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BD scopeblind-red-team

Policy benchmarking runner for MCP security policies. Runs attack suites against protect-mcp policy packs, produces signed receipts and badges.

ClawHub Agent Skills author: TJF v0.1.1 MIT-0 2 files body ≈ 142 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentsInfrastructuretype 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
86/100
safety, quality, tests
Safety 60%
97
Quality 40%
69
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Risky intent intent-offensive-security skill-card.md:25
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - [ScopeBlind Red Team npm package](https://npmjs.com/package/@scopeblind/red-team) <br>
  • low Risky intent intent-offensive-security skill-card.md:26
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - [ScopeBlind Red Team documentation](https://scopeblind.com/docs/red-team) <br>
  • low Risky intent intent-offensive-security SKILL.md:20
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    # ScopeBlind Red Team — Policy Benchmarking

Files scanned: 2. 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 46/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 20When it triggers. No condition that starts the skill
  • 50Steps. 2 steps
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 142 tokens
  • 100Running it twice. No mutating operations

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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 143: enough signal without eating the budget
  • +4Structure: 4 headings
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This skill is a straightforward helper for running an external MCP policy benchmark CLI, with the main caution that it installs unpinned npm packages.
LLM: benign (high) · VirusTotal: · 29 May 2026