SkillHub

skill-evolver

v0.1.0

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 si...

Sourced from ClawHub, Authored by testlbin

Installation

Please help me install the skill `skill-evolver` from SkillHub official store. npx skills add testlbin/skill-evolver

Skill Evolver

Solve first. Materialize later.

Workflow

Phase 0: Setup Output Directory

Create a timestamped output directory for this session:

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

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/<created-directory>

Phase 1: Intent Analysis

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

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