techwavedev Pull Request Enhancement
Workflow
- Run
git diff <base>...HEAD --statto identify changed files and scope - Categorise changes: source, test, config, docs, build, styles
- Generate the PR description using the template below
- Add a review checklist based on which file categories changed
- Flag breaking changes, security-sensitive files, or large diffs (>500 lines)
PR Description Template
## Summary
<!-- one-paragraph executive summary: what changed and why -->
## Changes
| Category | Files | Key change |
|----------|-------|------------|
| source | `src/auth.ts` | added OAuth2 PKCE flow |
| test | `tests/auth.test.ts` | covers token refresh edge case |
| config | `.env.example` | new `OAUTH_CLIENT_ID` var |
## Why
<!-- link to issue/ticket + one sentence on motivation -->
## Testing
- [ ] unit tests pass (`npm test`)
- [ ] manual smoke test on staging
- [ ] no coverage regression
## Risks & Rollback
- **Breaking?** yes / no
- **Rollback**: revert this commit; no migration needed
- **Risk level**: low / medium / high — because ___
Review Checklist Rules
Add checklist sections only when the matching file category appears in the diff:
| File category | Checklist items |
|---|---|
| source | no debug statements, functions <50 lines, descriptive names, error handling |
| test | meaningful assertions, edge cases, no flaky tests, AAA pattern |
| config | no hardcoded secrets, env vars documented, backwards compatible |
| docs | accurate, examples included, changelog updated |
security-sensitive (auth, crypto, token, password in path) |
input validation, no secrets in logs, authz correct |
Splitting Large PRs
When diff exceeds 20 files or 1000 lines, suggest splitting by feature area:
git checkout -b feature/part-1
git cherry-pick <commits-for-part-1>
Resources
resources/implementation-playbook.md— Python helpers for automated PR analysis, coverage reports, and risk scoring
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.
# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Comprehensive Review Pr Enhance"
Storing Results
After completing work, store workflow/automation decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
--type technical --project <project> \
--tags comprehensive-review-pr-enhance workflow
Multi-Agent Collaboration
Share workflow state with other agents so they can trigger, monitor, or extend the automation.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
--project <project>
Playbook Engine
Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.