hatch3r-learn
SolidCaptures learnings from completed development sessions into reusable knowledge files for future consultation. Invoke manually, from board-pickup after PR merge, or with a specific issue number for targeted reflection.
AI & Automation 26 stars
4 forks Updated 1 weeks ago MIT
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Quality Score: 83/100
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Recency 20%
Frontmatter 20%
Documentation 15%
Issue Health 10%
License 10%
Description 5%
Skill Content
# Learning Capture — Extract and Store Development Insights
## Quick Start
```
Task Progress:
- [ ] Step 0: Detect ambiguity (P8 B1)
- [ ] Step 1: Gather learning context
- [ ] Step 2: Extract learnings
- [ ] Step 3: Validate and write learning files
- [ ] Step 4: Summary
```
## Step 0 — Detect Ambiguity (P8 B1)
Before any action, scan the user's request and provided context for unresolved questions in scope, acceptance criteria, irreversibility, or constraint conflicts (contradictory inputs, missing target, unknown convention). If any are found, ask the user via the platform-native question tool per `agents/shared/user-question-protocol.md` — do not proceed under silent assumption. This is the default path, not an exception. Acceptable to proceed without asking ONLY when scope is single-target, single-concern, and the brief alone is testable. Any residual ambiguity discovered mid-workflow invokes the same protocol.
## Step 1: Gather Learning Context
1. Check what was recently completed:
- If invoked with an issue number: read the issue, its PR, and changes via `gh issue view` and `gh pr list --search`.
- If invoked standalone: **ASK** the user what they just completed.
- If invoked from board-pickup: use the issue/PR context already available.
2. Scan recent git history for context (`git log --oneline -20` on the current branch).
**ASK:** "What did you just complete? {auto-detected context}. Confirm or provide additional details."
## Step 2: Extract Learning...
Details
- Author
- hatch3r
- Repository
- hatch3r/hatch3r
- Created
- 5 months ago
- Last Updated
- 1 weeks ago
- Language
- TypeScript
- License
- MIT
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