Skill Factory

Skill Factory

Automated pipeline: session analysis -> duplicate check -> skill creation. Requires: Python 3.8+, bash, git. Agent Teams path requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.

Parameter Parsing

Parse $ARGUMENTS for flags:

If no arguments, run full auto-detection pipeline.

Phase 1: Session Analysis

Collect what happened in this session:

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# Uncommitted changes
git diff HEAD --name-only 2>/dev/null

# Recent commits on current branch
git log --oneline -20 2>/dev/null

# Branch diff from main
git diff main...HEAD --name-only 2>/dev/null

From collected changes, identify candidate patterns - repeatable workflows that appeared:

1. Multi-step sequences - 3+ actions performed in consistent order
2. Tool combinations - Specific tools used together (e.g., Grep + Read + Edit)
3. Domain procedures - File types or directories accessed with specific operations
4. Repeated transformations - Same type of change applied to multiple files

If --target is specified, focus analysis on that named pattern only.

For each candidate, produce a JSON entry (internal, not shown to user):

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{
  "name": "pattern-name",
  "description": "What was done repeatedly",
  "files": ["path/a.ts", "path/b.ts"],
  "steps": ["Step1", "Step2", "Step3"],
  "step_count": 3
}

Present findings to user:

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Session Analysis Complete

Candidate Patterns Found: N

1. [pattern-name] - "Description of what was done repeatedly"
   Files: path/a.ts, path/b.ts (N files)
   Steps: Step1 -> Step2 -> Step3

2. [pattern-name] - "Description"
   ...

Which patterns should become skills? (select or 'all')

Wait for user selection before proceeding.

Phase 2: Similarity Check

For each selected pattern, check against existing inventory.

Step 1: Scan inventory

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bash $HOME/.claude/skills/skill-factory/scripts/scan-inventory.sh --scope all > /tmp/sf-manifest.json

Step 2: Score similarity

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python3 $HOME/.claude/skills/skill-factory/scripts/similarity-scorer.py \
  --candidate "<pattern description>" \
  --candidate-name "<pattern-name>" \
  --manifest /tmp/sf-manifest.json \
  --top 3

Step 3: Apply decision logic (see references/decision-tree.md)

Present results to user:

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Similarity Check Results

Pattern: "pdf-batch-edit"
  Top match: nano-pdf (score: 0.72) -> MERGE
  Recommendation: Extend nano-pdf with batch operations

Pattern: "config-updater"
  Top match: init-project (score: 0.45) -> UPDATE
  Recommendation: Add config-update subsection to init-project

Pattern: "api-load-test"
  Top match: e2e (score: 0.24) -> CREATE
  Recommendation: Create new skill

Action for each pattern? (CREATE / UPDATE / MERGE / SKIP)

Wait for user decision per pattern.

Phase 3: Blueprint

For each CREATE/UPDATE/MERGE decision, design the skill structure.

CREATE Blueprint

Select template type from references/skill-templates.md:

• Workflow for sequential processes
• Task/Tool for operation collections
• Reference for domain knowledge
• Verification for automated checks

Generate blueprint:

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Blueprint: api-load-test

Type: Workflow
Scope: global (~/.claude/skills/)
Structure:
  api-load-test/
  ├── SKILL.md (~200 lines)
  │   ├── Frontmatter: name, description with triggers
  │   ├── Overview
  │   ├── Prerequisites
  │   ├── Workflow (4 steps)
  │   └── Output Format
  └── scripts/
      └── run-load-test.sh

Key sections:
  1. Target URL configuration
  2. Load profile definition
  3. Test execution
  4. Results analysis

Approve this blueprint? (y/n/edit)

Wait for user approval.

