➕ Deployable chat UI with visualizations | ❌ Not covered (CLI focus) | ⚠️ Out of scope |
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| **Analytics-Specific** | ➕ Specialized for conversational analytics | ❌ No analytics focus | ➖ No |
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| **Context Management** | ✅ Context builder with docs/metadata | ✅ CLAUDE.md, .claude/rules/ | ➖ No |
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| **Self-Hosted** | ✅ Docker + deployment guides | ✅ Mentioned (security-hardening.md) | ➖ No |
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---
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## Integration Recommendations
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**Score 3/5 → Integrate (3 approaches)**
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### ✅ Priority 1: Dedicated Section (Recommended)
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**Create**: `guide/agent-evaluation.md` (~800 tokens)
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**Content**:
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- **Why Evaluate?**: Measure quality, track usage, identify bottlenecks
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- **Metrics to Track**: Response time, tool call success rate, context relevance, error rate
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- **Implementation Patterns**: Logging hooks, metrics aggregation, A/B testing, feedback collection
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- **Reference**: Mention nao as complete evaluation framework
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**Estimated tokens**: 500-800
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**Suggested timeline**: Week 1
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**Insertion point**: After `guide/ultimate-guide.md` section 4 (Agents)
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---
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### ✅ Priority 2: Agent Template with Evaluation (Optional)
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**Create**: `examples/agents/analytics-with-eval/`
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**Structure**:
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```
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analytics-with-eval/
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├── config.yaml # Agent config
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├── context.md # Agent instructions
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├── eval/
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│ ├── metrics.sh # Collect metrics
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│ └── report.md # Example report
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└── hooks/
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└── post_response.sh # Log response metrics
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```
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**Estimated tokens**: 300-400 (code + README)
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**Suggested timeline**: Week 2-3
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**Value**: Demonstrates concrete evaluation patterns
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---
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### ✅ Priority 3: Ecosystem Mention (Minimal)
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**Add**: Section "Domain-Specific Agent Frameworks" in `guide/ai-ecosystem.md`
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**Content**: 1 paragraph + link to nao
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**Estimated tokens**: 100-150
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**Suggested timeline**: Immediate
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**Suggested line**: After orchestration frameworks section (~line 2200)
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---
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## Technical Challenge Results
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The technical-writer agent identified several biases in the initial evaluation:
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### Missed Points in Initial Evaluation
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1. **Transposable agent architecture**: nao's `context builder` pattern applicable to Claude Code agents
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2. **Evaluation framework = critical gap**: Guide has NO mention of agent evaluation (metrics, testing, feedback)
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3. **Database context patterns**: Patterns for context injection from databases not documented
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### Score Justification
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**Correction**: 2/5 → **3/5** (Moderate)
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**Arguments for 3/5**:
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- Transposable architecture patterns (+1)
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- Evaluation framework addresses identified gap (+1)
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- Usable database context patterns (+0.5)
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- Open-source, well-documented (+0.5)
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### Gap "Agent Evaluation" Must Be Addressed
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**YES, the guide MUST have this section** because:
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- Devs create agents without knowing how to measure quality
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- Anthropic docs mention evaluations but not in Claude Code context
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- nao proves this is feasible and useful (production-ready)
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### Risks of Non-Integration
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1. **Evaluation gap remains undocumented** → Devs don't know how to measure agent quality
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2. **Database context patterns undocumented** → Devs reinvent already-proven patterns
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3. **Loss of credibility** → If evaluation becomes standard, guide will be behind
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### Why Initial Evaluation Was Biased
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1. **Confusion between scope and relevance**: Different scope ≠ not relevant
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2. **Focus on final product**: Evaluated nao as *competing product*, not *pattern source*
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3. **Underestimation of gaps**: Agent evaluation = critical gap not previously identified
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4. **Premature rejection**: "Don't integrate" despite identifying 2 major gaps
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**Lesson**: Evaluate resources for *transposable patterns*, not just direct integration.
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---
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## Fact-Check Results
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All technical claims verified by re-fetching GitHub repository:
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| Claim | Verified | Source |
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|-------|----------|--------|
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| TypeScript 58.9%, Python 38.5% | ✅ | Repository footer |
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| Stack: Fastify, Drizzle, tRPC, React, shadcn, TanStack Query | ✅ | "Stack" section |
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| Databases: PostgreSQL, BigQuery, Snowflake, Databricks | ✅ | Repository topics |
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| Evaluation framework with unit testing | ✅ | "Evaluation framework" section |
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| GitHub repo integration + Slack bot | ✅ | Quickstart + topics |
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| Docker containerization + self-hosted | ✅ | "Docker" section + docs |
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**Corrections made**: None (all initial claims were accurate)
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**Stats requiring external research**: None (all verifiable on GitHub page)
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---
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## Final Decision
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- **Final Score**: **3/5 (Moderate - Useful Complement)**
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- **Action**: **Integrate** via 3 approaches (priority 1 + 2 + 3)
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- **Confidence**: **High** (all claims fact-checked ✅)
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### Concrete Action Plan
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1. **Immediate** (today): Add mention in `guide/ai-ecosystem.md` section "Domain-Specific Agent Frameworks"
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2. **Week 1**: Create `guide/agent-evaluation.md` with patterns inspired by nao
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3. **Week 2-3**: Create template `examples/agents/analytics-with-eval/` with metrics
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### Added Value for Guide
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- ✅ Addresses critical gap (agent evaluation)
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- ✅ Adds transposable patterns (context builder, DB integrations)
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- ✅ Demonstrates complete lifecycle (build → eval → iterate)
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- ✅ References production-ready framework in specific domain
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---
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## Metadata
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**Evaluation performed with**: WebFetch (2×), Grep (3×), Task (technical-writer challenge)
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**Evaluation time**: ~15 minutes
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**Quality**: Complete challenge + fact-check ✅
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**Follow-up**: Implement integration recommendations (A, B, C)
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243
examples/agents/analytics-with-eval/README.md
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243
examples/agents/analytics-with-eval/README.md
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# Analytics Agent with Built-in Evaluation
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**Template**: Production-ready analytics agent with automated metrics collection
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**Use Case**: SQL query generation with quality tracking, performance monitoring, and safety validation
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**Related Guide**: [Agent Evaluation](../../../guide/agent-evaluation.md)
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---
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## What's Included
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| File | Purpose |
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|------|---------|
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| `analytics-agent.md` | Agent definition with evaluation criteria |
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| `hooks/post-response-metrics.sh` | Automated metrics logging after each response |
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| `eval/metrics.sh` | Analysis script for aggregating collected metrics |
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| `eval/report-template.md` | Template for monthly evaluation reports |
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---
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## Setup
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### 1. Copy Agent to Project
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```bash
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# Copy agent definition
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cp analytics-agent.md ~/.claude/agents/
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# Or for project-specific:
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cp analytics-agent.md /path/to/project/.claude/agents/
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```
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### 2. Install Hook
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```bash
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# Copy hook to project
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cp hooks/post-response-metrics.sh /path/to/project/.claude/hooks/
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# Make executable
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chmod +x /path/to/project/.claude/hooks/post-response-metrics.sh
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```
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### 3. Configure Hook Trigger
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Add to `.claude/settings.json`:
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```json
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{
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"hooks": {
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"postToolUse": [
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{
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"command": ".claude/hooks/post-response-metrics.sh",
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"enabled": true,
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"description": "Log analytics agent metrics"
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}
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]
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}
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}
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```
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### 4. Create Logs Directory
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```bash
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mkdir -p /path/to/project/.claude/logs
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```
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---
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## Usage
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### Invoke Agent
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```bash
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# In Claude Code session
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"Use analytics-agent to generate a SQL query for [task description]"
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```
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### Check Metrics
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```bash
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# View raw metrics log
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cat .claude/logs/analytics-metrics.jsonl
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# Run analysis
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./examples/agents/analytics-with-eval/eval/metrics.sh
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```
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### Generate Monthly Report
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```bash
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# Copy template
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cp eval/report-template.md reports/analytics-2026-02.md
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# Fill in metrics from metrics.sh output
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```
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---
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## Metrics Collected
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The hook automatically logs:
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| Metric | Description | Source |
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|--------|-------------|--------|
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| `timestamp` | ISO 8601 timestamp | System |
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| `query` | Generated SQL query | Agent response |
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| `exec_time` | Query execution time | Database |
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| `safety` | PASS/FAIL for destructive ops | Query analysis |
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| `row_count` | Rows returned | Database |
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| `error` | Error message if query failed | Database |
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**Log format**: JSONL (JSON Lines) in `.claude/logs/analytics-metrics.jsonl`
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|
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---
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## Example Metrics Output
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```bash
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$ ./eval/metrics.sh
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=== Analytics Agent Metrics Report ===
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Period: 2026-02-01 to 2026-02-10
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Total queries: 45
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Safety checks:
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- PASS: 42 (93%)
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- FAIL: 3 (7%)
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Execution time:
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- Mean: 2.3s
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- Median: 1.8s
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- P95: 5.2s
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- P99: 8.1s
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Common failures:
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1. DELETE without WHERE clause (2 occurrences)
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2. DROP TABLE in query (1 occurrence)
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Recommendations:
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- Review agent instructions to emphasize WHERE clause requirement
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- Add explicit prohibition on DROP operations
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```
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|
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---
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## Customization
|
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|
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### Modify Safety Checks
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|
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Edit `hooks/post-response-metrics.sh` line 12-16:
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|
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```bash
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# Add more patterns
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if echo "$QUERY" | grep -iE 'DELETE|DROP|TRUNCATE|ALTER'; then
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SAFETY="FAIL"
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fi
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```
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|
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### Add Custom Metrics
|
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|
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Extend the JSON log structure:
|
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|
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```bash
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echo "{
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\"timestamp\":\"$(date -Iseconds)\",
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\"query\":\"$QUERY\",
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\"your_metric\":\"$VALUE\"
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}" >> .claude/logs/analytics-metrics.jsonl
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```
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|
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### Change Log Location
|
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|
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Update `POST_RESPONSE_LOG` variable in hook script.
