| name | training-sprint-loop |
| description | Run a repeatable training sprint loop: implement, validate, commit, push, and CI check with clear evidence and scoped commits. |
| argument-hint | Task slice, validation scope, and commit intent |
| user-invocable | true |
| disable-model-invocation | false |
Training Sprint Loop
Inherits from task-core-loop.
Use this skill for short, repeatable training-focused delivery rounds where each round ends in a pushed checkpoint and CI status update.
Use When
- You want commit/push/review/fix/repeat cadence.
- You are iterating on training harness behavior, telemetry contracts, or workbench integration.
Inputs
- Change slice and intended outcome.
- Validation scope (focused tests, strict GPU run, or both).
- Commit scope (files that must be included/excluded).
Procedure Additions
- Apply all
task-core-loop steps first.
- Add strict or focused training validation for the changed slice.
- Push and capture CI status with conservative checks.
- Record run-artifact evidence (
status.json, benchmark.json, event traces) when relevant.
Conservative CI Mode
- Prefer one
gh run list status refresh per round.
- Avoid frequent polling loops unless user explicitly requests live watch.
- If runs are queued/in-progress, continue next safe local slice instead of aggressive CI polling.
Done Criteria
- Changes applied and validated for the slice.
- Commit pushed with clean scope.
- CI state captured (queued/in-progress/success/failure).
- Tracker/memory notes updated with evidence.