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BF image-gen

Generate images using multiple AI models — Midjourney (via Legnext.ai), Flux, SDXL, Nano Banana (Gemini), and more via fal.ai. Automatically picks the best model based on user intent, or lets the user specify one explicitly.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 3 529 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate images using multiple AI models — Midjourney (via Legnext.ai), Flux, SDXL, Nano Banana (Gemini), and more via fal.ai. Automatically picks the best…

As a process F 40/100 · Will not run — References files that are not bundled: imageUrls[0], imageUrls[1], imageUrls[2]

GeneratorAI and agentsWriting and documentsSales and CRMtype 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
82/100
safety, quality, tests
Safety 60%
98
Quality 40%
59
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: imageUrls[0], imageUrls[1], imageUrls[2]
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv CONTRIBUTING.md:28
    Reads a .env file
    cp .env.example .env
  • low Secrets in code secret-high-entropy-token package-lock.json:34
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…SHP+Ptfk…NUA==",
    detector

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: imageUrls[0]
  • warning missing-ref reference to a missing file: imageUrls[1]
  • warning missing-ref reference to a missing file: imageUrls[2]
  • warning missing-ref reference to a missing file: imageUrls[3]
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: imageUrls[0], imageUrls[1], imageUrls[2]
  • 0Tools and files. 4 referenced file(s) missing: imageUrls[0], imageUrls[1], imageUrls[2]
  • 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
  • 30Running it twice. 17 mutating operations with no state check
  • 40Consistency. Frontmatter name (image-gen) differs from the folder (image-q)
  • 65Failures and branches. 3 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 47 steps
  • 100Execution cost. Instruction body is 3529 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 224: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 47 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.