AC rules-distill
Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files. Use when the same principle keeps recurring across skills and belongs in a rule file instead.
Claims to scan your skills, spot recurring principles, and consolidate them into rule files. Sounds like automated refactoring for your instruction base.
Three files, two scripts. Quality rated 87, safety 100. Scripts failed in sandbox — jq missing. Not a skill bug, just environment. Actual scanning logic untested. Files clean, no lint errors, no critical findings.
Runs on all major platforms. If you have jq already, install it — the idea works. If not, test locally first.
Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 3. 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 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 14 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2276 tokens
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 213: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (9 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.
In the sandbox Скрипты не запустились
The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.
Запущено 2 скрипта; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.
Ни один не дошёл до работы — им не хватило зависимостей, аргументов или файлов. Это не отзыв о поведении: наблюдать было не за чем.
scripts/scan-rules.sh | не запустился: scripts/scan-rules.sh: line 14: jq: command not found |
scripts/scan-skills.sh | не запустился: scripts/scan-skills.sh: line 114: jq: command not found |
6 Oct 2026