capture-learning
SkillDocs & knowledgeCapture significant learnings from the current work session. Structures insights for future sessions and updates agent memory.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the capture-learning skill
What this skill tells your AI
The instructions your AI receives, as published by kastalien-research/thoughtbox in .agents/skills/capture-learning/SKILL.md and read by ahel’s review.
Reflect on the current session and capture learnings. Context: $ARGUMENTS
Process
1. Reflect
- What was the main problem being solved?
- What non-obvious insights emerged?
- What patterns are reusable in future work?
- What failed and why?
2. Structure the Learning
Format each learning as:
### [Date]: [Title]
- **Issue**: [The problem encountered]
- **Solution**: [What worked]
- **Pattern**: [The reusable principle extracted]
- **Files**: [Key file references, if applicable]
- **Freshness**: HOT (actively relevant) | WARM (occasionally relevant) | COLD (reference only)
3. Store
Write the learning to the appropriate location:
- Agent-specific patterns: Update the relevant agent's project memory
- Project-wide rules: Add to
.Codex/rules/as a new file or append to an existing one - Debugging insights: Add to auto-memory
MEMORY.md
4. Calibrate
Check existing learnings for staleness:
- Are any HOT items now WARM or COLD?
- Are any previous learnings contradicted by what we learned today?
- Remove or update anything that's no longer accurate.
Signals
- GitHub stars
- 64
- Forks
- 20
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
capture-learning- Source
- github.com/kastalien-research/thoughtbox