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AC miao-vision

Create a self-contained Miao Vision artifact when the user explicitly invokes $miao-vision and supplies an article URL or local Markdown/text for an infographic, or local Markdown/text and optional CSV, TSV, XLSX, or JSON data for an HTML/PDF report, single-page data poster, or browser deck. Also validate a user-supplied Miao Vision report or deck spec. Do not trigger from isolated keywords such as chart, report, dashboard, slides, infographic, or PDF.

ClawHub Agent Skills v0.7.0 16 files · 2 scripts body ≈ 3 008 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Create a self-contained Miao Vision artifact when the user explicitly invokes $miao-vision and supplies an article URL or local Markdown/text for an…

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorPDFExcelData and analyticstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
56/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 16. 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 56/100

    • 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 (miao-vision) differs from the folder (miao-vision-skill)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 38 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 3008 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

    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
    • -33 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 456: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The skill appears to be a legitimate local report and deck generator, but its CLI discovery and installation paths can execute and persist binaries without strong provenance controls.
    LLM: suspicious (high)