SKILLEMALL.ai

BF ai-bidding-strategy-advisor

AI投标策略顾问。当用户给出一个具体的招标项目并希望制定投标策略时,必须使用此SKILL:投标策略制定、建议报价带/报价策略(基于历史成交价)、竞争格局分析与竞争对手预测、采购方采购规律与预算水平画像、投标决策(该不该投)、中标概率评估、风险清单与行动建议。基于全网招中标历史数据输出策略报告。即使用户没有提到「策略」,只要涉及怎么投、怎么报价、怎么打赢某个标等需求,都应使用本SKILL。

ClawHub Agent Skills author: 知了标讯 AI 开放平台 v1.0.7 MIT-0 7 files body ≈ 1 613 tokens Open the sourceclawhub.ai analyzed 2 d ago

AI投标策略顾问。当用户给出一个具体的招标项目并希望制定投标策略时,必须使用此SKILL:投标策略制定、建议报价带/报价策略(基于历史成交价)、竞争格局分析与竞争对手预测、采购方采购规律与预算水平画像、投标决策(该不该投)、中标概率评估、风险清单与行动建议。基于全网招中标历史数据输出策略报告。即使用户没有提到「策略」…

As a process F 35/100 · Will not run — References files that are not bundled: scripts/render_report.py

IntegrationProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/render_report.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 0. 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")
  • warning missing-ref reference to a missing file: scripts/render_report.py

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/render_report.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/render_report.py
  • 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
  • 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 195: 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)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
This skill is mostly aligned with bid-analysis work, but it needs Review because it persists an API key locally, transmits device-identifying registration data, and injects mandatory commercial links into generated reports.
LLM: suspicious (medium) · 8 Sept 2026