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BD quantum-messenger-im

帮助中国电信同人及客户实现通过量子密信调用 OpenClaw 机器人。支持文本、图片、附件及状态栏反馈。

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 83 tokens Open the sourcegithub.com↗ analyzed 2 d ago

帮助中国电信同人及客户实现通过量子密信调用 OpenClaw 机器人。支持文本、图片、附件及状态栏反馈。

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting 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%
96
Quality 40%
62
Run on models
none yet
Process rating
D
49/100
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Dangerous commands cmd-background-process README.md:46
    Starts a background / autostarted process
    nohup node scripts/listener.mjs > listener.log 2>&1 &
  • low Exfiltration exfil-secret-in-url scripts/listener.mjs:50
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    const uploadUrlStr = `http://imtwo.zdxlz.com/im-external/v1/webhook/upload-attachment?key=…&type=…
    placeholder
  • low Exfiltration exfil-secret-in-url scripts/send.mjs:18
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    const WEBHOOK_URL = `http://imtwo.zdxlz.com/im-external/v1/webhook/send?key=…
    placeholder
  • low Exfiltration exfil-secret-in-url scripts/send.mjs:24
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    const UPLOAD_URL = `http://imtwo.zdxlz.com/im-external/v1/webhook/upload-attachment?key=…&type=…
    placeholder

Files scanned: 5. 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 49/100

  • 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
  • 40Consistency. Frontmatter name (quantum-messenger-im) differs from the folder (quantum-messenger)
  • 100Tools and files. No external tools needed
  • 100Steps. 7 steps
  • 100Execution cost. Instruction body is 83 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)
  • +3Description length 52: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 3 headings
  • +3Step-by-step instructions: 7 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.