docs: create comprehensive AGENTS.md and improve AI agent guidance

[helmut-hoffer-von-ankershoffen]

Summary

Add comprehensive AGENTS.md as central navigation hub for AI coding agents, providing task-oriented guidance, common workflows, and troubleshooting.

Changes

• ✨ Create AGENTS.md (1,050+ lines) with:

  • Quick start guide and essential files checklist
  • Complete documentation map with navigation patterns
  • Architecture quick reference
  • 5 common agent tasks with workflows
  • 7 common pitfalls with solutions
  • Testing strategies for agents
  • Build system command reference
  • Module capabilities matrix
  • 3-level learning path
  • Comprehensive troubleshooting guide
  • 3 detailed workflows
  • Pre-commit checklist

• 📝 Update root CLAUDE.md:

  • Add prominent callout to AGENTS.md
  • Add "Quick Start for AI Agents" section
  • Include links to common tasks

• 📝 Update src/aignostics/CLAUDE.md with tip

• 📝 Update tests/CLAUDE.md with testing reference

Benefits

• Central navigation reducing agent confusion
• Task-oriented guidance vs. pure reference
• Clear prioritization of what to read first
• Concrete examples from actual codebase
• Cross-references between all docs

Closes #153

Generated with Claude Code

[claude]

🤖 AI Agent PR Review - #157

Summary

This PR introduces AGENTS.md as a comprehensive navigation hub for AI coding agents working with this SDK. It is an excellent addition that transforms the documentation from reference-oriented to task-oriented, making it significantly more useful for AI agents.

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✅ Strengths

1. Outstanding Organization & Structure (⭐⭐⭐⭐⭐)

• Clear hierarchy: Quick Start → Architecture → Tasks → Workflows → Troubleshooting
• Progressive disclosure: Essential info first, details available when needed
• Excellent use of visual diagrams (ASCII art for dependency graphs, module patterns)
• Cross-references create a cohesive documentation web

2. Task-Oriented Approach (⭐⭐⭐⭐⭐)

• 5 common agent tasks with concrete steps (lines 110-204)
• 3 detailed workflows with bash commands (lines 583-651)
• Practical examples from actual codebase patterns
• Finding the Right File scenarios (lines 306-339) are particularly helpful

3. Comprehensive Coverage

• 7 common pitfalls with solutions (lines 245-302) - addresses real pain points
• Testing strategies specific to agents (lines 340-397)
• Troubleshooting guide with actionable fixes (lines 497-579)
• Security considerations (lines 438-452)
• Pre-commit checklist (lines 653-667)

4. Integration with Existing Docs

• Seamlessly integrates with CLAUDE.md, CODE_STYLE.md, and module docs
• Updates to CLAUDE.md, src/aignostics/CLAUDE.md, and tests/CLAUDE.md create bidirectional navigation
• Maintains consistent terminology and patterns

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🔍 Areas for Improvement

1. Minor Inconsistencies

Line 228 vs. Documentation Matrix (line 458-469)

• Line 228 shows 95% for critical modules (platform, application, utils)
• However, the Module Capabilities Matrix does not indicate which modules are critical
• Suggestion: Either add a note in the matrix about coverage targets or reference back to CODE_STYLE.md

2. Anchors in Internal Links

Several links reference anchors that may not exist or are formatted inconsistently. Verify these work correctly:

• Line 140: CLAUDE.md#adding-new-modules
• Line 300: CLAUDE.md#core-modules--dependencies (double dash)
• Line 321: CLAUDE.md#cli-usage-examples
• Line 331: CLAUDE.md#dependency-graph

3. Test Example Variable

Lines 199-203: Test example uses undefined variable invalid_input

Suggestion: Make it more explicit with invalid_data string or add comment

4. Python Version Check

Line 511: States Should be 3.11, 3.12, or 3.13

• Verify this matches pyproject.toml requires-python field

5. Coverage Report Path

Line 557: References reports/coverage_html/index.html

• Default pytest-cov generates to htmlcov/index.html
• Verify the actual output directory or add note about path variation

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🧪 Test Coverage

Good news: This PR only adds documentation (no code changes), so test coverage is not applicable.

✅ Documentation follows CODE_STYLE.md guidelines
✅ Consistent with existing CLAUDE.md structure
✅ Examples reference actual codebase patterns

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🔐 Security Considerations

✅ No security concerns

• Properly emphasizes security in lines 438-452
• Warns against committing secrets
• References SECURITY.md appropriately
• No secrets or sensitive data in documentation

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🎯 Code Quality & Best Practices

Documentation Quality: A+

• ✅ Clear, concise, scannable
• ✅ Appropriate use of headings, lists, tables
• ✅ Code examples properly formatted
• ✅ Visual diagrams enhance understanding
• ✅ Consistent emoji usage for visual navigation

Adherence to Repository Standards: Excellent

• ✅ Follows CODE_STYLE.md documentation principles
• ✅ Uses conventional commit format
• ✅ Comprehensive PR description
• ✅ Properly closes issue PR AI Guidance #153

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📊 Impact Assessment

Positive Impact: Very High

1. Significantly reduces onboarding time for AI agents
2. Prevents common mistakes through pitfalls section
3. Provides clear navigation paths through complex codebase
4. Establishes patterns for future documentation improvements

Risk: Very Low

• Documentation-only changes
• No breaking changes
• No code modifications

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🎨 Suggestions for Future Enhancement (Post-Merge)

1. Add Examples Section: Include 2-3 complete before/after examples of agent interactions
2. Decision Trees: Add flowcharts for Which module should I modify decisions
3. Common Error Messages: Add section mapping error messages to solutions
4. Version-Specific Guidance: If SDK behavior differs across Python versions
5. Integration Test Examples: Expand test patterns with more examples

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📋 Pre-Merge Checklist

• ✅ No code changes, only documentation
• ✅ All new files follow markdown conventions
• ✅ Cross-references are bidirectional
• ✅ Consistent with existing documentation style
• ✅ No security issues
• ⚠️ Minor: Verify internal anchor links work correctly
• ⚠️ Minor: Verify coverage report path

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🎯 Final Recommendation: APPROVE ✅

This is an excellent PR that significantly improves the developer experience for AI agents working with this SDK. The documentation is comprehensive, well-organized, and practical.

Required Actions Before Merge:

1. ✅ None - can merge as-is

Recommended Actions (Nice-to-Have):

1. Verify all internal anchor links resolve correctly
2. Confirm coverage report output directory matches documentation
3. Double-check Python version requirements match pyproject.toml

Overall Assessment:

Quality: ⭐⭐⭐⭐⭐ (5/5)
Usefulness: ⭐⭐⭐⭐⭐ (5/5)
Completeness: ⭐⭐⭐⭐⭐ (5/5)

Verdict: This PR successfully addresses issue #153 and provides exactly what AI agents need to work effectively with this codebase. The task-oriented approach is a best practice for technical documentation. Well done! 🎉

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Built with love in Berlin 🐻 (nice touch on line 712!)

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Review generated by: Claude Code AI Agent
Review date: 2025-10-06

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ All tests successful. No failed tests found.

📢 Thoughts on this report? Let us know!

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud