AD REFINE_Project
REFINE is an adaptive skill engine for structured session diagnostics. Use this skill when a user explicitly requests: logging error patterns across sessions, maintaining session memory, generating System Prompt Patches from failure analysis, or structured feedback capture on ClawHub or SkillPay. Activate for requests about "error logging", "session memory", "patch synthesis", or "adaptive skill logging". Do not activate for general conversation or unrelated tasks.
As a process D 46/100 · 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens)
Process rating: all ten parameters 46/100
- 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. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (REFINE_Project) differs from the folder (refine-agent)
- 100Tools and files. No external tools needed
- 100Steps. 5 steps
- 100Execution cost. Instruction body is 1032 tokens
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 469: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 5 items
- +4Has examples (3 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.