Skill Review logo

Skill Review

Organization
runxhq
skill-review

Inspect, safely test, and assess one Runx skill package for capability, trust, and operator readiness. Use when deciding whether to adopt, improve, reject, install, or publish a skill; its evidence-only assess runner is available when native test evidence already exists.

Overview

Publisherrunxhq
Repositoryrunx
Skill nameskill-review
Stars
105
Forks
109
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by runxhq on GitHub. Read the source before you install it.

Installation

Install the Skill Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/runxhq/runx/tree/main/skills/skill-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/skill-review .claude/skills/skill-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skill Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skill Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill Review

Evaluate one skill from bounded evidence. The default runner passes a local package to native runx.skill.validate, which parses and inspects the exact package, runs read-only or planning harnesses with isolated receipts and no operator credentials, and forwards that evidence to the focused assessment runner. Execute-capable targets are inspected but never run automatically.

This skill does not mutate the package, publish a report, install a package, or manufacture missing evidence. Use assess directly when a caller already has a bounded evidence pack.

Procedure

  1. Resolve a workspace- or owning-skill-relative package path and capture its native capability and readiness envelope. Registry or marketplace material must first be installed or supplied as a bounded evidence pack; this skill does not fetch it implicitly.
  2. Run the native harness only when catalog execution is read or plan.
  3. Compare the documented capability with the inspected runner and catalog metadata in the evidence pack.
  4. Separate native harness results, provider readback, supplied assertions, and unverified claims.
  5. Check the happy path, refusal or stop path, authority boundary, artifact or effect, provenance, and recovery posture appropriate to the skill type.
  6. Return needs_more_evidence when the evidence cannot support a trust decision. Never upgrade a parse result or prose claim into execution proof.
  7. Recommend adopt, adopt_with_caveats, improve, or reject, naming the evidence and blocking gaps behind the decision.

Stop conditions

  • Refuse evidence containing secrets, raw credentials, private customer data, private inbox content, or provider dumps.
  • Do not recommend provider readiness without provider readback.
  • Do not recommend publication from private previews, placeholder hosts, dead links, or unrelated parent domains.
  • Return needs_more_evidence when no native inspection, harness, receipt, or equivalent bounded source evidence is supplied.
  • Reject capabilities that cannot be bounded, audited, or assigned a truthful terminal state.

Output

  • capability_profile: bounded capability, execution shape, and claimed effect.
  • trust_assessment: evidence tier, caveats, and unsupported claims.
  • test_matrix: passed, failed, skipped, and still-required checks.
  • recommendation_report: decision, rationale, blockers, and next action.

Inputs

  • skill (required): local package path for the default runner; any stable reference may label an evidence-only assess run.
  • evidence_pack (optional for the default runner, required for assess): inspection, harness, receipts, docs, or source evidence. References are preferable to copied private bodies.
  • objective (optional): decision the evaluation should support.
  • test_constraints (optional): time, environment, or safety limits.

Agent task contracts

skill-review-assess

Assess only the supplied evidence for one skill. Separate declared capability, native inspection, harness evidence, provider readback, supplied assertions, and unverified claims. Check the happy path, stop or refusal path, authority, artifact or effect, provenance, and recovery posture appropriate to the capability. Return needs_more_evidence when the packet cannot support a decision. Never infer provider readiness from a parse result, local supplied answer, or prose claim. Recommend adopt, adopt_with_caveats, improve, or reject with concrete blockers.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Review AI skill do?

Inspect, safely test, and assess one Runx skill package for capability, trust, and operator readiness. Use when deciding whether to adopt, improve, reject, install, or publish a skill; its evidence-only assess runner is available when native test evidence already exists.

Why use Skill Review on TypingMind?

Because you install it once and use it with any model. Skill Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skill Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/skill-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skill Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skill Review?

As many as you like. As long as a model supports skills, you can use Skill Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skill Review AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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