AD curiosity-learning-curve
Activate when: user says 'I used to love this but don't anymore', 'I feel like I've stopped learning', 'I go through the motions but nothing excites me', 'how do I stay curious as I get older', or a domain expert's knowledge has visibly stagnated despite continued effort. Do NOT activate when: the problem is burnout rather than curiosity deficit (burnout requires recovery first); the issue is capability or skill gap rather than curiosity; structural causes (bad environment, financial stress) are driving the motivation problem. More: deciqai.com/c/curiosity-learning-curve
Activate when: user says 'I used to love this but don't anymore', 'I feel like I've stopped learning', 'I go through the motions but nothing excites me', 'how…
As a process D 43/100 · 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: 4. 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 43/100
- 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. 6 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 43 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2294 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 577: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.