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OpenClaw

metaskill

Teaches AI agents how to learn better by enforcing deep correction, transfer learning, and proactive pattern recognition. Use when an error occurs and needs deep analysis (not surface patch), before starting a complex task to check past analogies, or after success to capture winning patterns. NOT for routine file reads or simple one-off commands.

fahrulalwan0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install fahrulalwan/metaskill

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Metaskill

3 Core Components

  1. Deep Self-Correction (deep-correct.sh) — 3-level breakdown on errors:

    • Surface: What specifically failed
    • Principle: The underlying rule/constraint violated
    • Habit: Concrete behavioral change to prevent recurrence
  2. Transfer Learning (transfer-check.sh) — Before a task, search past learnings for analogous patterns. Maps domains (e.g., "auth" → "security") to prevent siloed learning.

  3. Proactive Pattern Recognition (success-capture.sh) — Log what worked and why, building a repository of successful patterns.

Usage

# When an error occurs
bash skills/metaskill/scripts/deep-correct.sh "description of the error"

# Before starting a complex task
bash skills/metaskill/scripts/transfer-check.sh "description of the new task"

# After successful execution
bash skills/metaskill/scripts/success-capture.sh "what worked" "why it worked"

# Monthly health eval
bash skills/metaskill/scripts/eval.sh --save

Configuration (LLM Provider)

Metaskill uses two provider tiers — fast (extraction) and deep (transfer/eval). Edit config.yaml to match your setup:

# config.yaml
providers:
  fast: anthropic   # change to: openai | ollama | gemini
  deep: anthropic
Provider Env Var Notes
anthropic ANTHROPIC_API_KEY Default
openai OPENAI_API_KEY
ollama (none needed) Local, free
gemini GOOGLE_API_KEY

Ollama example (fully local, no API key):

providers:
  fast: ollama
  deep: ollama
models:
  ollama:
    fast: llama3.2
    deep: llama3.1:70b

If no provider is available, metaskill falls back to manual/heuristic mode (still works, but less precise extraction).

Integration with Self-Improving-Agent

Writes to skills/self-improving-agent/.learnings/ if present, otherwise falls back to its own .learnings/ directory. No extra setup needed.

AGENTS.md Wiring (Mandatory)

Add to pre-t...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/fahrulalwan/metaskill
Version
—
Commit
38d8bf440ce2960d6dab4408345fccc750d91d12
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Metaskill

## 3 Core Components

1. **Deep Self-Correction (`deep-correct.sh`)** — 3-level breakdown on errors:
   - **Surface**: What specifically failed
   - **Principle**: The underlying rule/constraint violated
   - **Habit**: Concrete behavioral change to prevent recurrence

2. **Transfer Learning (`transfer-check.sh`)** — Before a task, search past learnings for analogous patterns. Maps domains (e.g., "auth" → "security") to prevent siloed learning.

3. **Proactive Pattern Recognition (`success-capture.sh`)** — Log what worked and why, building a repository of successful patterns.

## Usage

```bash
# When an error occurs
bash skills/metaskill/scripts/deep-correct.sh "description of the error"

# Before starting a complex task
bash skills/metaskill/scripts/transfer-check.sh "description of the new task"

# After successful execution
bash skills/metaskill/scripts/success-capture.sh "what worked" "why it worked"

# Monthly health eval
bash skills/metaskill/scripts/eval.sh --save
```

## Configuration (LLM Provider)

Metaskill uses two provider tiers — **fast** (extraction) and **deep** (transfer/eval). Edit `config.yaml` to match your setup:

```yaml
# config.yaml
providers:
  fast: anthropic   # change to: openai | ollama | gemini
  deep: anthropic
```

| Provider | Env Var | Notes |
|---|---|---|
| `anthropic` | `ANTHROPIC_API_KEY` | Default |
| `openai` | `OPENAI_API_KEY` | |
| `ollama` | *(none needed)* | Local, free |
| `gemini` | `GOOGLE_API_KEY` | |

**Ollama example** (fully local, no API key):
```yaml
providers:
  fast: ollama
  deep: ollama
models:
  ollama:
    fast: llama3.2
    deep: llama3.1:70b
```

If no provider is available, metaskill falls back to manual/heuristic mode (still works, but less precise extraction).

## Integration with Self-Improving-Agent

Writes to `skills/self-improving-agent/.learnings/` if present, otherwise falls back to its own `.learnings/` directory. No extra setup needed.

## AGENTS.md Wiring (Mandatory)

Add to pre-t...

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