AC lygo-traumacodex
Run when the user asks for TraumaCodex, biometric IBI timing → dual offline/online digests, LDQ-style waveform from a timing list, or mirror dig seals. Pure local stdlib: no network, no subprocess, no external stack execution. Input is inter-beat interval milliseconds (demo set or --ibi-file), not a medical device. Not for health diagnosis or treatment. Healing codes mean protocol digests only.
Run when the user asks for TraumaCodex, biometric IBI timing → dual offline/online digests, LDQ-style waveform from a timing list, or mirror dig seals.
As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenscripts/traumacodex_core.py:312High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"signature": "Delt…-v1",
detector
Files scanned: 8. 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 56/100
- 0Result and completion. Does not say what the result is
- 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
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 6 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 380 tokens
- 100Running it twice. No mutating operations
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
- +4Structure: 1 headings, hard to scan
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 397: enough signal without eating the budget
- +3Step-by-step instructions: 6 items
- +4Has examples (1 code blocks)
- +3All 3 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.