Iteration Path
Use this path when improving a skill based on outcomes and examples.
Example intake
Read references/iteration-evidence.md when examples should be persisted across future skill revisions.
Capture example records with:
- label (
positiveornegative) - example kind (
true-positive,false-positive,fix,regression,edge-case) - evidence origin (
human-verified,mixed,synthetic) - anonymized content
- source provenance pointer (where the example came from)
Replay and Review
- Review behavior against working set.
- Review behavior against holdout set.
- Record improved/unchanged/regressed outcomes.
- Confirm both positive and negative behavior changed in the expected direction.
Improvement rules
- Prioritize fixes for repeated negative patterns.
- Preserve behavior that consistently succeeds on positives.
- Update transformed examples when guidance changes.
- Record deltas in
SOURCES.mdchangelog. - Expand input collection when failures indicate coverage gaps.
- Store durable positive/negative examples in
references/evidence/instead of overloadingSKILL.md,SOURCES.md, or a generic reference file. - Keep holdout examples separate from working examples until validation is complete.
- Update
SPEC.mdwhen iteration changes the skill's intended scope, evidence model, validation expectations, or known limitations.
Required output
- Example intake summary
- Behavior deltas
- Updated artifacts
- Replay summary