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BD Lead Processing Agent

外贸 B2B 线索自动化清洗与分发代理。

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 323 tokens Open the sourcegithub.com↗ analyzed 30 h ago

外贸 B2B 线索自动化清洗与分发代理。

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

ProcedureAI 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
80/100
safety, quality, tests
Safety 60%
98
Quality 40%
52
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token config.json:4
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "app_secret": "qlyS…aD0"
    quoted
  • low Secrets in code secret-high-entropy-token index.js:5
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const FEISHU_APP_SECRET = process.env.FEISHU_APP_SECRET || 'qlyS…aD0';
    quoted

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-short description under 40 chars: too little signal for triggering
  • 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
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (Lead Processing Agent) differs from the folder (lead-processor)
  • 100Tools and files. No external tools needed
  • 100Steps. 25 steps
  • 100Execution cost. Instruction body is 323 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 20: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 25 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.