AF evidence-first-research
Evidence-first workflow for scientific research, literature review, method selection, study planning, biomedical analysis, and research writing. Use when Codex should pause before acting to search for prior papers, datasets, protocols, software, libraries, reporting standards, or methodological patterns, then decide whether to adopt, adapt, benchmark, or design a new approach. Especially useful for medicine, public health, biology, translational research, clinical questions, and any task where evidence quality, safety, or reproducibility matters.
As a process F 44/100 · Will not run — References files that are not bundled: references/evidence-evaluation.md
How to improve
- The text references files that are not there: add them or drop the references.
- 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
- warning
missing-refreference to a missing file: references/evidence-evaluation.md
Process rating: all ten parameters 44/100
- 0Tools and files. 1 referenced file(s) missing: references/evidence-evaluation.md
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 55 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1490 tokens
- 100Running it twice. Mutating operations check current state
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)
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 552: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 55 items
- +3Output format is stated explicitly
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.