BF kepler-agent-search
支持 Bing/Baidu 通用搜索、知乎中文问答社区、小红书生活方式社区、GitHub代码仓库、 arXiv学术论文、199it互联网数据、东方财富财经资讯、智联招聘、前程无忧等多源搜索【按需持续增加】。 从多个平台搜索内容，实现统一搜索和内容提取，帮助AI Agent获取全网信息。 只要涉及获取网络信息——无论是搜索资讯、调研主题、查找资料、提取文章、多源对比—— 都应立即调用。即使用户未明确说"搜索"，只要需要网络信息支撑，自动使用本skill。
支持 Bing/Baidu 通用搜索、知乎中文问答社区、小红书生活方式社区、GitHub代码仓库、 arXiv学术论文、199it互联网数据、东方财富财经资讯、智联招聘、前程无忧等多源搜索【按需持续增加】。 从多个平台搜索内容，实现统一搜索和内容提取，帮助AI Agent获取全网信息。…
As a process F 34/100 · Will not run — References files that are not bundled: 链接, URL
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 10. 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") - warning
missing-refreference to a missing file: 链接 - warning
missing-refreference to a missing file: URL
Process rating: all ten parameters 34/100
- 0Tools and files. 2 referenced file(s) missing: 链接, URL
- 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
- 70Execution cost. Instruction body is 4341 tokens
- 100Steps. 123 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (34 tags): a typed call is more reliable
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 228: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 123 items
- +4Has examples (34 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.