AC auto-revolution
Safe publishing variant of Auto Revolution. Provides structured task templates, review prompts, safety scanning, task state updates, and logging for human supervised multi step workflows. Use when creating or reviewing task JSON files, applying review results, scanning instruction text for risky shell patterns, or managing queued task activation. This package avoids autonomous background execution and does not run arbitrary shell instruction strings.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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 · 3
✓ No critical or high findings
Medium and low: 3
✓ Guard found no suspicious behaviour. 3 matches are attack strings quoted in this security skill's own documentation.
Files scanned: 15. 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
- 30Running it twice. 7 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 29 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 549 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 454: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (0 code blocks)
- +3All 9 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.