Commit f4e89d9
feat: Add automated demo command for AgentReady (#24)
* feat: Add automated demo command for AgentReady
Implements P0 feature to showcase AgentReady capabilities with a single command.
Features:
- agentready demo command creates sample repository and runs assessment
- Supports Python (default) and JavaScript demo repositories
- Real-time progress indicators with color-coded output (✓/✗/⊘)
- Auto-opens HTML report in browser (optional --no-browser flag)
- Generates HTML, Markdown, and JSON reports in .agentready-demo/
- Options: --language, --no-browser, --keep-repo
Implementation:
- Created src/agentready/cli/demo.py with demo command logic
- Registered demo command in CLI main.py
- Added comprehensive unit tests in tests/unit/test_demo.py
- Demo completes in ~3-5 seconds with 25 attribute assessments
Perfect for presentations, stakeholder demos, and user onboarding.
Closes #1
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Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com>
* fix: install AgentReady from source in CI workflow
The workflow was attempting to install agentready from PyPI, but the
package has never been published. Changed to install from checked-out
repository source using 'pip install -e .' instead.
This ensures CI tests the actual code in the PR rather than a
potentially stale PyPI version.
Fixes GitHub Actions workflow failure on PR #24.
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Co-Authored-By: Claude <noreply@anthropic.com>
* feat: add manual PyPI publishing workflow
Added workflow_dispatch workflow for manual PyPI publishing with:
- Dry run mode (publishes to TestPyPI for testing)
- Production mode (publishes to PyPI)
- Optional version override
- Automatic GitHub release creation
- Built-in validation with twine check
- Secure: Uses environment variables to prevent command injection
Usage:
1. Configure TEST_PYPI_TOKEN and PYPI_TOKEN secrets
2. Go to Actions → "Publish to PyPI" → Run workflow
3. Choose dry run (TestPyPI) or production (PyPI)
Future enhancement tracked in issue #25 for automated integration
with semantic-release workflow.
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Co-Authored-By: Claude <noreply@anthropic.com>
* feat: implement continuous learning loop with LLM-powered skill extraction
Add comprehensive learning system that extracts high-quality skills from assessments:
- New 'learn' CLI command with heuristic and LLM enrichment modes
- Claude API integration for detailed skill analysis and instruction generation
- LLM response caching system (7-day TTL) to reduce API costs
- Code sampling for real repository examples
- Pattern extraction from high-scoring assessment attributes
- Support for multiple output formats (JSON, SKILL.md, GitHub issues)
Technical additions:
- src/agentready/cli/learn.py - Main learning command implementation
- src/agentready/learners/ - Pattern extraction and LLM enrichment modules
- src/agentready/services/llm_cache.py - LLM response caching
- src/agentready/models/discovered_skill.py - Skill data model
- tests/unit/learners/ - Unit tests for learning modules
- .github/workflows/continuous-learning.yml - CI workflow
- .github/CLAUDE_INTEGRATION.md - Integration documentation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Jeremy Eder <jeremyeder@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>1 parent 24f437f commit f4e89d9
36 files changed
Lines changed: 7888 additions & 17 deletions
File tree
- .agentready-demo
- .github
- workflows
- .skills-proposals
- implement-type-annotations
- setup-claude-md
- specs
- src/agentready
- cli
- learners
- models
- services
- tests/unit
- learners
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