AC book-deep-reader
Deep-read a book and produce teaching-quality notes with critical evaluation and action guidance. Use when: (1) user wants to quickly learn and understand a book's knowledge system, (2) user needs accurate extraction of a book's core ideas to teach others, (3) user provides book title/author/ISBN and asks for a structured reading guide or summary, (4) user wants to verify their understanding of a book against original sources, (5) user wants critical evaluation of a book's value and limitations, (6) user wants cross-disciplinary insights and actionable takeaways. Supports Chinese and English books. Outputs a comprehensive .md file with knowledge framework, chapter details, core principles, practical cases, teaching materials, AND critical evaluation with action plans.
As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice
How to improve
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 100Steps. 82 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1717 tokens
- 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)
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 778: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 82 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (1 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.