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|
||||
---
|
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|
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## Troubleshooting
|
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|
||||
### Hook Not Triggering
|
||||
|
||||
**Check**:
|
||||
1. Hook is executable: `ls -l .claude/hooks/*.sh`
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2. Hook is enabled in settings.json
|
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3. Agent name matches: `analytics-agent` (hyphenated)
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|
||||
**Debug**:
|
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```bash
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# Test hook manually
|
||||
export CLAUDE_RESPONSE='{"content":"SELECT * FROM users;"}'
|
||||
./.claude/hooks/post-response-metrics.sh
|
||||
```
|
||||
|
||||
### No Metrics in Log
|
||||
|
||||
**Check**:
|
||||
1. Log directory exists: `mkdir -p .claude/logs`
|
||||
2. Write permissions: `touch .claude/logs/test.log`
|
||||
3. Query extraction pattern matches your SQL format
|
||||
|
||||
### Metrics Analysis Fails
|
||||
|
||||
**Check**:
|
||||
1. `jq` is installed: `which jq`
|
||||
2. Log file is valid JSONL: `jq . .claude/logs/analytics-metrics.jsonl`
|
||||
|
||||
---
|
||||
|
||||
## Production Considerations
|
||||
|
||||
### Performance
|
||||
|
||||
- Hook adds ~50ms overhead per response
|
||||
- Log file grows ~200 bytes per query
|
||||
- Rotate logs monthly to prevent bloat
|
||||
|
||||
### Privacy
|
||||
|
||||
- Logs contain actual SQL queries (may include sensitive data)
|
||||
- Add to `.gitignore`: `.claude/logs/`
|
||||
- Consider redacting sensitive values in hook script
|
||||
|
||||
### Database Connection
|
||||
|
||||
- Hook requires database access for `exec_time` measurement
|
||||
- Configure connection in hook script or use environment variables
|
||||
- Ensure read-only credentials for safety
|
||||
|
||||
---
|
||||
|
||||
## Related Resources
|
||||
|
||||
- **[Agent Evaluation Guide](../../../guide/agent-evaluation.md)**: Complete evaluation methodology
|
||||
- **[Hooks Documentation](../../../guide/ultimate-guide.md#5-hooks)**: Hook system reference
|
||||
- **[nao Framework](https://github.com/getnao/nao/)**: Production analytics agent framework (inspiration)
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
Same as parent repository (MIT)
|
||||
|
||||
**Questions?** Open an issue or discussion in the main repository.
|
||||
315
examples/agents/analytics-with-eval/analytics-agent.md
Normal file
315
examples/agents/analytics-with-eval/analytics-agent.md
Normal file
|
|
@ -0,0 +1,315 @@
|
|||
---
|
||||
name: analytics-agent
|
||||
description: SQL query generator with built-in evaluation and safety checks
|
||||
model: sonnet
|
||||
tools: Read, Bash
|
||||
---
|
||||
|
||||
# Analytics Agent
|
||||
|
||||
Generate SQL queries for data analysis with built-in quality metrics and safety validation.
|
||||
|
||||
**Scope**: SQL query generation and data analysis guidance. Does not execute queries directly (delegated to user or automated hooks).
|
||||
|
||||
**Evaluation**: Automatically tracked via `post-response-metrics.sh` hook (see README.md for setup).
|
||||
|
||||
---
|
||||
|
||||
## Evaluation Criteria
|
||||
|
||||
Every query will be evaluated on:
|
||||
|
||||
1. **Correctness**: Does query produce expected results?
|
||||
2. **Performance**: Query execution time < 5s?
|
||||
3. **Safety**: No destructive operations without explicit confirmation?
|
||||
4. **Best practices**: Proper JOINs, indexes, parameterized queries?
|
||||
|
||||
These criteria are enforced through:
|
||||
- Automated safety checks (hook validation)
|
||||
- Performance monitoring (execution time logging)
|
||||
- User feedback collection (implicit via query success/failure)
|
||||
|
||||
---
|
||||
|
||||
## Safety Rules (CRITICAL)
|
||||
|
||||
### ⛔ Never Generate Without Confirmation
|
||||
|
||||
**Destructive operations require explicit user approval BEFORE generation**:
|
||||
- `DELETE` statements
|
||||
- `DROP` operations
|
||||
- `TRUNCATE` commands
|
||||
- `ALTER TABLE` schema changes
|
||||
- `UPDATE` without WHERE clause
|
||||
|
||||
### ✅ Always Include
|
||||
|
||||
1. **WHERE clause** on DELETE/UPDATE (unless explicitly requested otherwise)
|
||||
2. **LIMIT** on exploratory queries to prevent resource exhaustion
|
||||
3. **Parameterized queries** for user input (prevent SQL injection)
|
||||
4. **Comments** explaining complex logic
|
||||
5. **Indexes** referenced in query plan reasoning
|
||||
|
||||
---
|
||||
|
||||
## Query Generation Workflow
|
||||
|
||||
### Step 1: Understand Request
|
||||
|
||||
```markdown
|
||||
**User request**: [summarize in one sentence]
|
||||
**Data source**: [table/view names]
|
||||
**Expected output**: [columns, aggregations]
|
||||
**Filters**: [WHERE conditions]
|
||||
**Safety check**: [destructive? yes/no]
|
||||
```
|
||||
|
||||
### Step 2: Validate Safety
|
||||
|
||||
```bash
|
||||
# If destructive operation detected
|
||||
⚠️ WARNING: This query includes [DELETE/DROP/TRUNCATE/UPDATE without WHERE].
|
||||
|
||||
Confirm you want to proceed? (y/n)
|
||||
```
|
||||
|
||||
**Wait for explicit confirmation before generating**.
|
||||
|
||||
### Step 3: Generate Query
|
||||
|
||||
```sql
|
||||
-- Purpose: [Brief description]
|
||||
-- Expected rows: ~[estimate]
|
||||
-- Execution time estimate: [<1s / 1-5s / >5s]
|
||||
|
||||
SELECT
|
||||
column1,
|
||||
column2,
|
||||
AGG(column3) as metric
|
||||
FROM table_name
|
||||
WHERE condition
|
||||
GROUP BY column1, column2
|
||||
ORDER BY metric DESC
|
||||
LIMIT 100;
|
||||
```
|
||||
|
||||
### Step 4: Provide Context
|
||||
|
||||
```markdown
|
||||
**Query explanation**:
|
||||
- [What it does]
|
||||
- [Why these JOINs/filters]
|
||||
- [Performance considerations]
|
||||
|
||||
**Usage**:
|
||||
\`\`\`bash
|
||||
psql -U user -d database -f query.sql
|
||||
\`\`\`
|
||||
|
||||
**Expected result**: [Description of output]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Query Patterns by Use Case
|
||||
|
||||
### Exploratory Analysis
|
||||
|
||||
```sql
|
||||
-- Quick data exploration (LIMIT for safety)
|
||||
SELECT *
|
||||
FROM table_name
|
||||
LIMIT 10;
|
||||
```
|
||||
|
||||
### Aggregation
|
||||
|
||||
```sql
|
||||
-- Group by with aggregation
|
||||
SELECT
|
||||
category,
|
||||
COUNT(*) as total,
|
||||
AVG(value) as avg_value
|
||||
FROM table_name
|
||||
WHERE date >= '2026-01-01'
|
||||
GROUP BY category
|
||||
ORDER BY total DESC;
|
||||
```
|
||||
|
||||
### Complex JOIN
|
||||
|
||||
```sql
|
||||
-- Multi-table join with filters
|
||||
SELECT
|
||||
u.name,
|
||||
o.order_date,
|
||||
SUM(oi.quantity * oi.price) as total
|
||||
FROM users u
|
||||
INNER JOIN orders o ON u.id = o.user_id
|
||||
INNER JOIN order_items oi ON o.id = oi.order_id
|
||||
WHERE o.status = 'completed'
|
||||
AND o.order_date >= CURRENT_DATE - INTERVAL '30 days'
|
||||
GROUP BY u.name, o.order_date
|
||||
HAVING SUM(oi.quantity * oi.price) > 100
|
||||
ORDER BY total DESC;
|
||||
```
|
||||
|
||||
### Time-Series
|
||||
|
||||
```sql
|
||||
-- Daily aggregation with window function
|
||||
SELECT
|
||||
DATE(created_at) as date,
|
||||
COUNT(*) as daily_count,
|
||||
SUM(COUNT(*)) OVER (ORDER BY DATE(created_at)) as cumulative_count
|
||||
FROM events
|
||||
WHERE created_at >= CURRENT_DATE - INTERVAL '90 days'
|
||||
GROUP BY DATE(created_at)
|
||||
ORDER BY date;
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance Best Practices
|
||||
|
||||
### Index Hints
|
||||
|
||||
Always mention relevant indexes:
|
||||
|
||||
```markdown
|
||||
**Indexes used**:
|
||||
- `users.email` (indexed)
|
||||
- `orders.user_id` (foreign key, indexed)
|
||||
- `orders.created_at` (indexed for time-range queries)
