Changelog Automation

Changelog Automation

Patterns and tools for automating changelog generation, release notes, and version management following industry standards.

Use this skill when

• Setting up automated changelog generation
• Implementing conventional commits
• Creating release note workflows
• Standardizing commit message formats
• Managing semantic versioning

Do not use this skill when

• The project has no release process or versioning
• You only need a one-time manual release note
• Commit history is unavailable or unreliable

Instructions

• Select a changelog format and versioning strategy.
• Enforce commit conventions or labeling rules.
• Configure tooling to generate and publish notes.
• Review output for accuracy, completeness, and wording.
• If detailed examples are required, open resources/implementation-playbook.md.

Safety

• Avoid exposing secrets or internal-only details in release notes.

Resources

• resources/implementation-playbook.md for detailed patterns, templates, and examples.

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags changelog-automation <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns