Skill Extract

Learn: Session Knowledge Extraction

Extract reusable workflows, workarounds, and multi-step procedures discovered during a session into persistent skills. Complementary to the learn-tags rule — while [LEARN] tags record one-liner corrections in MEMORY.md, /skill-extract creates full skill definitions in skills/.

When to Use

• You discovered a non-obvious multi-step procedure
• You built a workaround for a recurring problem
• You developed a workflow that future sessions would benefit from
• the user says "save this as a skill", "learn this", or "remember how to do this"

Phase 1: Evaluate

Before creating anything, answer these 4 self-assessment questions:

1. Non-obvious? Would a future session figure this out without help, or would it waste time rediscovering it?
2. Future benefit? Will this come up again across sessions or projects?
3. Repeatable? Is this a procedure that can be followed step-by-step, or was it a one-off?
4. Multi-step? Does it involve 2+ non-trivial steps that benefit from being documented together?

Decision rule: If at least 3 of 4 answers are "yes", proceed to Phase 2. Otherwise, suggest recording as a [LEARN] tag in MEMORY.md instead (simpler, lower overhead).

If borderline, ask the user: > "This seems useful but may not warrant a full skill. Should I create a skill or just add a [LEARN] tag to MEMORY.md?"

Phase 2: Creation Guard + Read Patterns

Run the skill-preflight analysis (see [skills/skill-preflight/SKILL.md](../skill-preflight/SKILL.md)):

1. Identify the proposal (name, type, purpose, key functions, keywords)
2. Search existing skills and agents for overlap (skill-index scan + keyword grep)
3. Analyse overlap and generate a recommendation:

| Recommendation | Criteria | Action | |----------------|----------|--------| | PROCEED | "This is a [workflow/task/agent-delegation/reference]-based skill."

2. Identify resources needed:

• Does the skill need scripts/ for deterministic operations?
• Does it need references/ for detailed specs, rubrics, or large examples?
• Does it need to delegate to subagents?

3. Draft the architecture — for non-trivial skills, sketch the flow:

Input → [Step A] → REVIEW GATE → [Step B] → Output

Phase 4: Build the Skill

Write skills/{name}/SKILL.md:

Frontmatter

---
name: {kebab-case-name}
description: "{What it does. Concrete task types. Use when...}"
allowed-tools:
  - {minimum set of tools needed}
---

Body Structure

The body varies by pattern, but always includes these elements:

# {Skill Name}: {Short Description}

## When to Use
[Activation conditions — natural language triggers and /command]

## {Main Workflow / Processing Rules / Delegation Protocol}
[The core of the skill — structured per the chosen pattern]

## {Anti-Patterns / Never Do These}
[What NOT to do — agents default to generic without constraints]

## Output Format
[What the output looks like]

## Verification
[How to confirm it worked]

Optional sections (add when relevant):

• ## Defaults — assumptions table to reduce friction
• ## Examples — concrete before/after or good/bad snippets
• ## Notes — edge cases, limitations

Solution Pattern (for debugging/workaround skills)

When the skill captures a fix, workaround, or debugging procedure, use this body structure:

# {Skill Name}: {Short Description}

## Problem
[Specific error message or symptom. Quote the exact text users would see.]

## Context / Triggers
[When this occurs — tool versions, file types, OS, configurations]

## Solution
[Step-by-step fix. Imperative form.]

## Verification
[How to confirm the fix worked]

## Example
[Concrete before/after or input/output]

## Notes
[Edge cases, alternative approaches, when this does NOT apply]

Description Optimization

The description: field in frontmatter is what triggers skill discovery. Write it to match how a user would describe their problem:

• Include specific error messages or symptoms — "Fix Package biblatex Error: File 'references.bib' not found"
• Include context markers — file types, tools, situations where this applies
• Include negative cases — "Not for general proofreading (use /proofread instead)"
• Use natural trigger phrases — the exact words a user would type

Extraction Checklist

During skill creation, mentally verify each point before finalising:

1. Trigger coverage — would a user find this skill from 3 different phrasings of the same problem?
2. Self-contained — can the skill be followed without reading other files (except references/)?
3. Anti-patterns present — at least 2 "don't do this" entries to prevent common mistakes?
4. Verification step — does the skill tell you how to confirm it worked?
5. Scope bounded — is it clear what this skill does NOT do?

Writing Rules

• Imperative form. "Parse the input" not "You should parse the input."
• Be specific about what NOT to do. Anti-pattern lists are highly effective.
• Include concrete examples. Show expected input/output pairs.
• Keep SKILL.md under 300 lines. Move detail to references/.
• Every instruction must be actionable. No throat-clearing.
• Use tables for structured data. Faster to parse than prose.

Naming Conventions

• Directory and name: kebab-case, descriptive, 2-4 words
• Avoid generic names: fix-bug is bad; fix-overleaf-sync-conflict is good
• Match the trigger: If the natural trigger is "compile my slides", the name should relate to slide compilation

Allowed Tools

Follow principle of least privilege:

| Skill type | Tools | |-----------|-------| | Report-only | Read, Glob, Grep | | File-creating | + Write, Edit | | Shell-needing | + specific Bash(command*) patterns | | Interactive | + AskUserQuestion | | Delegating | + Task |

Phase 5: Validate

Run the validation script on the new skill:

uv run python skills/skill-extract/scripts/validate_skill.py skills/{name}

Fix all errors. Address warnings to improve quality. Use --strict to promote warnings to errors.

The validator checks: frontmatter validity, name format and directory match, description quality, body length, broken links, referenced directories, and placeholder text.

Phase 6: Deploy and Confirm

1. Copy the skill to the deployed location (rsync won't run until next session start):

``bash cp -r skills/{name} ~/.claude/skills/{name} ``

1. Check that ~/.claude/skills/{name}/SKILL.md exists
2. Tell the user: "Created /{name} — [one-line summary]. It's available immediately in all projects."
3. If the skill is substantial, suggest updating docs/components/skills.md with the new entry

What This Skill Does NOT Do

• Does not replace [LEARN] tags — one-liner corrections still go in MEMORY.md via the learn-tags rule; /skill-extract is for multi-step procedures, not one-liners
• Does not create agents — agents need separate context and persistent memory
• Does not modify existing skills — if an existing skill needs updating, do that directly

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: flonat
• Source: flonat/claude-research
• License: MIT

Install and usage instructions live in the source repository linked above.