AD gov-procurement-doc-assistant
政府采购 / 企业招标文件解读与深度分析助手。当用户上传招标文件（PDF / DOCX / 图片）或说"解读这份标书 / 拆解招标文件 / 标书解读 / 算价格分 / 提取废标条款 / 出风险清单 / 出Excel清单 / 生成投标框架"时触发。默认输出 6 大模块结构化解读（项目信息 / 资格性 / 符合性 / 商务红黄预警 / 技术要求 / 废标项）；当用户进一步要求"价格分测算 / 出 Excel / 投标框架 / 投标建议"时，额外生成含价格分情景测算的 6-Sheet Excel 与投标研判摘要。基于公开工商信息 / 行业惯例给出红黄预警，疑似违规 / 歧视性 / 差别待遇条款须调用 IMA 知识库（knowledge_base_id: 7463496212028405）引用具体法条（政府采购法、87/94/74 号令、财库〔2020〕46 号、国办发〔2025〕34 号）做合规溯源。区别于 bid-compliance-due-diligence（企业黑历史尽调）、bid-lightning-rod（废标雷区扫描）、bid-related-party-expert（关联关系识别），本技能专注"招标文件本身"的拆解与风险标注。不适用于合同审查、投标文件代写、保证中标定价、围标串标建议。
政府采购 / 企业招标文件解读与深度分析助手。当用户上传招标文件（PDF / DOCX / 图片）或说"解读这份标书 / 拆解招标文件 / 标书解读 / 算价格分 / 提取废标条款 / 出风险清单 / 出Excel清单 / 生成投标框架"时触发。默认输出 6 大模块结构化解读（项目信息 / 资格性 / 符合性 /…
As a process D 46/100 · Unfinished process — 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.
- 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-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 78 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3140 tokens
- 100Running it twice. No mutating operations
- low 12 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -226 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 556: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 78 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.