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Auto-extract patterns from coding sessions, track corrections, and build reusable knowledge with confidence scoring
Passed 18 of 21 test cases with the skill, and 7 of 21 without it.
and 14 more in the full report
13 fixed6 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
A developer explicitly rejects a proposed state management pattern that previously had a confidence score of 0.75. What is the updated score?
| Checked | Without skill | With skill |
|---|---|---|
| Calculates the updated confidence score as 0.55 | Missed | Passed |
The instructions your model reads when the skill loads.
After every significant coding session, extract and categorize learnings into three buckets:
pattern:
id: "LEARN-2025-0042"
category: "error-handling"
type: "correction" # correction | success | anti-pattern
confidence: 0.85 # 0.0 to 1.0
language: "typescript"
context: "API error responses"
observation: "Returning raw error messages from database exceptions exposes internals"
lesson: "Always map database errors to application-level error codes before returning"
example:
before: "catch (e) { res.status(500).json({ error: e.message }) }"
after: "catch (e) { logger.error(e); res.status(500).json({ error: 'INTERNAL_ERROR' }) }"
frequency: 3 # times this pattern has been observed
last_seen: "2025-06-15"
| Score | Meaning | Action |
|---|---|---|
| 0.95+ | Verified across multiple projects | Apply automatically |
| 0.80-0.94 | Confirmed in this codebase | Apply and mention |
| 0.60-0.79 | Observed but not fully validated | Suggest with caveat |
| 0.40-0.59 | Hypothesis based on limited data | Ask before applying |
| <0.40 | Speculative, needs validation | Document but do not apply |
Update confidence based on:
test cases passed, vs 7 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.