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BD 供应商与站点风险评分

对菜鸟物流供应商（承运商）和菜鸟驿站加盟商进行风险评分， 给出风险等级、核心问题诊断和建议措施。 基于行业标准KPI体系 + 菜鸟官方运营规则 + 供应商四维评估模型。 v1.7 新增：KPI量化计算公式详细版、行业基准对比表、权重调整权限边界、统一引用 GLOSSARY.md+INSUFFICIENCY-HANDLING.md+RULE-UPDATE-SOP.md。

ClawHub Agent Skills author: nic-yuan v1.0.1 MIT-0 3 files body ≈ 1 290 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

ReferenceLogistics and warehouseData and analyticsInfrastructuretype 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
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
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

The same skill appears in 1 more place: ClawHub

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 · 0

✓ No critical or high findings

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-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "data_sources"
  • note frontmatter-key unknown frontmatter key "reference"

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 (供应商与站点风险评分) differs from the folder (inter-control-02-risk-scoring)
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Execution cost. Instruction body is 1290 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)
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 185: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This appears to be a purpose-aligned assessment skill, but users should avoid pasting raw sensitive business or customer details into it.
LLM: benign (medium) · VirusTotal: · 29 May 2026