capture-learning

SkillDocs & knowledge

Capture 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.

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