Extracts reusable skills from work sessions. Use when: (1) a non-obvious problem was solved worth preserving, (2) a pattern was discovered that would help future sessions, (3) a workaround or debugging technique needs documentation. Manual invocation only via /skill-extractor command - no automatic triggers or hooks.
Extracts reusable skills from work sessions. Use when: (1) a non-obvious problem was solved worth preserving, (2) a pattern was discovered that would help future sessions, (3) a workaround or debugging technique needs documentation. Manual invocation only via /skill-extractor command - no automatic triggers or hooks.
Unverified solutions (wait until it actually works)
Finding Extraction Candidates
Use these prompts to identify knowledge worth extracting:
"What did I just learn that wasn't obvious before starting?"
"If I faced this exact problem again, what would I wish I knew?"
"What error message or symptom led me here, and what was the actual cause?"
"Is this pattern specific to this project, or would it help in similar projects?"
"What would I tell a colleague who hits this same issue?"
If you can't answer at least two of these with something non-trivial, it's probably not worth extracting.
Command
/skill-extractor [--project] [context hint]
Default: saves to ~/.claude/skills/[name]/SKILL.md
--project: saves to .claude/skills/[name]/SKILL.md
Context hint helps focus extraction (e.g., /skill-extractor the cyclic data DoS fix)
Extraction Process
Step 0: Check for Existing Skills
Before creating a new skill, search for existing ones that might cover the same ground:
# Check user skillsls ~/.claude/skills/
# Check project skillsls .claude/skills/
# Search by keyword
grep -r "keyword" ~/.claude/skills/ .claude/skills/ 2>/dev/null
If a related skill exists, consider updating it instead of creating a new one. See skill-lifecycle.md for guidance on when to update vs create.
Step 1: Identify the Learning
If $ARGUMENTS contains a context hint (e.g., "the cyclic data DoS fix"), use it to focus the extraction on that specific topic.
Analyze the conversation to identify:
What problem was solved?
What made the solution non-obvious?
What would someone need to know to solve this faster next time?
What are the exact trigger conditions (error messages, symptoms)?
Present a brief summary to the user:
I identified this potential skill:
**Problem:** [Brief description]
**Key insight:** [What made it non-obvious]
**Triggers:** [Error messages or symptoms]
Step 2: Quality Assessment
Evaluate the candidate skill against these criteria:
Criterion
Pass?
Evidence
Reusable - Helps future tasks, not just this instance
[Why]
Non-trivial - Required discovery, not docs lookup
[Why]
Verified - Solution actually worked
[Evidence]
Specific triggers - Exact error messages or scenarios
[What they are]
Explains WHY - Trade-offs and judgment, not just steps
[How]
Value-add - Teaches judgment, not just facts Claude could look up
[How]
Present assessment to user and ask: "Proceed with extraction? [yes/no]"
The user decides whether to proceed regardless of how many criteria pass. Respect their judgment - if they say yes, extract; if no, skip.
Step 3: Gather Details
Ask the user:
Skill name - Suggest a kebab-case name based on context, let them override
Scope - User-level (default) or project-level (--project)
Step 4: Optional Research
If the topic involves a specific library or framework:
Use web search to find current best practices
Use Context7 MCP (if available) for official documentation
Include relevant sources in the References section
If you catch yourself thinking any of these, do NOT extract:
"This might be useful someday" - Only extract verified, reusable knowledge
"Let me just save everything" - Quality over quantity
"The user didn't confirm but it seems valuable" - Always get explicit confirmation
"I'll skip the 'When NOT to Use' section" - It's mandatory for good skills
"The description can be vague" - Specific triggers are essential for discovery
Example Extraction
Scenario: User discovered that an AST visitor crashes with RecursionError when analyzing serialized files containing cyclic references (e.g., a list that contains itself).
Identified learning:
Cyclic data structures create cyclic ASTs
Visitor pattern without cycle tracking causes infinite recursion
Need to track visited nodes or enforce depth limits
Generated skill name:cyclic-ast-visitor-hardening
Key sections:
When to Use: "RecursionError in AST visitor", "analyzing untrusted serialized input"
When NOT to Use: "Recursion from deeply nested (but acyclic) structures"
