AD biomedical-paper
AI-powered biomedical manuscript generation with docx output. Activates when user provides Chinese draft/outline and requests full English research paper. Includes: Abstract, Introduction, Methods, Results, Discussion, References. Specialized for: GBD epidemiology, cohort studies (CHARLS/NHANES), cross-sectional mediation analyses, pharmacovigilance (FAERS). Also supports: Chinese graduate/doctoral thesis (学位论文) formatting. Features: python-docx generation, Vancouver numbered references, journal-specific formatting. Confidence: High (validated workflow with 30+ successful papers)
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 7. 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 41/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 40Consistency. Frontmatter name (biomedical-paper) differs from the folder (biomedical-paper-billing)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 69 steps
- 100Execution cost. Instruction body is 3114 tokens
- 100Running it twice. No mutating operations
- low 16 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)
- +3Output format is not stated: the model decides each time
- -42 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 586: enough signal without eating the budget
- +4Structure: 49 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (23 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.