AC phenosnap-phenotype-extractor
Extract clinical phenotypes and medication entities from user-provided text using PhenoSnap, producing a timestamped JSON output.
As a process C 64/100 · Has gaps — weak spots: result and completion, consistency, running it twice
How to improve
For the model run — optional
- 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: 4. 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 64/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (phenosnap-phenotype-extractor) differs from the folder (phenoskill)
- 60Tools and files. Uses tools (web, git, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 107 steps
- 100Failures and branches. 8 branches, has a failure section
- 100Execution cost. Instruction body is 2412 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
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
- +1No license
- +2Single-language instructions
- +3Description length 129: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 107 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.
External checks
ClawHub: suspicious
This skill has a coherent medical text extraction purpose, but it automatically downloads and runs unpinned third-party Python code before handling sensitive health text.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026