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BC trust-checker

A protocol-layer trust verification skill for AI agents. Before you read, install, or transact — check first. Protects against prompt injection, malicious skills, and unverified agents. Free version includes core protocol. Pro version ($29) adds active real-time scanner, expanded attack pattern library, and confidence scoring — upgrade at https://edvisage.gumroad.com/l/iwppa

ClawHub Agent Skills author: Edvisage Global v1.0.2 MIT-0 4 files body ≈ 2 824 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

AnalyzerAI 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
Technical rating
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
67
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 2

✓ No critical or high findings

Medium and low: 2

✓ Guard found no suspicious behaviour. 2 matches are attack strings quoted in this security skill's own documentation.

Files scanned: 4. 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")
  • note frontmatter-key unknown frontmatter key "requires"
  • note frontmatter-key unknown frontmatter key "permissions"

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (trust-checker) differs from the folder (edvisage-trust-checker)
  • 70Failures and branches. 4 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 34 steps
  • 100Execution cost. Instruction body is 2824 tokens
  • 100Progress reporting. Reports progress

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
  • +2Single-language instructions
  • +3Description length 377: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 34 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This is a documentation-only trust-checking protocol with disclosed memory logging and no scripts, network calls, hidden install behavior, or destructive actions.
LLM: benign (high) · VirusTotal: · 29 May 2026