BD proposed-project-tracker
拟建项目跟踪与早期商机发现助手。当用户想查询拟建项目、在建工程立项信息或挖掘早期商机时，必须使用此SKILL：拟建项目查询（发改立项/审批公示阶段，提前6-18个月）、审批状态筛选（未审批/审批中/办结）、立项单位跟进、项目进展跟踪（是否进入招标）、以及配套的采购意向与临期续约商机扫描。给一个行业/地区即输出按价值排序的商机清单。即使用户没有提到「拟建」，只要涉及立项项目查询、还没招标的项目、早期项目线索等需求，都应使用本SKILL。
拟建项目跟踪与早期商机发现助手。当用户想查询拟建项目、在建工程立项信息或挖掘早期商机时，必须使用此SKILL：拟建项目查询（发改立项/审批公示阶段，提前6-18个月）、审批状态筛选（未审批/审批中/办结）、立项单位跟进、项目进展跟踪（是否进入招标）、以及配套的采购意向与临期续约商机扫描。给一个行业/地区即输出按价值排…
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=s98` 手动登录充值。
quoted -
low Obfuscation
obf-base64-blobscripts/render_report.py:64Long base64-looking blob (quoted — discussed, not commanded)_LOGO_B64 = "iVBO…B5x
quoted -
low Secrets in code
secret-high-entropy-tokenscripts/render_report.py:64High-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. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1748 tokens
- 100Running it twice. No mutating operations
- low 11 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 219: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 35 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.