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AC clawdhub-reskill-usage

Teaches AI agents how to use reskill — a Git-based package manager for AI agent skills. Covers CLI commands, install formats, configuration, publishing, and common workflows.

ClawHub Agent Skills author: Kris v0.1.1 1 file body ≈ 3 915 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedureGitHubGitLabAWSAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration net-credential-use SKILL.md:374
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      git config --global url."https://gitlab-ci-token:${CI_JOB_TOKEN}@gitlab.company.com/".insteadOf "https://gitlab.company.com/"
      quoted

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (clawdhub-reskill-usage) differs from the folder (rush-reskill-usage)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 16 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 3915 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (10 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 174: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a transparent usage guide for the reskill skill package manager; its risks are normal package-manager and credential-handling risks rather than hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: benign · 28 May 2026