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BC github-readme-generator

Automatically generates comprehensive README files from GitHub repositories with installation, API docs, and usage examples.

modbender/skill-library-mcp Agent Skills author: modbender MIT 18 files body ≈ 805 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Automatically generates comprehensive README files from GitHub repositories with installation, API docs, and usage examples.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationGitHubWriting and documentstype 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%
93
Quality 40%
65
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • low Exfiltration read-dotenv assets/examples/complex-api/README.md:54
    Reads a .env file (test fixture / example file)
    cp .env.example .env
    fixture
  • low Secrets in code secret-high-entropy-token package-lock.json:60
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…DTb+yLyB…Pqd/v9yr…VM1/kunVkw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:69
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…k3S+5pN2…QV8/vgJg…NOK/EljA…HRQ==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:167
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…DTb+yLyB…Pqd/v9yr…VM1/kunVkw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:313
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…G77+p/MqW1JeXXgL++6umi…XAN+0HEj…Rig==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:352
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…DTb+yLyB…Pqd/v9yr…VM1/kunVkw==",
    detector
  • low Exfiltration read-dotenv scripts/readme-generator.js:191
    Reads a .env file (quoted — discussed, not commanded)
    lines.push("cp .env.example .env");
    quoted

Files scanned: 18. 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")

Process rating: all ten parameters 55/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (github-readme-generator) differs from the folder (neo-github-readme-generator)
  • 50When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Execution cost. Instruction body is 805 tokens

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
  • +4No input/output examples
  • -34 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 124: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 44 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.