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全网招中标数据查询与分析助手。当用户涉及以下任何场景时，必须使用此SKILL：查询招标/中标公告、搜索标讯、查找临期/即将到期项目、商机预测、推荐潜在投标供应商、分析公司主营业务/历史中标、查询公司上下游合作客户与供应商、分析竞争对手/竞对企业、查询Top采购单位/Top中标单位/Top中标品牌、招中标数据统计分析（按月/季/年/省份/品牌等维度）、查询品牌型号历史中标单价/价格趋势、市场分析/行业分析/采购寻源/渠道拓展等采购与投标相关场景。即使用户没有提到「知了标讯」，只要涉及招投标、采购、中标、供应商、竞争对手等关键词，都应使用本SKILL。

ClawHub Agent Skills v2.1.4 7 files body ≈ 3 414 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureProcurementtype 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
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
D
43/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: tender-search (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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration net-credential-use references/auto-register.md:195
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    **如果当前 api_key 来自 `$ZLBX_API_KEY`**：跳过 SID 流程，提示用户访问 `https://ai.zhiliaobiaoxun.com/?ch=s21` 手动登录充值。
    quoted

Files scanned: 7. 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 43/100

  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3414 tokens
  • low 15 top-level sections: this looks like several domains in one skill

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 277: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (17 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill has a real tender-search purpose, but it also performs account setup with a hardware-derived device identifier, stores an API key locally, and can add vendor-controlled promotional or update text.
LLM: suspicious (high)