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OpenClaw

crew-school

Structured learning system for AI agent crews. Design curricula, run research sessions, track progress, and prevent common failure modes (lazy output, planning-without-executing). Use when setting up agent learning, running training sessions, auditing knowledge gaps, or building a curriculum for specialized agents. Works for single agents or multi-agent crews.

crewhaus0 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 crewhaus/crew-school

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

Crew School — Agent Learning System

Run structured learning sessions that produce real knowledge articles, not plans. Prevents the #1 failure mode: agents that say "I'll research this" and stop.

Quick Start

  1. Identify gaps — What does your agent need to learn? Check what knowledge files exist vs what's needed.
  2. Pick a topic — Start with critical gaps that directly impact daily work.
  3. Spawn a session — Use the Session Template below. The anti-laziness guardrails are load-bearing — don't remove them.
  4. Verify output — Check line count, source count, and scan for planning language.

Session Template

Use this exact template when spawning learning sessions. Fill in the brackets.

CREW LEARNING SESSION — [ROLE]: [TOPIC]

You are the [ROLE] agent. Your assignment is to research [TOPIC] and produce a comprehensive knowledge article.

### Context
[2-3 sentences: why this topic matters for this agent's role]

### Prerequisites
[List knowledge files to read first, or "None"]

### Research Assignment
Deep research on [TOPIC]. You MUST cover:
1. [Subtopic 1] — [Specific questions]
2. [Subtopic 2] — [Specific questions]
3. [Subtopic 3] — [Specific questions]

### Execution Rules — READ CAREFULLY
- DO NOT PLAN. EXECUTE. If you write "I will..." or "Let me create...", STOP and DO IT instead.
- DO NOT stop after outlining. Every section must contain real research findings.
- Search the web. Perform at least 5 web searches. Read at least 2 full articles.
- Cite sources. Every claim needs a source URL.
- Be specific. Names, numbers, examples > generic advice.

### Output Requirements
Write findings to `knowledge/[filename].md` with this structure:

# [Topic]

> **TL;DR:** [2-3 sentence summary]
> **Applies to:** [Which roles]
> **Prerequisites:** [Other knowledge files]

## Key Takeaways
- [Bullet list of actionable findings]

## [Sections with real findings]

## Practical Application
[Steps, templates, checklists the agent can use immediately]

#...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/crewhaus/crew-school
Version
—
Commit
a8367025d09d2efaef8a1862e3936182da9aadca
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
# Crew School — Agent Learning System

Run structured learning sessions that produce real knowledge articles, not plans. Prevents the #1 failure mode: agents that say "I'll research this" and stop.

## Quick Start

1. **Identify gaps** — What does your agent need to learn? Check what knowledge files exist vs what's needed.
2. **Pick a topic** — Start with critical gaps that directly impact daily work.
3. **Spawn a session** — Use the Session Template below. The anti-laziness guardrails are load-bearing — don't remove them.
4. **Verify output** — Check line count, source count, and scan for planning language.

## Session Template

Use this exact template when spawning learning sessions. Fill in the brackets.

```
CREW LEARNING SESSION — [ROLE]: [TOPIC]

You are the [ROLE] agent. Your assignment is to research [TOPIC] and produce a comprehensive knowledge article.

### Context
[2-3 sentences: why this topic matters for this agent's role]

### Prerequisites
[List knowledge files to read first, or "None"]

### Research Assignment
Deep research on [TOPIC]. You MUST cover:
1. [Subtopic 1] — [Specific questions]
2. [Subtopic 2] — [Specific questions]
3. [Subtopic 3] — [Specific questions]

### Execution Rules — READ CAREFULLY
- DO NOT PLAN. EXECUTE. If you write "I will..." or "Let me create...", STOP and DO IT instead.
- DO NOT stop after outlining. Every section must contain real research findings.
- Search the web. Perform at least 5 web searches. Read at least 2 full articles.
- Cite sources. Every claim needs a source URL.
- Be specific. Names, numbers, examples > generic advice.

### Output Requirements
Write findings to `knowledge/[filename].md` with this structure:

# [Topic]

> **TL;DR:** [2-3 sentence summary]
> **Applies to:** [Which roles]
> **Prerequisites:** [Other knowledge files]

## Key Takeaways
- [Bullet list of actionable findings]

## [Sections with real findings]

## Practical Application
[Steps, templates, checklists the agent can use immediately]

#...

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