|
||||
|
||||
**Query plan**: EXPLAIN shows index scan on users.email, sequential scan acceptable on orders (small table).
|
||||
```
|
||||
|
||||
### Optimization Tips
|
||||
|
||||
1. **Filter early**: WHERE before JOIN when possible
|
||||
2. **Limit columns**: SELECT only needed columns, not `*`
|
||||
3. **Use EXISTS**: Instead of COUNT(*) > 0 for existence checks
|
||||
4. **Avoid subqueries**: Use JOINs or CTEs for readability
|
||||
5. **Pagination**: Use OFFSET/LIMIT or cursor-based for large results
|
||||
|
||||
---
|
||||
|
||||
## Error Handling Guidance
|
||||
|
||||
### Common Issues
|
||||
|
||||
| Error | Cause | Fix |
|
||||
|-------|-------|-----|
|
||||
| `column does not exist` | Typo or wrong table | Check schema with `\d table_name` |
|
||||
| `syntax error` | Invalid SQL | Validate syntax, check PostgreSQL version |
|
||||
| `timeout` | Query too slow | Add WHERE filters, check indexes |
|
||||
| `permission denied` | Insufficient privileges | Use read-only user or request permission |
|
||||
|
||||
### Debugging Workflow
|
||||
|
||||
```sql
|
||||
-- Step 1: Validate table exists
|
||||
SELECT * FROM information_schema.tables WHERE table_name = 'your_table';
|
||||
|
||||
-- Step 2: Check column names
|
||||
\d your_table
|
||||
|
||||
-- Step 3: Test query with LIMIT
|
||||
SELECT * FROM your_table LIMIT 1;
|
||||
|
||||
-- Step 4: Add filters incrementally
|
||||
SELECT * FROM your_table WHERE condition LIMIT 10;
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Metrics Integration
|
||||
|
||||
This agent integrates with automated evaluation via hooks:
|
||||
|
||||
### What Gets Logged
|
||||
|
||||
```json
|
||||
{
|
||||
"timestamp": "2026-02-10T14:32:00Z",
|
||||
"query": "SELECT * FROM users WHERE active = true;",
|
||||
"exec_time": "0.23s",
|
||||
"safety": "PASS",
|
||||
"row_count": 1523,
|
||||
"error": null
|
||||
}
|
||||
```
|
||||
|
||||
### Monthly Review Process
|
||||
|
||||
1. **Analyze metrics**: Run `eval/metrics.sh`
|
||||
2. **Identify patterns**: Common safety failures, slow queries
|
||||
3. **Update instructions**: Refine based on failure modes
|
||||
4. **Retest**: Validate improvements with unit tests
|
||||
|
||||
See `README.md` for complete evaluation setup.
|
||||
|
||||
---
|
||||
|
||||
## Example Interaction
|
||||
|
||||
**User**: "Show me total revenue by product category for Q1 2026"
|
||||
|
||||
**Agent Response**:
|
||||
|
||||
```markdown
|
||||
**User request**: Revenue by category for Q1 2026
|
||||
**Data source**: orders, order_items, products
|
||||
**Expected output**: category, total_revenue
|
||||
**Safety check**: Read-only (safe)
|
||||
```
|
||||
|
||||
```sql
|
||||
-- Revenue by product category, Q1 2026
|
||||
-- Expected rows: ~20 categories
|
||||
-- Execution time estimate: 1-2s
|
||||
|
||||
SELECT
|
||||
p.category,
|
||||
SUM(oi.quantity * oi.price) as total_revenue,
|
||||
COUNT(DISTINCT o.id) as order_count
|
||||
FROM orders o
|
||||
INNER JOIN order_items oi ON o.id = oi.order_id
|
||||
INNER JOIN products p ON oi.product_id = p.id
|
||||
WHERE o.order_date >= '2026-01-01'
|
||||
AND o.order_date < '2026-04-01'
|
||||
AND o.status = 'completed'
|
||||
GROUP BY p.category
|
||||
ORDER BY total_revenue DESC;
|
||||
```
|
||||
|
||||
**Query explanation**:
|
||||
- Joins orders → order_items → products to link revenue to categories
|
||||
- Filters Q1 2026 (Jan 1 - Mar 31) with date range
|
||||
- Only includes completed orders (status filter)
|
||||
- Groups by category and sums line item totals
|
||||
|
||||
**Indexes used**:
|
||||
- `orders.order_date` (indexed for time-range)
|
||||
- `order_items.order_id`, `order_items.product_id` (foreign keys)
|
||||
- `products.category` (not indexed, acceptable for small table)
|
||||
|
||||
**Usage**:
|
||||
```bash
|
||||
psql -U user -d ecommerce -f revenue_by_category.sql
|
||||
```
|
||||
|
||||
**Expected result**: Table with category names and revenue totals, sorted descending.
|
||||
|
||||
---
|
||||
|
||||
## Related Resources
|
||||
|
||||
- **[Agent Evaluation Guide](../../../guide/agent-evaluation.md)**: Complete methodology
|
||||
- **[SQL Best Practices](https://www.postgresql.org/docs/current/performance-tips.html)**: PostgreSQL optimization
|
||||
- **[nao Framework](https://github.com/getnao/nao/)**: Production analytics agent framework
|
||||
|
||||
---
|
||||
|
||||
**Status**: Template v1.0 | **Compatibility**: PostgreSQL 12+, MySQL 8+, SQLite 3+
|
||||
137
examples/agents/analytics-with-eval/eval/metrics.sh
Normal file
137
examples/agents/analytics-with-eval/eval/metrics.sh
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
#!/bin/bash
|
||||
# Analytics Agent Metrics Analysis
|
||||
# Analyzes collected metrics from .claude/logs/analytics-metrics.jsonl
|
||||
# Produces summary statistics, safety reports, and recommendations
|
||||
#
|
||||
# Usage:
|
||||
# ./metrics.sh # Analyze all metrics
|
||||
# ./metrics.sh --since 2026-02-01 # Filter by date
|
||||
# ./metrics.sh --report # Generate formatted report
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
# Configuration
|
||||
LOG_FILE="${1:-.claude/logs/analytics-metrics.jsonl}"
|
||||
SINCE_DATE="${2:-}"
|
||||
|
||||
# Check dependencies
|
||||
if ! command -v jq > /dev/null 2>&1; then
|
||||
echo "Error: jq is required but not installed." >&2
|
||||
echo "Install: brew install jq (macOS) or apt-get install jq (Linux)" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check log file exists
|
||||
if [ ! -f "$LOG_FILE" ]; then
|
||||
echo "Error: Log file not found: $LOG_FILE" >&2
|
||||
echo "Ensure analytics agent has been used and hook is configured." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Filter by date if specified
|
||||
if [ -n "$SINCE_DATE" ]; then
|
||||
METRICS=$(jq -c "select(.timestamp >= \"$SINCE_DATE\")" "$LOG_FILE")
|
||||
else
|
||||
METRICS=$(cat "$LOG_FILE")
|
||||
fi
|
||||
|
||||
# Count total queries
|
||||
TOTAL=$(echo "$METRICS" | wc -l | xargs)
|
||||
|
||||
if [ "$TOTAL" -eq 0 ]; then
|
||||
echo "No metrics found."
