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Skill Engineer
by
Chunhua Liao
· GitHub ↗
· v3.2.0
907
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Install in OpenClaw
/install skill-engineer
Description
Design, test, review, and maintain agent skills for OpenClaw systems using multi-agent iterative refinement. Orchestrates Designer, Reviewer, and Tester suba...
Usage Guidance
This skill mostly does what it says, but it asks agents to: (1) query your vector memory (session history/notes), (2) read OpenClaw config and skill files in your home/workspace, and (3) regenerate README and push changes to GitHub. Before installing or enabling autonomous use:
- Review and run the included scripts locally yourself (check-completeness.sh, validate-scorecard.sh, validate-trigger.sh, quality-score.py) to see what they do and to confirm there are no unexpected network calls.
- Restrict or disable autonomous push-to-GitHub behavior: require manual approval for any git commits/pushes or run the README-sync step locally.
- Be deliberate about enabling vector memory access (memory_search) because it exposes session history/notes; if that data is sensitive, keep memory.enabled disabled or limit the skill's permissions.
- Ensure the dependent deepwiki skill is from a trusted source before using it.
If you want higher assurance, run the skill in a sandboxed repo/environment first and require human approval before giving it repository write/push rights or access to session memory.
Capability Analysis
Type: OpenClaw Skill
Name: skill-engineer
Version: 3.2.0
The skill-engineer bundle is a highly structured framework for the iterative development and quality assurance of OpenClaw agent skills. It implements a sophisticated multi-agent orchestration pattern (Designer, Reviewer, Tester) and includes several utility scripts (e.g., quality-score.py, validate-trigger.sh) to provide deterministic validation of skill artifacts. The bundle is notable for its strong emphasis on security and best practices, featuring explicit OPSEC rubrics in reviewer-rubric.md and designer-guide.md designed to prevent the accidental inclusion of secrets or internal organizational data. All high-risk capabilities, such as subagent spawning and file system manipulation, are strictly aligned with the stated purpose of skill engineering and are governed by rigorous quality gates.
Capability Assessment
Purpose & Capability
The name/description (design, review, test skills) align with the included materials: detailed SKILL.md, reviewer/tester/designer guides, and deterministic validation scripts. The declared non-code registry metadata (no env vars/binaries) matches the instruction-only install model; mandatory dependencies listed in SKILL.md (deepwiki skill, vector memory DB) are coherent for an orchestrator that needs current API behavior and session history.
Instruction Scope
SKILL.md instructs querying the agent's vector memory (memory_search), inspecting local OpenClaw files/paths (e.g., ~/.openclaw/skills/deepwiki/ and openclaw.json) and contains a README sync / push-to-GitHub step. Those actions reach beyond the skill's own files and ask for access to session history, local config and repository operations. While plausible for a skill-engineer, they are significant side-effects and broaden the trust surface.
Install Mechanism
No install spec or remote downloads; this is instruction-only with packaged reference docs and local validation scripts. That's low-install risk — nothing is fetched from external URLs or extracted. The provided scripts are local deterministic tools (bash/python) that operate on repository files.
Credentials
Registry metadata requests no environment variables or credentials. However, SKILL.md requires the vector memory feature and the deepwiki skill and tells the agent to inspect openclaw.json and user skill directories. This does not request new secrets, but it implies access to potentially sensitive session history and local configuration; that access is plausible for the role but should be intentionally granted and audited.
Persistence & Privilege
always:false and normal autonomous invocation are set (not elevated). But the workflow explicitly includes a README sync that regenerates README from the implementation and a 'Push to GitHub' step. That implies write/commit and remote push privileges over repositories. The package itself doesn't include automated push code, but the documented workflow expects the orchestrator to perform repo-side changes — a capability that increases impact and should require explicit authorization and careful scoping.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install skill-engineer - After installation, invoke the skill by name or use
/skill-engineer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v3.2.0
v3.2.0: Skill taxonomy (Capability Uplift vs Encoded Preference), mandatory dependencies (deepwiki + vector memory), new scripts (quality-score.py, validate-trigger.sh), Configuration section
v3.1.0
v3.1.0: Mandatory skill naming step in Designer workflow — present 3-5 candidates to user before writing artifacts. Naming criteria table, step-by-step process, worked example. Removed redundant Separation of Concerns section.
v3.0.1
Version 3.0.1
- No file changes detected for this release.
- No modifications to SKILL.md or other artifacts.
- Functionality and documentation remain unchanged from version 3.0.0.
v3.0.0
Skill-engineer v3.0.0 introduces a comprehensive multi-agent framework for quality-gated skill development, leveraging separation of concerns and iterative refinement.
- Adds a three-role architecture (Designer, Reviewer, Tester) for designing, reviewing, and validating agent skills.
- Clearly defines scope, boundaries, and success criteria for skill lifecycle management.
- Details orchestrator responsibilities and strictly enforces that only subagents create or evaluate skill content.
- Sets a maximum of three development iterations, with explicit failure and user notification protocols if quality gates are not met.
- Provides example requirements gathering templates and outlines both recommended (role-based) and advanced (parallel session) subagent spawning mechanisms.
- Excludes release, deployment, tracking, and infrastructure management from this skill’s responsibilities.
Metadata
Frequently Asked Questions
What is Skill Engineer?
Design, test, review, and maintain agent skills for OpenClaw systems using multi-agent iterative refinement. Orchestrates Designer, Reviewer, and Tester suba... It is an AI Agent Skill for Claude Code / OpenClaw, with 907 downloads so far.
How do I install Skill Engineer?
Run "/install skill-engineer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Skill Engineer free?
Yes, Skill Engineer is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Skill Engineer support?
Skill Engineer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Skill Engineer?
It is built and maintained by Chunhua Liao (@chunhualiao); the current version is v3.2.0.
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