AD skill-distiller
Distill successful workflows into reusable skills with quality gates. Use after completing multi-step tasks to evaluate if the workflow should be saved. Triggers: 'distill this', 'save as skill', 'make this reusable', or automatically at end of complex tasks. Evaluates novelty, success, reuse potential AND grounding (≥10 real samples) before generating SKILL.md. Includes IP boundary check (private vs shared classification), trigger keyword discipline, and pre-publish vet for skills going to public registries. Prevents skill bloat through 4-question quality gates and the 'sycophancy of completeness' anti-pattern.
As a process D 49/100 · Unfinished process — 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: 2. 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 49/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 17 mutating operations with no state check
- 40Consistency. Frontmatter name (skill-distiller) differs from the folder (auto-skill-distiller)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 42 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Execution cost. Instruction body is 2216 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (6 tags): a typed call is more reliable
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
- -218 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 619: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 42 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.