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BC clawra-selfie

Generate AI images using MiniMax or fal.ai (Grok Imagine) and send to messaging channels via OpenClaw

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 3 451 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate AI images using MiniMax or fal.ai (Grok Imagine) and send to messaging channels via OpenClaw

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorTelegramWhatsAppSlackDiscordAI 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
B
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: skill-library-mcp

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash(curl:*)
      allowed-tools: Bash(npm:*) Bash(npx:*) Bash(openclaw:*) Bash(curl:*) Read Write WebFetch

    Files scanned: 3. 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

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 15 mutating operations with no state check
    • 40Consistency. Frontmatter name (clawra-selfie) differs from the folder (skill)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 34 steps, 2 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Execution cost. Instruction body is 3451 tokens
    • low 12 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)
    • +3Description length 101: 120–800 characters recommended
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -2localhost URLs: will not work for another user
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 34 items
    • +3Output format is stated explicitly
    • +4Has examples (19 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.