AC weread-digest
微信读书笔记智能消化技能。 AI-powered reading note digestion for WeRead (微信读书). Generates weekly/monthly reading reports, synthesizes book highlights into structured summaries, explores themes across books, surfaces reading stats, and archives concepts to knowledge bases. 触发词 / Triggers: "读书报告"、"reading digest"、"总结笔记"、"summarize my notes"、 "这周读了什么"、"reading report"、"微信读书报告"、"notebook summary"、"读书回顾"、 "reading review"、"reading stats"、"读书统计"、"跨书主题"、"cross-book theme"、 "整理进知识库"、"archive to knowledge base"、"同步笔记到知识库"、"入库"
微信读书笔记智能消化技能。 AI-powered reading note digestion for WeRead (微信读书).
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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 · 0
✓ No critical or high findings
Files scanned: 3. 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")
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 77 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2409 tokens
- 100Running it twice. No mutating operations
- low 11 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -220 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 12 example trigger phrases
- +3Description length 510: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 77 items
- +4Has examples (19 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.