SKILLEMALL.ai

AD enterprise-bidding-decision-agent

企业投标决策智能助手(知了标讯官方)。当用户给出一个具体的招标项目(公告链接/项目标题/招标文件),并希望进行投标决策相关分析时,必须使用此SKILL:该不该投/值不值得投、投标决策、标前分析、竞争对手预测(谁会来投标)、中标概率评估、报价参考/建议报价、采购方倾向分析、废标风险评估。基于知了标讯全网招中标历史数据输出带结论的决策报告。即使用户没有提到「投标决策」,只要涉及某个具体标该不该投、投标评估、竞争分析、报价参考等需求,都应使用本SKILL。

ClawHub Agent Skills author: 知了标讯 AI 开放平台 v1.0.4 MIT-0 7 files body ≈ 1 613 tokens Open the sourceclawhub.ai analyzed 2 d ago

企业投标决策智能助手(知了标讯官方)。当用户给出一个具体的招标项目(公告链接/项目标题/招标文件),并希望进行投标决策相关分析时,必须使用此SKILL:该不该投/值不值得投、投标决策、标前分析、竞争对手预测(谁会来投标)、中标概率评估、报价参考/建议报价、采购方倾向分析、废标风险评估。基于知了标讯全网招中标历史数据输…

As a process D 46/100 · 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%
100
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
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 · 0

✓ No critical or high findings

Files scanned: 1. 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 46/100

  • 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
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1613 tokens
  • 100Running it twice. No mutating operations
  • low 10 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 227: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill appears aimed at bidding analysis, but it also handles signup, device fingerprinting, local credential/report storage, and signed share links in ways users should review carefully.
LLM: suspicious (high) · 8 Sept 2026