learning-loop
SkillAI & modelsStructured self-improvement system for AI agents with confidence decay, cross-agent sharing, and anomaly detection. Use when: (1) After debugging sessions to capture lessons learned, (2) When receiving feedback or corrections from users, (3) Before risky actions to check relevant rules, (4) Weekly t
Use learning-loop in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add learning-loop and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the learning-loop skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by leoyeai/openclaw-master-skills in skills/learning-loop/SKILL.md and read by ahel’s review.
What the learning loop is
stellar-build ships with a local learning loop: the more you use your skills,
the better they get — without anything leaving your machine. It mirrors OpenJarvis's
jarvis optimize skills / jarvis bench skills cycle, adapted for markdown skills.
Three moving parts:
- Capture — a skill use is a multi-turn span, so capture is decoupled
from "skill became active". A
PostToolUse:Skillhook marks which skill is resident; aStophook records each turn's real assistant output; and aSessionEndhook closes the span with a summary, counting it as one activation. Everything lands in~/.stellar-build/traces/<day>.jsonl. Fully local. Opt out withSTELLAR_BUILD_NO_TRACE=1. - Optimize (
/optimize-skills) — compiles those traces into sharper skills: tightened triggers/instructions and few-shot examples mined from your own successful runs. Every edit is backed up and reversible. - Bench (
/bench-skills) — scores skills on held-out prompts so you can measure whether an optimization actually helped.
use skills ──▶ ~/.stellar-build/traces/*.jsonl
│
/optimize-skills (DSPy-style compile)
│
▼
sharper ~/.claude/skills/<skill>/SKILL.md ◀── stellar-loop restore (undo)
│
/bench-skills (measure the lift)
What to do when invoked
- Overview / "how does it work" → explain the three parts above, then point to the two action skills and the CLI.
- "status" / "is tracing on" / "show my usage" → run the CLI and relay the
output:
stellar-loop status— install + capture + hook state, trace/optimization countsstellar-loop stats— per-skill usage from local tracesstellar-loop tail— most recent trace records The CLI is installed at~/.stellar-build/bin/stellar-loop(add~/.stellar-build/binto PATH, or call it by full path).
- "optimize" / "improve my skills" → hand off to the
optimize-skillsskill. - "benchmark" / "did it help" → hand off to the
bench-skillsskill. - "undo" / "restore" →
stellar-loop restore <skill>reverts the last optimization for that skill from its backup.
Data & privacy
- Everything lives under
~/.stellar-build/(traces/,backups/,bench/,optimizations.jsonl). Override the root withSTELLAR_BUILD_HOME. - Nothing is transmitted anywhere — no API calls, no telemetry. The "optimizer" is your own agent session reasoning over local files.
- Result previews are truncated and examples are generalized before they ever land
in a skill, so traces and learned blocks should not accumulate secrets. Wipe
anytime with
stellar-loop clear(--allalso clears backups).
Quick reference
| Goal | Do this |
|---|---|
| See if it's working | stellar-loop status |
| See what you use most | stellar-loop stats |
| Make skills sharper | /optimize-skills (or stellar-loop optimize) |
| Measure the lift | /bench-skills (or stellar-loop bench) |
| Undo an optimization | stellar-loop restore <skill> |
| Turn capture off | export STELLAR_BUILD_NO_TRACE=1 |
| Delete all traces | stellar-loop clear |
Signals
- GitHub stars
- 2k
- Forks
- 325
- Last commit
- Jul 2026
Advanced
- Item type
- skill
- Key
learning-loop- Source
- github.com/leoyeai/openclaw-master-skills
github.com/leoyeai/openclaw-master-skills
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