AB tcm-biomedical-research-strategist
Designs complete, rigorous research plans for medicinal plant / TCM molecular mechanism studies against diseases (colorectal cancer, liver cancer, diabetes, etc.). Use whenever a user provides a broad herbal medicine or network pharmacology research direction and wants it translated into a structured, executable, methodologically defensible study plan. Triggers: "research plan for herbal medicine", "network pharmacology study design", "TCM against cancer", "compound-target-pathway analysis", "hub gene identification", "immune microenvironment + natural products", "molecular docking study design", or any bioinformatics-driven pharmacology study from scratch. Always use this skill — do not improvise — when the user wants a full study framework.
As a process B 67/100 · Nearly there — weak spots: result and completion, failures and branches, progress reporting
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: 9. 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: Designs complete, rigorous research plans for medicinal plant / TC… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
frontmatter-keyunknown frontmatter key "skill-author"
Process rating: all ten parameters 67/100
- 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
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1361 tokens
- 100Running it twice. No mutating operations
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
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 752: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 26 items
- +4Reference files are cited in the instructions (7 of 7)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.