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Calculate date range
|
||||
FIRST_DATE=$(echo "$METRICS" | head -1 | jq -r '.timestamp' | cut -d'T' -f1)
|
||||
LAST_DATE=$(echo "$METRICS" | tail -1 | jq -r '.timestamp' | cut -d'T' -f1)
|
||||
|
||||
# Safety analysis
|
||||
SAFETY_PASS=$(echo "$METRICS" | jq -s 'map(select(.safety == "PASS")) | length')
|
||||
SAFETY_FAIL=$(echo "$METRICS" | jq -s 'map(select(.safety == "FAIL")) | length')
|
||||
SAFETY_PASS_PCT=$((SAFETY_PASS * 100 / TOTAL))
|
||||
SAFETY_FAIL_PCT=$((SAFETY_FAIL * 100 / TOTAL))
|
||||
|
||||
# Execution time analysis (if available)
|
||||
EXEC_TIMES=$(echo "$METRICS" | jq -r 'select(.exec_time != null and .exec_time != "null") | .exec_time' || echo "")
|
||||
if [ -n "$EXEC_TIMES" ]; then
|
||||
HAS_EXEC_TIME=true
|
||||
# Convert to seconds (assuming format like "0.23s" or "2.1s")
|
||||
EXEC_TIMES_SEC=$(echo "$EXEC_TIMES" | sed 's/s$//' | sort -n)
|
||||
EXEC_COUNT=$(echo "$EXEC_TIMES_SEC" | wc -l | xargs)
|
||||
EXEC_MEAN=$(echo "$EXEC_TIMES_SEC" | awk '{sum+=$1} END {printf "%.2f", sum/NR}')
|
||||
EXEC_MEDIAN=$(echo "$EXEC_TIMES_SEC" | awk '{arr[NR]=$1} END {print arr[int(NR/2)]}')
|
||||
EXEC_P95=$(echo "$EXEC_TIMES_SEC" | awk '{arr[NR]=$1} END {print arr[int(NR*0.95)]}')
|
||||
EXEC_P99=$(echo "$EXEC_TIMES_SEC" | awk '{arr[NR]=$1} END {print arr[int(NR*0.99)]}')
|
||||
else
|
||||
HAS_EXEC_TIME=false
|
||||
fi
|
||||
|
||||
# Top failure reasons
|
||||
FAILURE_REASONS=$(echo "$METRICS" | jq -r 'select(.safety == "FAIL") | .safety_reason' | sort | uniq -c | sort -rn | head -5)
|
||||
|
||||
# Print report
|
||||
echo "=== Analytics Agent Metrics Report ==="
|
||||
echo ""
|
||||
echo "Period: $FIRST_DATE to $LAST_DATE"
|
||||
echo "Total queries: $TOTAL"
|
||||
echo ""
|
||||
|
||||
echo "Safety Checks:"
|
||||
echo " PASS: $SAFETY_PASS ($SAFETY_PASS_PCT%)"
|
||||
echo " FAIL: $SAFETY_FAIL ($SAFETY_FAIL_PCT%)"
|
||||
echo ""
|
||||
|
||||
if [ "$HAS_EXEC_TIME" = true ]; then
|
||||
echo "Execution Time (based on $EXEC_COUNT queries with timing data):"
|
||||
echo " Mean: ${EXEC_MEAN}s"
|
||||
echo " Median: ${EXEC_MEDIAN}s"
|
||||
echo " P95: ${EXEC_P95}s"
|
||||
echo " P99: ${EXEC_P99}s"
|
||||
echo ""
|
||||
fi
|
||||
|
||||
if [ "$SAFETY_FAIL" -gt 0 ]; then
|
||||
echo "Common Safety Failures:"
|
||||
echo "$FAILURE_REASONS" | while read -r line; do
|
||||
COUNT=$(echo "$line" | awk '{print $1}')
|
||||
REASON=$(echo "$line" | cut -d' ' -f2-)
|
||||
echo " - $REASON ($COUNT occurrences)"
|
||||
done
|
||||
echo ""
|
||||
fi
|
||||
|
||||
# Recommendations
|
||||
echo "Recommendations:"
|
||||
if [ "$SAFETY_FAIL_PCT" -gt 10 ]; then
|
||||
echo " ⚠️ HIGH: ${SAFETY_FAIL_PCT}% safety failures detected"
|
||||
echo " Action: Review agent instructions to emphasize safety rules"
|
||||
fi
|
||||
|
||||
if [ "$HAS_EXEC_TIME" = true ]; then
|
||||
if (( $(echo "$EXEC_P95 > 5.0" | bc -l) )); then
|
||||
echo " ⚠️ P95 execution time > 5s"
|
||||
echo " Action: Review slow queries, add indexes, or optimize filters"
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ "$SAFETY_FAIL" -eq 0 ]; then
|
||||
echo " ✅ No safety failures - agent following rules correctly"
|
||||
fi
|
||||
|
||||
if [ "$TOTAL" -lt 10 ]; then
|
||||
echo " ℹ️ Low sample size ($TOTAL queries)"
|
||||
echo " Action: Collect more data before drawing conclusions"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "Next Steps:"
|
||||
echo " 1. Review failed queries: jq 'select(.safety == \"FAIL\")' $LOG_FILE"
|
||||
echo " 2. Generate monthly report: cp eval/report-template.md reports/$(date +%Y-%m).md"
|
||||
echo " 3. Update agent instructions based on patterns"
|
||||
echo ""
|
||||
|
||||
# Optional: Export for further analysis
|
||||
# echo "$METRICS" | jq -s '.' > analytics-metrics-export.json
|
||||
# echo "Exported to: analytics-metrics-export.json"
|
||||
257
examples/agents/analytics-with-eval/eval/report-template.md
Normal file
257
examples/agents/analytics-with-eval/eval/report-template.md
Normal file
|
|
@ -0,0 +1,257 @@
|
|||
# Analytics Agent Evaluation Report
|
||||
|
||||
**Month**: [YYYY-MM]
|
||||
**Report Date**: [YYYY-MM-DD]
|
||||
**Evaluator**: [Your Name]
|
||||
**Agent Version**: 1.0
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
[2-3 sentence overview of agent performance this month]
|
||||
|
||||
**Key Metrics**:
|
||||
- Total queries: [X]
|
||||
- Safety pass rate: [Y]%
|
||||
- Avg execution time: [Z]s
|
||||
|
||||
**Status**: 🟢 Healthy / 🟡 Needs Attention / 🔴 Critical
|
||||
|
||||
---
|
||||
|
||||
## Metrics Overview
|
||||
|
||||
### Volume
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Total queries generated | [X] |
|
||||
| Unique users/sessions | [Y] |
|
||||
| Queries per day (avg) | [Z] |
|
||||
| Growth vs last month | [+/-]% |
|
||||
|
||||
### Quality Metrics
|
||||
|
||||
| Metric | Target | Actual | Status |
|
||||
|--------|--------|--------|--------|
|
||||
| Safety pass rate | >95% | [X]% | 🟢/🟡/🔴 |
|
||||
| Query correctness | >90% | [Y]% | 🟢/🟡/🔴 |
|
||||
| User satisfaction | >4.0/5 | [Z]/5 | 🟢/🟡/🔴 |
|
||||
|
||||
### Performance Metrics
|
||||
|
||||
| Metric | Target | Actual | Status |
|
||||
|--------|--------|--------|--------|
|
||||
| Mean execution time | <3s | [X]s | 🟢/🟡/🔴 |
|
||||
| P95 execution time | <5s | [Y]s | 🟢/🟡/🔴 |
|
||||
| P99 execution time | <10s | [Z]s | 🟢/🟡/🔴 |
|
||||
|
||||
---
|
||||
|
||||
## Safety Analysis
|
||||
|
||||
### Safety Check Results
|
||||
|
||||
```
|
||||
Total: [X] queries
|
||||
- PASS: [Y] ([Z]%)
|
||||
- FAIL: [A] ([B]%)
|
||||
```
|
||||
|
||||
### Top Safety Failures
|
||||
|
||||
1. **[Failure Type]** - [X] occurrences
|
||||
- Example: `[SQL query snippet]`
|
||||
- Root cause: [Brief explanation]
|
||||
- Action: [What was done to fix]
|
||||
|
||||
2. **[Failure Type]** - [Y] occurrences
|
||||
- Example: `[SQL query snippet]`
|
||||
- Root cause: [Brief explanation]
|
||||
- Action: [What was done to fix]
|
||||
|
||||
### Trends
|
||||
|
||||
[Graph or description showing safety pass rate over time]
|
||||
|
||||
---
|
||||
|
||||
## Performance Analysis
|
||||
|
||||
### Execution Time Distribution
|
||||
|
||||
```
|
||||
Mean: [X]s
|
||||
Median: [Y]s
|
||||
P95: [Z]s
|
||||
P99: [A]s
|
||||
Max: [B]s
|
||||
```
|
||||
|
||||
### Slowest Queries
|
||||
|
||||
1. **[Query description]** - [X]s
|
||||
```sql
|
||||
[SQL query]
|
||||
```
|
||||
- Reason: [Why slow]
|
||||
- Optimization: [What could improve it]
|
||||
|
||||
2. **[Query description]** - [Y]s
|
||||
```sql
|
||||
[SQL query]
|
||||
```
|
||||
- Reason: [Why slow]
|
||||
- Optimization: [What could improve it]
|
||||
|
||||
---
|
||||
|
||||
## User Feedback
|
||||
|
||||
### Explicit Feedback
|
||||
|
||||
- **Positive**: [X] responses
|
||||
- Common praise: "[Theme 1]", "[Theme 2]"
|
||||
- **Negative**: [Y] responses
|
||||
- Common complaints: "[Theme 1]", "[Theme 2]"
|
||||
|
||||
### Implicit Signals
|
||||
|
||||
- **Query retry rate**: [X]% (users re-running queries)
|
||||
- **Query modification rate**: [Y]% (users editing generated queries)
|
||||
- **Adoption rate**: [Z] queries/user/week
|
||||
|
||||
### Notable Feedback
|
||||
|
||||
> "[User quote 1]"
|
||||
— [User name/role, if available]
|
||||
|
||||
> "[User quote 2]"
|
||||
— [User name/role, if available]
|
||||
|
||||
---
|
||||
|
||||
## Incident Log
|
||||
|
||||
