BD medical-device-bid-decision
医疗器械投标决策分析助手。当用户给出一个具体的医疗类招标项目（医疗设备/器械/耗材/检验试剂/医院信息化等），并希望进行投标决策分析时，必须使用此SKILL：该不该投、医院/卫健单位历史采购规律与品牌偏好分析、长期供应商（在位者）识别、竞争对手（同类器械投标人）预测、同品牌型号历史中标单价与建议报价、配置参数倾向与废标风险评估。基于全网招中标历史数据输出决策报告。即使用户没有提到「医疗」，只要涉及医院采购投标评估、设备标该不该投、器械报价参考等需求，都应使用本SKILL。
医疗器械投标决策分析助手。当用户给出一个具体的医疗类招标项目（医疗设备/器械/耗材/检验试剂/医院信息化等），并希望进行投标决策分析时，必须使用此SKILL：该不该投、医院/卫健单位历史采购规律与品牌偏好分析、长期供应商（在位者）识别、竞争对手（同类器械投标人）预测、同品牌型号历史中标单价与建议报价、配置参数倾向与废…
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=s76` 手动登录充值。
quoted -
low Obfuscation
obf-base64-blobscripts/render_report.py:71Long base64-looking blob (quoted — discussed, not commanded)_LOGO_B64 = "iVBO…B5x
quoted -
low Secrets in code
secret-high-entropy-tokenscripts/render_report.py:71High-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. 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 238: 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)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.