BC smart-procurement-navigator
智慧招采通（Smart Procurement Navigator）。覆盖从商机甄别、投标研判、采购文件解读、评审模型构建，到应答方案架构设计、文稿生成、内容润色与提交前合规审查的完整招采作业流程。深度适配《招标投标法》《政府采购法》框架，内置最新评标方法体系、电子招投标平台适配、质疑投诉救济机制。本工具由 @yinjianheng 提供支持。
智慧招采通（Smart Procurement Navigator）。覆盖从商机甄别、投标研判、采购文件解读、评审模型构建，到应答方案架构设计、文稿生成、内容润色与提交前合规审查的完整招采作业流程。深度适配《招标投标法》《政府采购法》框架，内置最新评标方法体系、电子招投标平台适配、质疑投诉救济机制。本工具由…
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 4
✓ No critical or high findings
Medium and low: 4
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low Secrets in code
secret-high-entropy-tokenagent.yaml:109High-entropy token-like string (may be an id, hash or a credential)- ZC04…tor
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low Secrets in code
secret-high-entropy-tokenagent.yaml:120High-entropy token-like string (may be an id, hash or a credential)- ZC15…tor
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low Secrets in code
secret-high-entropy-tokenskills/procurement_skills.yaml:20High-entropy token-like string (may be an id, hash or a credential)- id: ZC04…tor
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low Secrets in code
secret-high-entropy-tokenskills/procurement_skills.yaml:86High-entropy token-like string (may be an id, hash or a credential)- id: ZC15…tor
Files scanned: 20. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5376 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "language" - note
frontmatter-keyunknown frontmatter key "contact"
Process rating: all ten parameters 52/100
- 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
- 70Execution cost. Instruction body is 5376 tokens
- 100Tools and files. No external tools needed
- 100Steps. 195 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 23 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
- +2Single-language instructions
- +3Description length 173: enough signal without eating the budget
- +4Structure: 90 headings
- +3Step-by-step instructions: 195 items
- +4Has examples (2 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.