BD Supplier Bid Assistant - Award Data Analysis
乙方投标助手，面向投标方（乙方）的全网招中标数据分析助手。当用户涉及以下场景时必须使用此SKILL：查询招标/中标公告、分析竞争对手中标情况、寻找潜在合作与供应商、评估某类项目的中标价格区间、乙方视角的投标决策支持。作为投标方的日常查标工具，必须以清晰的表格或图表输出结论。
As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
This is a copy of a skill from another catalog; the rating counts the canonical one: Supplier Bid Assistant - Award Data Analysis (ClawHub)
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Exfiltration
net-credential-usereferences/auto-register.md:195Credential 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=s24` 手动登录充值。
quoted
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 39/100
- 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
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (Supplier Bid Assistant - Award Data Analysis) differs from the folder (bidding-yifangbao)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 23 steps
- 100Execution cost. Instruction body is 3414 tokens
- low 15 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
- +1No license
- +2Single-language instructions
- +3Description length 137: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (17 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.
External checks
ClawHub: suspicious
The skill mostly performs bid-data analysis, but it also registers accounts, fingerprints the device, stores credentials locally, and forces promotional referrals in ways users should review before installing.
LLM: suspicious (high)