### Critical Issues
|
||||
|
||||
| Date | Issue | Impact | Resolution |
|
||||
|------|-------|--------|------------|
|
||||
| [YYYY-MM-DD] | [Brief description] | [High/Medium/Low] | [What was done] |
|
||||
|
||||
### Near-Misses
|
||||
|
||||
[List of queries that almost caused problems but were caught by safety checks]
|
||||
|
||||
---
|
||||
|
||||
## Improvements Made
|
||||
|
||||
### Agent Instruction Updates
|
||||
|
||||
1. **[Update 1]**
|
||||
- **Reason**: [Why needed]
|
||||
- **Change**: [What was modified in agent instructions]
|
||||
- **Impact**: [Expected improvement]
|
||||
|
||||
2. **[Update 2]**
|
||||
- **Reason**: [Why needed]
|
||||
- **Change**: [What was modified]
|
||||
- **Impact**: [Expected improvement]
|
||||
|
||||
### Hook/Metrics Updates
|
||||
|
||||
- [Any changes to metrics collection or analysis]
|
||||
|
||||
---
|
||||
|
||||
## A/B Test Results (if applicable)
|
||||
|
||||
### Test: [Description]
|
||||
|
||||
**Period**: [Start date] to [End date]
|
||||
|
||||
**Variants**:
|
||||
- **Control (A)**: [Description]
|
||||
- **Experiment (B)**: [Description]
|
||||
|
||||
**Metrics**:
|
||||
|
||||
| Metric | Control (A) | Experiment (B) | Change |
|
||||
|--------|-------------|----------------|--------|
|
||||
| Safety pass rate | [X]% | [Y]% | [+/-]% |
|
||||
| Avg exec time | [X]s | [Y]s | [+/-]s |
|
||||
| User satisfaction | [X]/5 | [Y]/5 | [+/-] |
|
||||
|
||||
**Decision**: ✅ Promote B / ❌ Keep A / ⏸️ Needs more data
|
||||
|
||||
**Rationale**: [Why this decision]
|
||||
|
||||
---
|
||||
|
||||
## Recommendations
|
||||
|
||||
### High Priority
|
||||
|
||||
1. **[Recommendation 1]**
|
||||
- **Current state**: [Problem description]
|
||||
- **Proposed change**: [What to do]
|
||||
- **Expected impact**: [Improvement estimate]
|
||||
- **Effort**: Low/Medium/High
|
||||
|
||||
### Medium Priority
|
||||
|
||||
1. **[Recommendation 2]**
|
||||
- **Current state**: [Problem description]
|
||||
- **Proposed change**: [What to do]
|
||||
- **Expected impact**: [Improvement estimate]
|
||||
- **Effort**: Low/Medium/High
|
||||
|
||||
### Low Priority / Future
|
||||
|
||||
- [Quick list of nice-to-have improvements]
|
||||
|
||||
---
|
||||
|
||||
## Next Month Goals
|
||||
|
||||
1. **[Goal 1]**: [Specific, measurable target]
|
||||
2. **[Goal 2]**: [Specific, measurable target]
|
||||
3. **[Goal 3]**: [Specific, measurable target]
|
||||
|
||||
---
|
||||
|
||||
## Appendix
|
||||
|
||||
### Methodology
|
||||
|
||||
**Data sources**:
|
||||
- `.claude/logs/analytics-metrics.jsonl` (automated metrics)
|
||||
- User feedback forms
|
||||
- Manual query reviews
|
||||
|
||||
**Analysis tools**:
|
||||
- `eval/metrics.sh` for automated reporting
|
||||
- SQL queries for deep-dive analysis
|
||||
- Manual review of safety failures
|
||||
|
||||
**Limitations**:
|
||||
- [Any known gaps in data collection]
|
||||
- [Potential biases in analysis]
|
||||
|
||||
### Raw Data
|
||||
|
||||
**Export**: `analytics-metrics-[YYYY-MM].json`
|
||||
|
||||
**Query**:
|
||||
```bash
|
||||
jq 'select(.timestamp >= "2026-MM-01" and .timestamp < "2026-MM+1-01")' \
|
||||
.claude/logs/analytics-metrics.jsonl > analytics-metrics-2026-MM.json
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**Previous Reports**: [Link to folder with past reports]
|
||||
|
||||
**Questions?** Contact [evaluation team email/slack]
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
#!/bin/bash
|
||||
# Post-response hook for analytics agent metrics collection
|
||||
# Triggered after each agent response to log quality, performance, and safety metrics
|
||||
#
|
||||
# Setup:
|
||||
# 1. Copy to .claude/hooks/post-response-metrics.sh
|
||||
# 2. chmod +x .claude/hooks/post-response-metrics.sh
|
||||
# 3. Add to .claude/settings.json hooks.postToolUse configuration
|
||||
# 4. mkdir -p .claude/logs
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
# Configuration
|
||||
LOG_FILE="${CLAUDE_LOGS_DIR:-.claude/logs}/analytics-metrics.jsonl"
|
||||
AGENT_NAME="analytics-agent"
|
||||
|
||||
# Ensure log directory exists
|
||||
mkdir -p "$(dirname "$LOG_FILE")"
|
||||
|
||||
# Extract agent ID from environment (if available)
|
||||
# Note: This assumes Claude Code exposes agent context via environment variables
|
||||
# Adjust based on actual Claude Code hook environment
|
||||
CURRENT_AGENT="${CLAUDE_AGENT_NAME:-unknown}"
|
||||
|
||||
# Only log if this is the analytics agent
|
||||
if [ "$CURRENT_AGENT" != "$AGENT_NAME" ]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Extract SQL query from response
|
||||
# This is a simplified pattern - adjust based on actual response format
|
||||
QUERY=$(echo "$CLAUDE_RESPONSE" | grep -oP '```sql\s*\K[^`]+' | head -1 || echo "")
|
||||
|
||||
# If no query found, skip logging
|
||||
if [ -z "$QUERY" ]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Safety check: Detect destructive operations
|
||||
if echo "$QUERY" | grep -iE '\b(DELETE|DROP|TRUNCATE|ALTER)\b' > /dev/null; then
|
||||
SAFETY="FAIL"
|
||||
SAFETY_REASON="Contains destructive operation (DELETE/DROP/TRUNCATE/ALTER)"
|
||||
else
|
||||
SAFETY="PASS"
|
||||
SAFETY_REASON=""
|
||||
fi
|
||||
|
||||
# Check for UPDATE without WHERE (dangerous)
|
||||
if echo "$QUERY" | grep -iE '\bUPDATE\b' | grep -ivE '\bWHERE\b' > /dev/null; then
|
||||
SAFETY="FAIL"
|
||||
SAFETY_REASON="UPDATE without WHERE clause"
|
||||
fi
|
||||
|
||||
# Performance measurement (requires database connection)
|
||||
# TODO: Configure database credentials
|
||||
# Uncomment and configure if you want actual execution time measurement
|
||||
#
|
||||
# DB_HOST="${DB_HOST:-localhost}"
|
||||
# DB_USER="${DB_USER:-readonly_user}"
|
||||
# DB_NAME="${DB_NAME:-analytics_db}"
|
||||
#
|
||||
# EXEC_TIME=$( (time psql -h "$DB_HOST" -U "$DB_USER" -d "$DB_NAME" -c "$QUERY" > /dev/null 2>&1) 2>&1 | grep real | awk '{print $2}')
|
||||
#
|
||||
# For now, set as null (requires database setup)
|
||||
EXEC_TIME="null"
|
||||
ROW_COUNT="null"
|
||||
ERROR="null"
|
||||
|
||||
# Build JSON log entry
|
||||
# Using jq for proper JSON escaping (fallback to basic escaping if jq not available)
|
||||
if command -v jq > /dev/null 2>&1; then
|
||||
LOG_ENTRY=$(jq -n \
|
||||
--arg timestamp "$(date -Iseconds)" \
|
||||
--arg query "$QUERY" \
|
||||
--arg exec_time "$EXEC_TIME" \
|
||||
--arg safety "$SAFETY" \
|
||||
--arg safety_reason "$SAFETY_REASON" \
|
||||
--arg row_count "$ROW_COUNT" \
|
||||
--arg error "$ERROR" \
|
||||
'{
|
||||
timestamp: $timestamp,
|
||||
query: $query,
|
||||
exec_time: $exec_time,
|
||||
safety: $safety,
|
||||
safety_reason: $safety_reason,
|
||||
row_count: $row_count,
|
||||
error: $error
|
||||
}')
|
||||
else
|
||||
# Fallback: Basic JSON (may need escaping for special characters)
|
||||
LOG_ENTRY="{\"timestamp\":\"$(date -Iseconds)\",\"query\":\"${QUERY//\"/\\\"}\",\"exec_time\":$EXEC_TIME,\"safety\":\"$SAFETY\",\"safety_reason\":\"$SAFETY_REASON\",\"row_count\":$ROW_COUNT,\"error\":$ERROR}"
|
||||
fi
|
||||
|
||||
# Append to log file
|
||||
echo "$LOG_ENTRY" >> "$LOG_FILE"
|
||||
|
||||
# Optional: Alert on safety failures
|
||||
if [ "$SAFETY" = "FAIL" ]; then
|
||||
echo "⚠️ SAFETY CHECK FAILED: $SAFETY_REASON" >&2
|
||||
echo "Query: $QUERY" >&2
|
||||
fi
|
||||
|
||||
exit 0
|
||||
|
|
@ -16,6 +16,7 @@ Core documentation for mastering Claude Code.
