BD bid-winner-company-profile
企业中标履历与业绩查询助手。当用户给出一个公司主体（公司名/简称/公司链接），想查中标履历、中标记录、业绩证明、履约记录时，必须使用此SKILL：某公司中过什么标、中标数量与总金额、逐年中标走势、中标地区分布、标王项目盘点（附公告原文链接）、同类业绩核验（投标材料业绩真伪）、资质申报业绩梳理、供应商履约记录审查，以及配套的企业画像、客户供应商生态、竞争格局与公开风险全景背调。基于全网招投标数据输出报告，每条业绩可追溯到公告原文。支持单公司深度报告与双公司对比。即使用户没有提到「履历」，只要想用中标记录核实一家公司的业绩与履约能力，都应使用本SKILL。注意边界：若用户给出一个具体的招标项目做该不该投的决策分析，使用 zlbx-bid-decision SKILL；若用户想主动挖掘商机/销售线索，使用 zlbx-opportunity-radar SKILL；若用户只是搜索/查询招中标公告数据，使用 zlbx-bidding SKILL。
企业中标履历与业绩查询助手。当用户给出一个公司主体（公司名/简称/公司链接），想查中标履历、中标记录、业绩证明、履约记录时，必须使用此SKILL：某公司中过什么标、中标数量与总金额、逐年中标走势、中标地区分布、标王项目盘点（附公告原文链接）、同类业绩核验（投标材料业绩真伪）、资质申报业绩梳理、供应商履约记录审查，以及…
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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Exfiltration
net-credential-usereferences/auto-register.md:215Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)**如果当前 api_key 来自 `$ZLBX_API_KEY`**：跳过 SID 流程，提示用户访问 `https://ai.zhiliaobiaoxun.com/?ch=s125` 手动登录充值。
quoted -
low Obfuscation
obf-base64-blobscripts/render_report.py:93Long base64-looking blob (quoted — discussed, not commanded)_LOGO_B64 = "iVBO…B5x
quoted -
low Secrets in code
secret-high-entropy-tokenscripts/render_report.py:93High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)_LOGO_B64 = "iVBO…B5x
quoted
Files scanned: 7. 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")
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 (web) that frontmatter does not declare
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1987 tokens
- 100Running it twice. No mutating operations
- low 13 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 424: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.