Skill Evolver

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name: skill-evolver description: | A complete skill lifecycle manager for discovering, orchestrating, fusing, and evolving skills.

Helps decide which skills to use, how to compose or fuse them, and whether to materialize a successful workflow into a new reusable skill.

Trigger when: - User asks how to choose or combine skills - No single skill is sufficient and orchestration is needed - A solved workflow may need to be preserved as a new skill - Multiple skills need to be fused into one

Do not trigger when: - User explicitly requests one specific skill - Native Claude ability is obviously sufficient

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Skill Evolver

Solve first. Materialize later.

Workflow

Phase 0: Setup Output Directory

Create a timestamped output directory for this session:

# Format: output/MM-DD-/
Example: output/03-09-pdf-translate/
mkdir -p "output/$(date +%m-%d)-"

> Tip: Use a short slug derived from the task (e.g., pdf-translate, data-export, api-integration)

Store the output path for subsequent phases:

OUTPUT_DIR=output/

Phase 1: Intent Analysis

Analyze the user task and output ${OUTPUT_DIR}/01-intent.md. See template: references/templates/01-intent.md

Phase 2: Skill Search

Follow the complete skill search workflow: → references/skill-search.md

This workflow covers:

CLI prerequisites and installation

Local + Registry (dual-track) search

Skill selection checkpoint

Installation and verification

Security audit

Output files:

${OUTPUT_DIR}/02-candidates.md - Merged search results

${OUTPUT_DIR}/02-verify.md - Installation verification (if installed)

${OUTPUT_DIR}/02-audit.md - Security audit report (if installed)

Phase 3: Deep Inspection

For each candidate skill, perform deep analysis:

Follow the workflow: references/skill-inspector.md

Output: ${OUTPUT_DIR}/03-inspection.md

Checkpoint: Approach Decision

After inspection, evaluate whether skills can solve the task:

LLM evaluates:

Do skill capabilities match task requirements?

Is modification needed?

Is fusion beneficial?

LLM Recommendation:

Orchestration (skills match well, no major modification)

Fusion (skills partially match, combining creates new value)

Native (no suitable skills found)

Options for user:

A: Orchestration (LLM recommended)

B: Skill Fusion (enter coding mode)

C: Use native abilities instead

D: Re-analyze (return to Phase 3)

Phase 3.5: Skill Fusion (Conditional)

Only if approach is Fusion

Follow the complete skill fusion workflow: → references/skill-fusion.md

This workflow covers:

Fusion spec design

Invoke skill-creator

Audit fused skill

Output files:

${OUTPUT_DIR}/03-fusion-spec.md - Fusion specification

${OUTPUT_DIR}/03-fusion-audit.md - Security audit (if fusion)

Phase 4: Orchestration

Design execution plan and output ${OUTPUT_DIR}/04-orchestration.md. See template: references/templates/04-orchestration.md

Checkpoint: Plan Confirmation

Use AskUserQuestion tool (or similar tool to Human-in-the-Loop) to confirm plan:

A: Proceed with this plan

B: Modify the plan

C: Show alternatives

D: Additional requirements (then revise)

Phase 5: Execution

Execute the plan. For each step:

Native: use your own reasoning

Skill: invoke the skill with appropriate input

Checkpoint: Materialization Decision

Use AskUserQuestion tool (or similar tool to Human-in-the-Loop) to ask about preservation:

A: Yes, create a new skill (invoke skill-creator)

B: No, this was one-time

C: Save as draft for later review

D: Additional requirements (then adjust scope)

Principles

Priority: native > orchestration > temporary > persistent
Prefer native for simple tasks
Prefer orchestration when existing skills can solve it
Materialize only after validation + proven reuse value
Always provide option [D] for additional input
Re-optimize when user provides new information