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AB markdown-image-enricher

Read a plain Markdown file (e.g. 260321_openclawConfig.md), generate header and section images using the default OpenClaw image model and API key from ~/openclaw/.env, create a new *_img.md copy with embedded PNGs, and move all generated images into an img/ folder next to the original file.

ClawHub Agent Skills author: Jeff Yang v1.0.1 MIT-0 2 files body ≈ 3 239 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 65/100 · Nearly there — weak spots: result and completion, consistency

IntegrationInfrastructuretype 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
B
65/100
Nearly there
Result and completion w 14
0
Consistency w 8
40
Tools and files w 18
60
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: 2. 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 65/100

    • 0Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (markdown-image-enricher) differs from the folder (blog-image-enricher)
    • 60Tools and files. Uses tools (bash, read) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 109 steps, 2 vague phrases
    • 100Failures and branches. 7 branches, has a failure section
    • 100Execution cost. Instruction body is 3239 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 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
    • +2Single-language instructions
    • +3Description length 291: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 109 items
    • +4Has examples (1 code blocks)
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This skill mostly does what it claims, but it needs review because it tells the agent to read local API-key configuration directly.
    LLM: suspicious (high) · 8 Aug 2026