|
|||
| [architecture.md](./architecture.md) | How Claude Code works internally (master loop, tools, context) | 25 min |
|
||||
| [learning-with-ai.md](./learning-with-ai.md) | Guide for juniors on using AI without losing skills | 15 min |
|
||||
| [adoption-approaches.md](./adoption-approaches.md) | Implementation strategies for teams | 15 min |
|
||||
| [agent-evaluation.md](./agent-evaluation.md) | **Agent quality metrics**: Measuring custom agent effectiveness with hooks, tests, and feedback loops | 20 min |
|
||||
| [data-privacy.md](./data-privacy.md) | Data retention and privacy guide | 10 min |
|
||||
| [observability.md](./observability.md) | Session monitoring and cost tracking | 15 min |
|
||||
| [methodologies.md](./methodologies.md) | 15 development methodologies reference (TDD, SDD, BDD, etc.) | 20 min |
|
||||
|
|
|
|||
436
guide/agent-evaluation.md
Normal file
436
guide/agent-evaluation.md
Normal file
|
|
@ -0,0 +1,436 @@
|
|||
# Agent Evaluation
|
||||
|
||||
**Quick nav**: [Why Evaluate?](#why-evaluate-agents) · [Metrics to Track](#metrics-to-track) · [Implementation](#implementation-patterns) · [Example](#example-agent-with-evaluation) · [Tools](#tools--references)
|
||||
|
||||
---
|
||||
|
||||
## Why Evaluate Agents?
|
||||
|
||||
When you create custom agents in `.claude/agents/`, you're encoding specialized expertise into reusable workflows. But how do you know if your agents are actually effective?
|
||||
|
||||
**Without evaluation**, you're building blind:
|
||||
- ❌ No way to measure if agent responses are improving or degrading over time
|
||||
- ❌ Can't compare different agent configurations objectively
|
||||
- ❌ Difficult to identify which aspects of agent context/instructions need refinement
|
||||
- ❌ No data to justify investment in agent development
|
||||
|
||||
**With evaluation**, you iterate with confidence:
|
||||
- ✅ Quantify agent quality through metrics (response time, accuracy, tool usage)
|
||||
- ✅ A/B test different agent configurations with measurable outcomes
|
||||
- ✅ Identify patterns in successful vs failed interactions
|
||||
- ✅ Build feedback loops for continuous improvement
|
||||
|
||||
**Core principle**: Agents are code. Like all code, they need tests, metrics, and observability.
|
||||
|
||||
---
|
||||
|
||||
## Metrics to Track
|
||||
|
||||
### 1. Response Quality Metrics
|
||||
|
||||
**What to measure**:
|
||||
- **Task completion rate**: Did the agent accomplish the stated goal?
|
||||
- **Correctness**: Were the agent's outputs factually accurate?
|
||||
- **Relevance**: Did the response stay on-topic and address the actual question?
|
||||
- **Hallucination rate**: How often did the agent invent information?
|
||||
|
||||
**How to track**:
|
||||
```bash
|
||||
# Post-response hook: .claude/hooks/log-response-quality.sh
|
||||
# Triggered after each agent response
|
||||
|
||||
# Log structure:
|
||||
{
|
||||
"timestamp": "2026-02-10T14:32:00Z",
|
||||
"agent_id": "backend-architect",
|
||||
"task_completed": true,
|
||||
"correctness_score": 4.5, # User rating 1-5
|
||||
"hallucinations": 0,
|
||||
"response_tokens": 1250
|
||||
}
|
||||
```
|
||||
|
||||
**Implementation tip**: Use user feedback prompts (thumbs up/down) or automated checks (test suite passing after agent code generation).
|
||||
|
||||
---
|
||||
|
||||
### 2. Tool Usage Metrics
|
||||
|
||||
**What to measure**:
|
||||
- **Tool call success rate**: Percentage of tool calls that executed without errors
|
||||
- **Tool selection accuracy**: Did agent choose the right tool for the task?
|
||||
- **Tool call efficiency**: Minimum calls to achieve goal (avoid unnecessary reads/searches)
|
||||
- **Error recovery**: Did agent handle tool failures gracefully?
|
||||
|
||||
**How to track**:
|
||||
```bash
|
||||
# Post-tool-use hook: .claude/hooks/log-tool-usage.sh
|
||||
# Triggered after each tool call
|
||||
|
||||
# Log structure:
|
||||
{
|
||||
"timestamp": "2026-02-10T14:32:05Z",
|
||||
"agent_id": "backend-architect",
|
||||
"tool_name": "Read",
|
||||
"tool_success": true,
|
||||
"tool_parameters": {"file_path": "src/auth.ts"},
|
||||
"execution_time_ms": 45
|
||||
}
|
||||
```
|
||||
|
||||
**Implementation tip**: Use Claude Code hooks system (see `examples/hooks/`) to automatically log tool calls.
|
||||
|
||||
---
|
||||
|
||||
### 3. Performance Metrics
|
||||
|
||||
**What to measure**:
|
||||
- **Response time**: Total time from user prompt to complete response
|
||||
- **Token efficiency**: Input/output tokens used per task
|
||||
- **Context utilization**: How much of context window was used?
|
||||
- **Cost per task**: API cost for the full interaction
|
||||
|
||||
**How to track**:
|
||||
```bash
|
||||
# Session-end hook: .claude/hooks/log-performance.sh
|
||||
# Triggered at end of session
|
||||
|
||||
# Log structure:
|
||||
{
|
||||
"timestamp": "2026-02-10T14:35:00Z",
|
||||
"agent_id": "backend-architect",
|
||||
"session_duration_s": 180,
|
||||
"input_tokens": 3500,
|
||||
"output_tokens": 2800,
|
||||
"total_cost_usd": 0.15,
|
||||
"context_utilization": 0.42
|
||||
}
|
||||
```
|
||||
|
||||
**Implementation tip**: Parse Claude Code session logs or use MCP observability tools.
|
||||
|
||||
---
|
||||
|
||||
### 4. User Satisfaction Metrics
|
||||
|
||||
**What to measure**:
|
||||
- **Explicit feedback**: User ratings, comments, bug reports
|
||||
- **Implicit signals**: Did user accept agent's suggestions? Did they retry the prompt?
|
||||
- **Adoption rate**: How often is this agent used vs alternatives?
|
||||
- **Retention**: Do users return to this agent for similar tasks?
|
||||
|
||||
**How to track**:
|
||||
```bash
|
||||
# Manual feedback collection
|
||||
# After agent completes task, prompt user:
|
||||
"Rate this agent's performance (1-5): _"
|
||||
|
||||
# Log:
|
||||
{
|
||||
"timestamp": "2026-02-10T14:35:10Z",
|
||||
"agent_id": "backend-architect",
|
||||
"user_rating": 5,
|
||||
"user_comment": "Perfect analysis of auth flow",
|
||||
"would_use_again": true
|
||||
}
|
||||
```
|
||||
|
||||
**Implementation tip**: Add feedback prompts to agent templates or use post-session surveys.
|
||||
|
||||
---
|
||||
|
||||
## Implementation Patterns
|
||||
|
||||
### Pattern 1: Logging Hook System
|
||||
|
||||
**Use Case**: Automatically track all agent interactions without manual intervention
|
||||
|
||||
**Setup**:
|
||||
```bash
|
||||
# .claude/hooks/post-tool-use.sh
|
||||
#!/bin/bash
|
||||
# Triggered after every tool call
|
||||
|
||||
AGENT_ID=$(echo "$CLAUDE_AGENT_ID" | jq -r)
|
||||
TOOL_NAME=$(echo "$CLAUDE_TOOL_NAME" | jq -r)
|
||||
TOOL_SUCCESS=$(echo "$CLAUDE_TOOL_SUCCESS" | jq -r)
|
||||
|
||||
# Append to metrics log
|
||||
echo "{\"timestamp\":\"$(date -Iseconds)\",\"agent\":\"$AGENT_ID\",\"tool\":\"$TOOL_NAME\",\"success\":$TOOL_SUCCESS}" \
|
||||
>> .claude/logs/agent-metrics.jsonl
|
||||
```
|
||||
|
||||
**Pros**: Zero manual overhead, complete coverage, time-series data
|
||||
**Cons**: Requires parsing Claude Code environment variables (may change across versions)
|
||||
|
||||
---
|
||||
|
||||
### Pattern 2: Agent Unit Tests
|
||||
|
||||
**Use Case**: Regression testing to ensure agent improvements don't break existing capabilities
|
||||
|
||||
**Setup**:
|
||||
```bash
|
||||
# tests/agents/backend-architect.test.sh
|
||||
#!/bin/bash
|
||||
|
||||
# Test 1: Agent correctly identifies hexagonal architecture layers
|
||||
echo "Test: Hexagonal architecture analysis"
|
||||
RESULT=$(claude agent backend-architect "Analyze src/auth.ts for layer violations")
|
||||
if echo "$RESULT" | grep -q "domain layer"; then
|
||||
echo "✅ PASS: Identified layers"
|
||||
else
|
||||
echo "❌ FAIL: Did not identify layers"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Test 2: Agent recommends correct patterns
|
||||
echo "Test: Pattern recommendations"
|
||||
RESULT=$(claude agent backend-architect "Improve error handling in src/api.ts")
|
||||
if echo "$RESULT" | grep -q "Result<T, E>"; then
|
||||
echo "✅ PASS: Recommended Result pattern"
|
||||
else
|
||||
echo "❌ FAIL: Incorrect pattern"
|
||||
exit 1
|
||||
fi
|
||||
```
|
||||
|
||||
**Pros**: Automated, catches regressions, CI/CD integration
|
||||
**Cons**: Requires maintenance, may have false positives/negatives
|
||||
|
||||
---
|
||||
|
||||
### Pattern 3: A/B Testing Configurations
|
||||
|
||||
**Use Case**: Compare two versions of agent to determine which performs better
|
||||
|
||||
**Setup**:
|
||||
```yaml
|
||||
# .claude/agents/backend-architect-v1.md (control)
|
||||
name: backend-architect
|
||||
version: 1.0
|
||||
instructions: |
|
||||
You are a backend architect specializing in...
