SKILLEMALL.ai

AD smart-tender-procurement-search

招标采购信息检索服务,按关键词、地区、金额、时间、行业等多维度检索全网招标公告与采购信息,支持高级逻辑(关键词分组、排除词)、获取标讯完整详情、查询临期周期性项目。当用户需要查招标公告、找采购信息、检索标讯、获取标书详情、跟踪某地区或行业项目动态时,必须使用此SKILL。

ClawHub Agent Skills author: 知了标讯 AI 开放平台 v1.0.4 MIT-0 7 files body ≈ 3 431 tokens Open the sourceclawhub.ai analyzed 2 d ago

招标采购信息检索服务,按关键词、地区、金额、时间、行业等多维度检索全网招标公告与采购信息,支持高级逻辑(关键词分组、排除词)、获取标讯完整详情、查询临期周期性项目。当用户需要查招标公告、找采购信息、检索标讯、获取标书详情、跟踪某地区或行业项目动态时,必须使用此SKILL。

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

IntegrationProcurementtype and topics are labelled automatically from the skill text
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

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:215
    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=s132` 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 3431 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 136: 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
This appears to be a real procurement-search skill, but it needs Review because it collects a device fingerprint for signup, stores API credentials locally, and forces promotional referral text into answers.
LLM: suspicious (high) · 8 Sept 2026