SKILLEMALL.ai

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国央企招投标信息监控与研判技能。自动采集多平台公告,按资质规则比对可投性(可投/不可投/需确认),生成带研判简报并多通道推送。v1.5 起新增:资质匹配度评分(0-100)、投标日历与开标倒计时、金额/地区/行业多维筛选、零代码规则编辑器、诊断中心、示例模式、集中FAQ、统一命令入口;v2.0 本地AI增强(招标文件AI速读+风险条款识别,零云成本);v2.5 投标策略建议;v3.0 本地开放API+签名webhook;v3.5 合规红线引擎(31条法定废标红线本地扫描、交标前自查清单、多主体串标隔离,零API成本);v2.7 报价闸门(品类清算价地板、规模换算线性、单位单价、毛利红线,任一道不过即禁止输出结论金额)。★v2.8 覆盖率重写:修复只读前9000字符导致200页标书95%未读的缺陷,改为结构化分片全覆盖并强制标注覆盖率;修复PDF/DOCX扫描静默失效(此前规则命中恒为0);新增评分点逐项映射与分值守恒校验、待补材料清单(高危红标签字盖章页缺件)、零响应检测(空洞满足/待补占位/跨项目残留/数字型空洞),无AI key也能出完整简报。触发词:抓招投标、今日哪些能投、标讯监控、标书研判、招投标公告、央企招标、投标资质匹配、AI读标书、投标建议、这份标会不会被废、废标风险、交标前检查、合规红线扫描、多主体投标、会不会被认定串标、报价闸门、这个标报多少、报价复核、出价前检查、这个标值不值得投、报价会不会被废、待补材料清单、还差什么材料、零响应检测、评分点、评分表、评分办法、这个分怎么拿、漏项、丢分、扣分项、待补、待提供、准备什么材料、递交清单、应答表、偏离表、评分台账、评分项跟踪、覆盖矩阵、哪些评分项没写、哪条要求没响应。

ClawHub Agent Skills author: 419597334-sudo v2.9.1 MIT-0 52 files · 34 scripts body ≈ 3 838 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

国央企招投标信息监控与研判技能。自动采集多平台公告,按资质规则比对可投性(可投/不可投/需确认),生成带研判简报并多通道推送。v1.5 起新增:资质匹配度评分(0-100)、投标日历与开标倒计时、金额/地区/行业多维筛选、零代码规则编辑器、诊断中心、示例模式、集中FAQ、统一命令入口;v2.0…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationWordExcelProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 52. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 48/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 53 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3838 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
  • -2localhost URLs: will not work for another user
  • -213 emoji in the instructions: noise for the model
  • -37 of 26 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 727: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
The skill mostly fits its bid-monitoring purpose, but it needs Review because remote bid data can be injected into local HTML reports and the local rule editor can be changed by cross-site browser requests.
LLM: suspicious (high) · 6 Oct 2026