BB value-mining-lengthybooks
Extract actionable insights from books using Four-Layer Methodology: (1) Skeleton - conceptual frameworks and mental models, (2) Flesh - 2-3 detailed case studies including original examples, cross-industry analogies, and real-world applications, (3) Essence - cross-industry migration matrices with specific industry adaptations and 3-5 step executable SOPs, (4) Residue - critical analysis of boundaries, limitations, and failure conditions. Dual processing modes: Quick (5 core points, 10-15 min) for rapid assessment and Deep (10-20 comprehensive points, 30-45 min) for systematic learning. Includes Feynman validation testing with scenario-based problems and scoring rubrics. Generates structured reports in Markdown/PDF/Word formats. Use when user requests systematic knowledge extraction, concept distillation, or implementation guidance from methodology/business/psychology/self-help books with emphasis on practical application and cross-domain transfer.
Extract actionable insights from books using Four-Layer Methodology: (1) Skeleton - conceptual frameworks and mental models, (2) Flesh - 2-3 detailed case…
As a process B 68/100 · Nearly there — weak spots: when it triggers, consistency, running it twice
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Extract actionable insights from books using Four-Layer Methodolog… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
body-longSKILL.md body ≈ 5520 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 68/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (value-mining-lengthybooks) differs from the folder (valuemining-lengthybooks)
- 50Failures and branches. 0 branches, has a failure section
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5520 tokens
- 85Steps. 191 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Result and completion. Output format and completion criterion are stated
- 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 963: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +4Structure: 43 headings
- +3Step-by-step instructions: 191 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.