BB paper-deepread-comic-studio
Deep-read research papers into authoritative, reproducible teaching reports, then turn them into source-grounded multi-image cartoon-comic storyboard workflows with camera/style/logic continuity and final image-PDF assembly guidance. Use when users need rigorous paper understanding plus presentation-ready visual explanations without mixing report generation and image generation in one turn.
As a process B 72/100 · Nearly there — weak spots: result and completion, execution cost
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: 43. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 14366 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 72/100
- 40Result and completion. Does not say what the result is
- 40Execution cost. Instruction body is 14366 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 37 branches
- 85Steps. 462 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 18 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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
- -31 of 8 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 393: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 462 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.