fastclaw-skill-learner

name	fastclaw-skill-learner
description	Analyze conversations to extract reusable skill patterns. Used internally by FastClaw to auto-generate skills from complex multi-step tasks.
metadata	{"fastclaw":{"always":true}}

Skill Learner

Analyze a conversation and determine if it demonstrates a reusable multi-step workflow that should be saved as a skill.

When to Extract

Extract a skill when ALL of the following are true:

• The task involved 3+ tool calls in a clear, repeatable sequence
• The steps form a general procedure useful beyond this specific conversation
• The workflow is not trivially simple (not just "read a file and summarize it")

Do NOT extract when:

• The task is one-off or highly specific to current context
• The steps are standard and don't need specialized instructions
• A similar skill already exists

How to Analyze

Given a conversation transcript, identify:

1. The core workflow — What sequence of actions was performed?
2. The pattern — Is this generalizable to other inputs/contexts?
3. The value — Would having this as a skill save significant effort next time?

Output Format

If the conversation demonstrates a reusable skill, output JSON:

{
  "extract": true,
  "skill": {
    "name": "Human Readable Name",
    "slug": "kebab-case-slug",
    "description": "One-line description of what this skill does and when to trigger it",
    "content": "Full SKILL.md content with YAML frontmatter and markdown instructions"
  }
}

If not reusable, output:

{
  "extract": false
}

Skill Content Guidelines

When generating the SKILL.md content:

• Include proper YAML frontmatter with name and description
• Write clear step-by-step instructions in markdown
• Generalize from the specific conversation — replace specific values with placeholders
• Explain the reasoning behind each step, not just the commands
• Include example inputs/outputs where helpful
• Keep under 500 lines
• Use {baseDir} for any bundled resource references

Example Extraction

A conversation where the user asks to set up a new Go project with CI, and the agent creates go.mod, writes a Makefile, sets up GitHub Actions, and adds a Dockerfile — this is a good extraction candidate because:

• Multiple coordinated steps (4+ tool calls)
• Generalizable to any new Go project
• Saves significant setup time

The extracted skill would capture the project structure, file templates, and the sequence of steps, parameterized for project name and Go version.