AC disease-investigation
Conduct comprehensive disease investigation combining academic literature, epidemiological data, clinical guidelines, pharmaceutical intelligence, and clinical trial reports. Users may inquire about disease pathogenesis, symptoms, pharmaceutical interventions, treatment options, patent landscapes, and business development opportunities. Load the skill when queries involve: - Disease pathology and molecular mechanisms - Regional disease incidence and subtypes - Clinical symptoms and diagnostic indicators - Treatment landscape and drug development pipeline - Patent and IP analysis for therapeutic areas - Business development and deal intelligence Typical queries - Pathogenesis of NSCLC - Treatment options for influenza - Incidence rates of leukemia in China - Clinical manifestations of depression - PD-1/PD-L1 patent landscape - Drug development pipeline for NSCLC
Conduct comprehensive disease investigation combining academic literature, epidemiological data, clinical guidelines, pharmaceutical intelligence, and…
As a process C 53/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice
The same skill appears in 1 more place: ClawHub
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: 2. 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 53/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Steps. 70 steps, 4 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3532 tokens
- 100Progress reporting. Reports progress
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 875: 120–800 characters recommended
- +2Single-language instructions
- +4Structure: 24 headings
- +3Step-by-step instructions: 70 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.