AC target-intelligence
Provides target intelligence report covering target details, drugs, pipelines, druggability, and indications. When to use this skill - Target structure and biological functions - Competitive intelligence of pipelines with targets - Development of targeting pharmaceuticals - Target druggability or tractability - The indication treated with targets Typical queries - EGFR - Drugs targeting P53 - Druggability of Beta-amyloid - Cancers treated by targeting BRCA1 and BRCA2 Proteins
Provides target intelligence report covering target details, drugs, pipelines, druggability, and indications.
As a process C 56/100 · 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
- 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
body-longSKILL.md body ≈ 5222 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 56/100
- 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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Steps. 71 steps, 10 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5222 tokens
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 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)
- +2Single-language instructions
- +3Description length 480: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 71 items
- +3Output format is stated explicitly
- +4Has examples (9 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.