AC medical-conference-search
Search medical conference and presentation databases. Use this skill whenever the user asks about medical conferences, academic conferences, session abstracts, posters, oral presentations, or conference-presented drug/trial data. Three scripts are available: search_conferences.py (find conferences), search_presentations.py (find abstracts/presentations), and search_chained.py (find conferences then auto-inject into presentation search). Trigger words include: conference, symposium, congress, ASCO, ESMO, AHA, ACC, session, abstract, poster, oral presentation, data presented at, efficacy data, safety data, congress abstract.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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: 5. 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 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (medical-conference-search) differs from the folder (medical-conference)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 17 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 3179 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 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
- +1No license
- +2Single-language instructions
- +3Description length 630: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (18 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.