SKILLEMALL.ai

AC bid-rejection-decision-support

评标委员会(专家)侧否决/废标决策支持引擎。当用户提供"某投标人的具体响应情况 + 招标文件对应条款"并询问"该不该否决""应当否决还是澄清补正""帮我写评标报告用的否决理由"时触发。输出三类成果:① 是否构成否决的判断及法条/条款依据;② 应当否决与可澄清补正的区分判定(避免把可补正的形式瑕疵直接否掉,或把实质偏差误当可澄清);③ 可直接写入评标报告的规范化否决理由措辞。核心目标:评得准、否得有依据、经得起投诉复核、不踩纪律红线。区别于"废标风险雷达"(扫描招标文件提取风险条款)、"否决雷区体检"(历史案例雷区)、"招投标评标专家"(通用评标问答)。

ClawHub Agent Skills author: 一线评标专家 v1.0.0 MIT-0 8 files body ≈ 1 002 tokens Open the sourceclawhub.ai analyzed 2 d ago

评标委员会(专家)侧否决/废标决策支持引擎。当用户提供"某投标人的具体响应情况 + 招标文件对应条款"并询问"该不该否决""应当否决还是澄清补正""帮我写评标报告用的否决理由"时触发。输出三类成果:① 是否构成否决的判断及法条/条款依据;②…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
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")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1002 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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

  • +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
  • +5Description quotes 6 example trigger phrases
  • +3Description length 279: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This skill is a scoped procurement bid-rejection decision-support guide with disclosed knowledge-base lookup behavior and no hidden execution, persistence, or data access.
LLM: benign (high) · VirusTotal: · 25 Jul 2026