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B benderstack-integration

Comprehensive guide and rules for an AI agent to interact with the BenderStack API, including the 5-layer Write Operation Security.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 948 tokens open source ↗ analyzed 8 h ago

Comprehensive guide and rules for an AI agent to interact with the BenderStack API, including the 5-layer Write Operation Security.

IntegrationAI and agentstype 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
B
87/100
Overall score
Safety 60%
100
Quality 40%
68
Tests bonus
0

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): it is the main trigger signal.
  2. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  3. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Lint

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process maturity 59/100

  • 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
  • 30Running it twice. 5 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 948 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 131: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 30 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.