SKILLEMALL.ai

BF bid-carrot-pit-tenderer

招采文件萝卜坑识别专家(招标人版)· 招标文件合规体检与整改。招标人/采购代理机构侧「招标文件合规自检(发标前体检)」独立技能:扫描整份招标文件全文,用多维信号标尺预判哪些条款容易被投标人质疑/异议/投诉,输出风险等级+法条方向+可落地整改(改写)建议+异议抗辩预案+合理性论证备查。与「招采萝卜坑识别专家(投标人版)bid-carrot-pit」立场相反、共享9类信号标尺与法条表、解耦可单独触发;含文档类型预检、内部矛盾自检、分章节定位、无脚本降级、合理性评估(真违规/表述不当/合理门槛三分类)、法条核验声明、两法硬隔离、不教唆设坑护栏。

ClawHub Agent Skills author: 一线评标专家 v1.0.0 MIT-0 9 files body ≈ 3 175 tokens Open the sourceclawhub.ai analyzed 2 d ago

招采文件萝卜坑识别专家(招标人版)·…

As a process F 28/100 · Will not run — References files that are not bundled: scripts/split_sections.py, references/compliance-signals.md, references/legal-anchors.md

ProcedureProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
56
Run on models
none yet
Process rating
F
28/100
Will not run
References files that are not bundled: scripts/split_sections.py, references/compliance-signals.md, references/legal-anchors.md
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/split_sections.py
  • warning missing-ref reference to a missing file: references/compliance-signals.md
  • warning missing-ref reference to a missing file: references/legal-anchors.md
  • warning missing-ref reference to a missing file: templates/compliance-selfcheck-report.md
  • warning missing-ref reference to a missing file: scripts/batch_scan.py
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 28/100

Will not run. References files that are not bundled: scripts/split_sections.py, references/compliance-signals.md, references/legal-anchors.md
  • 0Tools and files. 5 referenced file(s) missing: scripts/split_sections.py, references/compliance-signals.md, references/legal-anchors.md
  • 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. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (bid-carrot-pit-tenderer) differs from the folder (bid-carrot-pit-tenderer-1-0-0)
  • 100Steps. 56 steps
  • 100Execution cost. Instruction body is 3175 tokens

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
  • -239 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 272: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.

External checks

ClawHub: clean
This skill is a procurement-document compliance checker with disclosed, purpose-aligned local parsing scripts and no evidence of hidden data access, exfiltration, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 Jul 2026