CC book-learning-tutor
把书课程化并作为「专业教师」逐课带人类学完：课前备课→详尽费曼教学→练习闸门→间隔复习→背诵作业→自进化。默认处理使用者已提供的本地书（PDF/EPUB/DJVU/MOBI/AZW/DOCX/TXT/MD/CBZ）；**当使用者无本地书且明确授权时，可代为联网检索公开来源并用项目抓取工具获取**。当用户说「学这本书 <本地路径> / 把这本 PDF 课程化 / 教我这本书 / 继续学 <书> / 复习 <书> / 考考我 <书> / 帮我学 X」时调用。教学引擎已单包自包含，无需再加载其他技能。
把书课程化并作为「专业教师」逐课带人类学完：课前备课→详尽费曼教学→练习闸门→间隔复习→背诵作业→自进化。默认处理使用者已提供的本地书（PDF/EPUB/DJVU/MOBI/AZW/DOCX/TXT/MD/CBZ）；当使用者无本地书且明确授权时，可代为联网检索公开来源并用项目抓取工具获取。当用户说「学这本书…
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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 Obfuscation
uni-zero-widthexamples/flatland/第01章_I__Of_the_Nature_of_Flatland/第01课_I__Of_the_Nature_of_Flatland.md:6Zero-width / invisible characters (possible hidden text) (6 occurrences) (test fixture / example file; a stray character, not a sequence)Imagine a vast sheet of paper on which straight Lines, Triangles, Squares, Pentagons, Hexagons, and other Figures, instead of remaining fixed in their places, m…
fixturestray character -
low Obfuscation
obf-base64-blobtools/acquire/pipeline.py:482Long base64-looking blob (quoted — discussed, not commanded)"iVBO…mNk+M8AAAMBAQDJ/pLvAAAAAElFTkSuQmCC")
quoted -
low Obfuscation
obf-base64-blobtools/acquire/pipeline.py:604Long base64-looking blob (quoted — discussed, not commanded)"iVBO…mNk+M8AAAMBAQDJ/pLvAAAAAElFTkSuQmCC")
quoted
Files scanned: 48. 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") - note
frontmatter-keyunknown frontmatter key "description_en" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 32 steps, 2 vague phrases
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3727 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (6 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 250: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.