BF knowledge-agent-tool-free
基于文件的知识捕获与检索工具,支持URL、视频、文章、社交内容收藏与搜索。Use when 需要SEO优化、关键词分析、排名提升、搜索流量优化时使用。不适用于黑帽SEO手段。适用于独立开发者、企业团队和自动化工作流场景。支持中文交互，无需复杂配置即开即用。输出结果可直接使用，减少二次加工成本。提供结构化输出和错误处理机制。
基于文件的知识捕获与检索工具,支持URL、视频、文章、社交内容收藏与搜索。Use when…
As a process F 35/100 · Will not run — References files that are not bundled: urls/2026-02-26-ai-agent-guide.md, extracts/2026-02-26-python-async.md, research/2026-02-26-ai-market.md
How to improve
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: urls/2026-02-26-ai-agent-guide.md - warning
missing-refreference to a missing file: extracts/2026-02-26-python-async.md - warning
missing-refreference to a missing file: research/2026-02-26-ai-market.md - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 35/100
- 0Tools and files. 3 referenced file(s) missing: urls/2026-02-26-ai-agent-guide.md, extracts/2026-02-26-python-async.md, research/2026-02-26-ai-market.md
- 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
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1739 tokens
- 100Running it twice. No mutating operations
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- +2Single-language instructions
- +3Description length 162: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (10 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.