|
||||
[original instructions]
|
||||
|
||||
# .claude/agents/backend-architect-v2.md (experiment)
|
||||
name: backend-architect-v2
|
||||
version: 2.0
|
||||
instructions: |
|
||||
You are a backend architect specializing in...
|
||||
[modified instructions with new pattern emphasis]
|
||||
```
|
||||
|
||||
**Evaluation**:
|
||||
```bash
|
||||
# Run same task with both agents, compare metrics
|
||||
# Task: "Analyze src/auth.ts for security issues"
|
||||
|
||||
# Version 1 metrics:
|
||||
# - Response time: 45s
|
||||
# - Issues found: 3
|
||||
# - User rating: 4/5
|
||||
|
||||
# Version 2 metrics:
|
||||
# - Response time: 38s
|
||||
# - Issues found: 5 (2 additional critical issues)
|
||||
# - User rating: 5/5
|
||||
|
||||
# Conclusion: Version 2 is more thorough and faster → promote to production
|
||||
```
|
||||
|
||||
**Pros**: Data-driven decisions, quantifiable improvements
|
||||
**Cons**: Requires discipline to run controlled experiments
|
||||
|
||||
---
|
||||
|
||||
### Pattern 4: Feedback Loop Integration
|
||||
|
||||
**Use Case**: Continuously improve agent based on real-world usage data
|
||||
|
||||
**Setup**:
|
||||
```bash
|
||||
# After agent completes task
|
||||
echo "How would you rate this response? (1-5, or 'skip'): "
|
||||
read RATING
|
||||
|
||||
if [ "$RATING" != "skip" ]; then
|
||||
echo "Any specific feedback?: "
|
||||
read COMMENT
|
||||
|
||||
# Log feedback
|
||||
echo "{\"timestamp\":\"$(date -Iseconds)\",\"agent\":\"$AGENT_ID\",\"rating\":$RATING,\"comment\":\"$COMMENT\"}" \
|
||||
>> .claude/logs/agent-feedback.jsonl
|
||||
fi
|
||||
|
||||
# Weekly: Review feedback.jsonl, identify patterns
|
||||
# Monthly: Update agent instructions based on aggregated feedback
|
||||
```
|
||||
|
||||
**Pros**: Aligns agent with actual user needs, identifies edge cases
|
||||
**Cons**: Requires manual review and action on feedback
|
||||
|
||||
---
|
||||
|
||||
## Example: Agent with Evaluation
|
||||
|
||||
**Full template available**: [`examples/agents/analytics-with-eval/`](../../examples/agents/analytics-with-eval/) includes complete agent definition, hooks, analysis scripts, and report template.
|
||||
|
||||
### Setup: Analytics Agent with Built-in Metrics
|
||||
|
||||
```yaml
|
||||
# .claude/agents/analytics-agent.md
|
||||
---
|
||||
name: analytics-agent
|
||||
description: SQL query generator with evaluation hooks
|
||||
version: 1.0
|
||||
tools:
|
||||
- Read
|
||||
- Write
|
||||
- Bash
|
||||
hooks:
|
||||
post_response: .claude/hooks/log-analytics-metrics.sh
|
||||
---
|
||||
|
||||
# Analytics Agent
|
||||
|
||||
You are an expert SQL analyst helping users query databases.
|
||||
|
||||
## Evaluation Criteria
|
||||
|
||||
After each query:
|
||||
1. **Correctness**: Does query produce expected results?
|
||||
2. **Performance**: Query execution time < 5s?
|
||||
3. **Safety**: No destructive operations (DELETE, DROP, TRUNCATE)?
|
||||
4. **Best practices**: Uses proper JOINs, indexes, parameterized queries?
|
||||
|
||||
## Instructions
|
||||
|
||||
[... agent instructions ...]
|
||||
```
|
||||
|
||||
### Metrics Hook
|
||||
|
||||
```bash
|
||||
# .claude/hooks/log-analytics-metrics.sh
|
||||
#!/bin/bash
|
||||
# Triggered after analytics-agent response
|
||||
|
||||
# Extract query from response (naive grep, improve with jq)
|
||||
QUERY=$(echo "$CLAUDE_RESPONSE" | grep -oP 'SELECT.*?;')
|
||||
|
||||
if [ -n "$QUERY" ]; then
|
||||
# Test query (requires database connection)
|
||||
EXEC_TIME=$( (time psql -U user -d db -c "$QUERY") 2>&1 | grep real | awk '{print $2}')
|
||||
|
||||
# Check for destructive operations
|
||||
if echo "$QUERY" | grep -iE 'DELETE|DROP|TRUNCATE'; then
|
||||
SAFETY="FAIL"
|
||||
else
|
||||
SAFETY="PASS"
|
||||
fi
|
||||
|
||||
# Log metrics
|
||||
echo "{\"timestamp\":\"$(date -Iseconds)\",\"query\":\"$QUERY\",\"exec_time\":\"$EXEC_TIME\",\"safety\":\"$SAFETY\"}" \
|
||||
>> .claude/logs/analytics-metrics.jsonl
|
||||
fi
|
||||
```
|
||||
|
||||
### Analysis
|
||||
|
||||
```bash
|
||||
# Monthly review: Analyze metrics
|
||||
jq -s 'group_by(.safety) | map({safety: .[0].safety, count: length})' \
|
||||
.claude/logs/analytics-metrics.jsonl
|
||||
|
||||
# Output:
|
||||
# [
|
||||
# {"safety": "PASS", "count": 127},
|
||||
# {"safety": "FAIL", "count": 3}
|
||||
# ]
|
||||
|
||||
# Action: Review 3 failed queries, update agent instructions to prevent future violations
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Tools & References
|
||||
|
||||
### Open-Source Evaluation Frameworks
|
||||
|
||||
#### nao (Analytics Agents)
|
||||
|
||||
**URL**: [github.com/getnao/nao](https://github.com/getnao/nao/)
|
||||
|
||||
**What it provides**:
|
||||
- Built-in evaluation framework for analytics agents
|
||||
- Unit testing capabilities for agent responses
|
||||
- Metrics collection (response quality, tool usage, performance)
|
||||
- Feedback loop integration
|
||||
|
||||
**How to adapt for Claude Code**:
|
||||
- **Context builder pattern**: Apply nao's structured context approach to `.claude/agents/` config
|
||||
- **Evaluation hooks**: Translate nao's evaluation framework to Claude Code hooks system
|
||||
- **Metrics schema**: Use nao's metrics schema as template for your logs
|
||||
|
||||
**Status**: Production-ready, actively maintained, TypeScript + Python
|
||||
|
||||
---
|
||||
|
||||
### Claude Code Native Patterns
|
||||
|
||||
**Hooks system**: `.claude/hooks/` for automated logging (see `examples/hooks/README.md`)
|
||||
|
||||
**Agents directory**: `.claude/agents/` for custom agent definitions (see `guide/ultimate-guide.md` Section 4)
|
||||
|
||||
**MCP observability**: Use MCP servers for advanced logging and metrics aggregation
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Start Simple
|
||||
|
||||
**Week 1**: Add basic logging hook (tool calls only)
|
||||
**Week 2**: Add user feedback prompt (manual ratings)
|
||||
**Week 3**: Build dashboard to visualize metrics
|
||||
**Week 4**: Run first A/B test on agent configuration
|
||||
|
||||
### Focus on Actionable Metrics
|
||||
|
||||
Don't track metrics you won't act on. Prioritize:
|
||||
1. **Task completion rate** → Refine agent instructions
|
||||
2. **Tool call errors** → Improve context or add examples
|
||||
3. **User ratings** → Identify confusing or unhelpful responses
|
||||
|
||||
### Automate Where Possible
|
||||
|
||||
Manual evaluation doesn't scale. Use:
|
||||
- Hooks for automatic logging
|
||||
- CI/CD integration for agent unit tests
|
||||
- Scripts for periodic metric aggregation
|
||||
|
||||
### Build Feedback Loops
|
||||
|
||||
Metrics are useless without action:
|
||||
- Weekly: Review metrics, identify patterns
|
||||
- Monthly: Update agent instructions based on data
|
||||
- Quarterly: Major agent refactoring if needed
|
||||
|
||||
---
|
||||
|
||||
## Related Sections
|
||||
|
||||
- **[Agents](./ultimate-guide.md#4-agents)**: Creating custom agents
|
||||
- **[Hooks](./ultimate-guide.md#5-hooks)**: Automation with event hooks
|
||||
- **[Observability](./observability.md)**: Logging and monitoring strategies
|
||||
- **[AI Ecosystem](./ai-ecosystem.md#82-domain-specific-agent-frameworks)**: External frameworks like nao
|
||||
|
||||
---
|
||||
|
||||
**Next steps**:
|
||||
1. Add logging hook to your most-used agent
|
||||
2. Collect 1 week of metrics
|
||||
3. Analyze and refine agent based on data
|
||||
|
||||
**Template**: See `examples/agents/analytics-with-eval/` for complete implementation with hooks, scripts, and report template
|
||||
|
|
@ -1609,6 +1609,32 @@ If you're not using Gas Town/multiclaude, you can still:
|
|||
- Experimentation tolerance is high (work may be lost/redone)
|
||||
- Team has SRE capacity to monitor/intervene
|
||||
|
||||
### 8.2 Domain-Specific Agent Frameworks
|
||||
|
||||
Beyond general-purpose coding assistants, specialized frameworks target specific use cases with built-in context, evaluation, and deployment patterns.
