CD shimo-export
石墨文档导出 AI Skill — 通过 AI Agent 与石墨文档 (shimo.im) API 交互， 实现自动登录、文件列表浏览、团队空间扫描、批量导出（支持 Markdown、PDF、Word、Excel、PPT、XMind、图片等格式）。 当用户提到石墨文档、导出文件、下载文档、批量导出、团队空间、文件列表时，使用此 skill。 即使用户没有明确说"石墨"，只要意图涉及从 shimo.im 导出或浏览文档，也应触发此 skill。 Shimo Document Export AI Skill — Enables AI agents to interact with shimo.im API for automated login, file browsing, team space scanning, and batch export in multiple formats (md, pdf, docx, xlsx, pptx, xmind, jpg).
石墨文档导出 AI Skill — 通过 AI Agent 与石墨文档 (shimo.im) API 交互， 实现自动登录、文件列表浏览、团队空间扫描、批量导出（支持 Markdown、PDF、Word、Excel、PPT、XMind、图片等格式）。…
As a process D 35/100 · Unfinished process — References files that are not bundled: references/api.md
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: 2. 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: references/api.md - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: references/api.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. 44 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1645 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (16 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 441: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 44 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.