SKILLEMALL.ai

AD xinchuang-it-procurement-analyzer

信创与 IT 信息化采招数据分析助手。当用户涉及以下任何场景时,必须使用此SKILL:搜索信创/国产化/IT信息化招标中标公告(服务器、数据库、操作系统、云、数据中心、网络安全、软件等)、查询某IT品牌/型号的中标占有率与历史单价、分析IT集成商的主营业务与竞争对手、查询某品类的Top采购单位/Top中标单位、推荐潜在投标供应商、数字政府与信创项目趋势分析、IT采购寻源/渠道拓展等场景。即使用户没有提到「信创」,只要涉及IT、信息化、国产化、服务器、数据库、软件、云、网络安全等采购与中标需求,都应使用本SKILL。

ClawHub Agent Skills author: 知了标讯 AI 开放平台 v1.0.5 MIT-0 7 files body ≈ 3 431 tokens Open the sourceclawhub.ai analyzed 4 d ago

信创与 IT…

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

AnalyzerProcurementtype 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=s57` 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 260: 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 coherent procurement-search purpose, but it also handles device-based account registration, local key storage, recharge login links, and broad promotional routing that users should review carefully before installing.
LLM: suspicious (high) · 8 Sept 2026