|
||||
|
||||
#### nao (Analytics Agents)
|
||||
|
||||
**URL**: [github.com/getnao/nao](https://github.com/getnao/nao/) | **Stack**: TypeScript 58.9%, Python 38.5%
|
||||
|
||||
**What it is**: Open-source framework for building and deploying analytics agents. Two-step architecture: build agent context via CLI (databases, docs, metadata) → deploy chat UI for natural language data queries.
|
||||
|
||||
**Key features**:
|
||||
- Database agnostic (PostgreSQL, BigQuery, Snowflake, Databricks)
|
||||
- Built-in evaluation framework with unit testing
|
||||
- Native data visualization in chat interface
|
||||
- Self-hosted deployment with Docker
|
||||
- Stack: Fastify, Drizzle ORM, tRPC, React, shadcn UI
|
||||
|
||||
**Relevance to Claude Code**: While nao deploys agents as standalone services (not Claude Code plugins), its patterns are transposable:
|
||||
- **Context builder architecture**: Structuring complex agent context (similar to `.claude/agents/` best practices)
|
||||
- **Evaluation framework**: Measuring agent quality through metrics, unit tests, and feedback loops (gap in current Claude Code workflows)
|
||||
- **Database integrations**: Patterns for injecting database context into agent prompts
|
||||
|
||||
**When to use**: Data teams building conversational analytics interfaces for business users. For Claude Code users, nao serves as reference architecture for agent evaluation and database context patterns.
|
||||
|
||||
**Status**: Active open-source project, production-ready, well-documented
|
||||
|
||||
---
|
||||
|
||||
## 9. Cost & Subscription Strategy
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
**Written with**: Claude (Anthropic)
|
||||
|
||||
**Version**: 3.23.5 | **Last Updated**: February 2026
|
||||
**Version**: 3.24.0 | **Last Updated**: February 2026
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -520,4 +520,4 @@ where.exe claude; claude doctor; claude mcp list
|
|||
|
||||
**Author**: Florian BRUNIAUX | [@Méthode Aristote](https://methode-aristote.fr) | Written with Claude
|
||||
|
||||
*Last updated: February 2026 | Version 3.23.5*
|
||||
*Last updated: February 2026 | Version 3.24.0*
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@
|
|||
|
||||
**Last updated**: January 2026
|
||||
|
||||
**Version**: 3.23.5
|
||||
**Version**: 3.24.0
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -4241,7 +4241,7 @@ The `.claude/` folder is your project's Claude Code directory for memory, settin
|
|||
| Personal preferences | `CLAUDE.md` | ❌ Gitignore |
|
||||
| Personal permissions | `settings.local.json` | ❌ Gitignore |
|
||||
|
||||
### 3.23.5 Version Control & Backup
|
||||
### 3.24.0 Version Control & Backup
|
||||
|
||||
**Problem**: Without version control, losing your Claude Code configuration means hours of manual reconfiguration across agents, skills, hooks, and MCP servers.
|
||||
|
||||
|
|
@ -19293,4 +19293,4 @@ We'll evaluate and add it to this section if it meets quality criteria.
|
|||
|
||||
**Contributions**: Issues and PRs welcome.
|
||||
|
||||
**Last updated**: January 2026 | **Version**: 3.23.5
|
||||
**Last updated**: January 2026 | **Version**: 3.24.0
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
# Source: guide/ultimate-guide.md
|
||||
# Purpose: Condensed index for LLMs to quickly answer user questions about Claude Code
|
||||
|
||||
version: "3.23.5"
|
||||
version: "3.24.0"
|
||||
updated: "2026-02-09"
|
||||
|
||||
# ════════════════════════════════════════════════════════════════
|
||||
|
|
@ -173,7 +173,7 @@ deep_dive:
|
|||
third_party_toad: "https://github.com/batrachianai/toad"
|
||||
third_party_conductor: "https://docs.conductor.build"
|
||||
# Configuration Management & Backup (Added 2026-02-02)
|
||||
config_management_guide: "guide/ultimate-guide.md:4085" # Section 3.23.5
|
||||
config_management_guide: "guide/ultimate-guide.md:4085" # Section 3.24.0
|
||||
config_hierarchy: "guide/ultimate-guide.md:4095" # Global → Project → Local precedence
|
||||
config_git_strategy_project: "guide/ultimate-guide.md:4110" # What to commit in .claude/
|
||||
config_git_strategy_global: "guide/ultimate-guide.md:4133" # Version control ~/.claude/
|
||||
|
|
@ -1169,7 +1169,7 @@ ecosystem:
|
|||
- "Cross-links modified → Update all 4 repos"
|
||||
history:
|
||||
- date: "2026-01-20"
|
||||
event: "Code Landing sync v3.23.5, 66 templates, cross-links"
|
||||
event: "Code Landing sync v3.24.0, 66 templates, cross-links"
|
||||
commit: "5b5ce62"
|
||||
- date: "2026-01-20"
|
||||
event: "Cowork Landing fix (paths, README, UI badges)"
|
||||
|
|
@ -1181,7 +1181,7 @@ ecosystem:
|
|||
onboarding_matrix_meta:
|
||||
version: "2.0.0"
|
||||
last_updated: "2026-02-05"
|
||||
aligned_with_guide: "3.23.5"
|
||||
aligned_with_guide: "3.24.0"
|
||||
changelog:
|
||||
- version: "2.0.0"
|
||||
date: "2026-02-05"
|
||||
|
|
@ -1209,7 +1209,7 @@ onboarding_matrix:
|
|||
core: [rules, sandbox_native_guide, commands]
|
||||
time_budget: "5 min"
|
||||
topics_max: 3
|
||||
note: "SECURITY FIRST - sandbox before commands (v3.23.5 critical fix)"
|
||||
note: "SECURITY FIRST - sandbox before commands (v3.24.0 critical fix)"
|
||||
|
||||
beginner_15min:
|
||||
core: [rules, sandbox_native_guide, workflow, essential_commands]
|
||||
|
|
@ -1294,7 +1294,7 @@ onboarding_matrix:
|
|||
- default: agent_validation_checklist
|
||||
time_budget: "60 min"
|
||||
topics_max: 6
|
||||
note: "Dual-instance pattern for quality workflows (v3.23.5)"
|
||||
note: "Dual-instance pattern for quality workflows (v3.24.0)"
|
||||
|
||||
learn_security:
|
||||
intermediate_30min:
|
||||
|
|
@ -1305,7 +1305,7 @@ onboarding_matrix:
|
|||
- default: permission_modes
|
||||
time_budget: "30 min"
|
||||
topics_max: 4
|
||||
note: "NEW goal (v3.23.5) - Security-focused learning path"
|
||||
note: "NEW goal (v3.24.0) - Security-focused learning path"
|
||||
|
||||
power_60min:
|
||||
core: [sandbox_native_guide, mcp_secrets_management, security_hardening]
|
||||
|
|
@ -1330,7 +1330,7 @@ onboarding_matrix:
|
|||
core: [rules, sandbox_native_guide, workflow, essential_commands, context_management, plan_mode]
|
||||
time_budget: "60 min"
|
||||
topics_max: 6
|
||||
note: "Security foundation + core workflow (v3.23.5 sandbox added)"
|
||||
note: "Security foundation + core workflow (v3.24.0 sandbox added)"
|
||||
|
||||
intermediate_120min:
|
||||
core: [plan_mode, agents, skills, config_hierarchy, git_mcp_guide, hooks, mcp_servers]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue