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BB verify-matrix-device

Verify and cross-sign the active Matrix device for one OpenClaw-managed account. Use when a user needs to repair trust for an OpenClaw Matrix device, confirm the current device is self-signed, or recover cross-signing with a recovery key.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 448 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Verify and cross-sign the active Matrix device for one OpenClaw-managed account.

As a process B 65/100 · Nearly there — weak spots: result and completion, running it twice

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
88/100
safety, quality, tests
Safety 60%
96
Quality 40%
77
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Failures and branches w 10
55
the three weakest of ten parameters · all ten

How to improve

    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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token package-lock.json:24
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…3tR+NJ7k…0Og==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:45
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…DmC+EJUz…RN4+0gEx…AvA==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:144
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…Oqj+pGdF…7bb+Gsj3…BVw+Z+tSW4…twQ==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:188
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…82c/W+GYQ0…Z9z+piSG…3Kw==",
      detector

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 448 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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 238: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 15 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.