# Changelog Automation

> Automate changelog generation from commits, PRs, and releases following Keep a Changelog format. Use when setting up release workflows, generating release notes, or standardizing commit conventions.

- Skill: `techwavedev/changelog-automation` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/changelog-automation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/changelog-automation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/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](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:
```bash
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:

```bash
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