UPDATE Blueprint

For UPDATE verdicts (score 0.3-0.6), plan a lightweight addition to the existing skill:

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UPDATE Blueprint: config-updater -> init-project

Target skill: ~/.claude/skills/init-project/SKILL.md
Action: Add subsection "## Config Update" with steps
Estimated diff: +20-40 lines in existing SKILL.md

MERGE Blueprint

For MERGE verdicts (score 0.6-0.8), plan a significant extension of the existing skill:

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MERGE Blueprint: pdf-batch-edit -> nano-pdf

Target skill: ~/.claude/skills/nano-pdf/SKILL.md
Sections to add: "## Batch Operations" (new workflow section)
Scripts to add: scripts/batch-process.sh
Estimated diff: +60-100 lines in SKILL.md, +1 script

Phase 4: Execution

Two paths based on --no-team flag and Agent Teams availability.

Check Agent Teams availability:

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[ "${CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS:-0}" = "1" ] && echo "teams" || echo "no-team"

If --no-team is set or env var is missing/0, use Path B automatically.

Path A: Agent Teams (default)

Read references/team-composition.md for full team details.

Team: 3 teammates (tami, jiwon, duri)

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TeamCreate -> "skill-factory-run"

TaskCreate -> tami's analysis tasks (T1-T6)
TaskCreate -> jiwon's creation tasks (T7-T12, blocked by T6)
TaskCreate -> duri's validation tasks (T13-T18, blocked by T12)

Task -> tami (Explore, sonnet, blue)
  "Analyze session, run scan-inventory.sh, run similarity-scorer.py, report findings"

Task -> jiwon (general-purpose, sonnet, green)
  "For CREATE: read skill-templates.md, create SKILL.md + resources based on blueprint"
  "For UPDATE/MERGE: read target skill, apply diff from blueprint, add new sections/scripts"

Task -> duri (general-purpose, sonnet, yellow)
  "Run validate-skill.sh, verify triggers, register skill"

Pipeline:

1. tami completes analysis -> reports to lead
2. Lead confirms with user (Checkpoint 1-2)
3. jiwon creates skill files -> reports to lead
4. Lead confirms with user (Checkpoint 3)
5. duri validates and registers -> reports to lead
6. Lead confirms with user (Checkpoint 4)
7. Shutdown all teammates, TeamDelete

Path B: Sequential (--no-team)

Execute the same phases inline without Agent Teams:

1. Run scan-inventory.sh and similarity-scorer.py directly
2. Checkpoint 1-2: Present similarity results, ask user for CREATE/UPDATE/MERGE/SKIP per pattern
3. Design blueprint based on template selection
4. Checkpoint 3: Present blueprint, wait for user approval
5. Create/update skill directory and files based on approved blueprint
6. Run validate-skill.sh to verify
7. Checkpoint 4: Present validation results, ask user to register or edit
8. Register and log

--dry-run Mode

Stop after Phase 3 (blueprint). Print the blueprint and exit without creating files:

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DRY RUN COMPLETE

Patterns analyzed: N
Decisions: X CREATE, Y MERGE, Z SKIP
Blueprints generated: X

No files were created. Remove --dry-run to execute.

Phase 5: Registration

After validation passes:

1. Log creation - Append to ~/.claude/skill-factory.log:

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   [2026-02-18T14:30:00] CREATED api-load-test (global) from session patterns
   [2026-02-18T14:30:00] MERGED batch-operations into nano-pdf
   

2. Scope placement:

   • --scope global: ~/.claude/skills/<name>/
   • --scope project: .claude/skills/<name>/

3. Optional CLAUDE.md update: If project-scoped, offer to add skill reference to project CLAUDE.md.

Output Format

Final report after all patterns are processed:

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Skill Factory Report

Session: <branch-name or "main">
Patterns found: N
Patterns processed: M

Results:
  CREATED: api-load-test (global) - 4 files, 180 lines
  MERGED:  batch-ops into nano-pdf - 2 sections added
  SKIPPED: data-transform (0.85 match with data-research)

Files created/modified:
  ~/.claude/skills/api-load-test/SKILL.md
  ~/.claude/skills/api-load-test/scripts/run-load-test.sh
  ~/.claude/skills/nano-pdf/SKILL.md (updated)

Validation: ALL PASS
Log: ~/.claude/skill-factory.log

Next steps:
  Test the new skill: /api-load-test
  Review: cat ~/.claude/skills/api-load-test/SKILL.md

Error Handling

Related Files