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skillopt-sleep

Run the Microsoft SkillOpt-Sleep workflow for ADVX Live's room-6657 style skill. Use when downloading the pinned optimizer, validating the reviewed task set, running mock or Codex optimization, inspecting staged candidates, evaluating final tests, adopting a reviewed improvement, rejecting an unsafe candidate, or rolling back an adopted proposal.

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Quellinformationen

Repository
woodfishhhh/ADVX-live
Letzte Quellaktivität
24. Juli 2026 um 15:28
Erkannte Sprache von SKILL.md
Englisch
Sterne
2
Forks
1

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
skillopt-sleep
description
Run the Microsoft SkillOpt-Sleep workflow for ADVX Live's room-6657 style skill. Use when downloading the pinned optimizer, validating the reviewed task set, running mock or Codex optimization, inspecting staged candidates, evaluating final tests, adopting a reviewed improvement, rejecting an unsafe candidate, or rolling back an adopted proposal.
# SkillOpt-Sleep Use the project wrapper for every room-6657 optimization action: `python scripts/run_room_6657_skillopt.py <action>`. Do not invoke the upstream `skillopt_sleep` CLI directly from this repository. The wrapper pins the upstream commit, uses project-private state, selects the native Windows Codex executable, disables memory evolution, and compiles accepted learned directives into the backend runtime artifact. ## Workflow 1. Run `bootstrap`, then `validate`. 2. Run `dry-run --backend mock` to prove plumbing only. 3. Run `run --backend codex` for a real bounded optimization. 4. Run `status` and inspect the latest `report.md`, `report.json`, `diagnostics.json`, and `proposed_SKILL.md`. 5. Reject a candidate that weakens a Persona or product contract, even when the model gate accepts it. 6. Run `evaluate --backend codex --skill <candidate>` on every candidate that survives review. 7. Record the explicit review with `approve --staging <directory> --reason "<review rationale>"` after the final tests pass. A rejected candidate cannot later be approved; run a new optimization instead. 8. Run `adopt --staging <directory>` only after approval is recorded. 9. Run project tests after adoption. Use `rollback --staging <directory>` if an adopted proposal regresses runtime behavior. ## Boundaries - Use only the reviewed task file under `tests/fixtures/room-6657/`. - Never harvest local transcripts or private sessions for this workflow. - Never enable memory evolution, scheduling, or automatic adoption. - Treat mock results as plumbing evidence, not language-quality evidence. - Keep the sb6657 corpus out of prompts, tasks, skills, staging reports, and generated runtime JSON. - Preserve every second-level heading and all 13 Persona identifiers in the target skill. - Allow at most two proposed edits in one run. - Require strict validation-set improvement, project review, and untouched final-test success before adoption. - Keep rejected staging directories as local evidence; they are Git-ignored and cannot be adopted through the wrapper after rejection. ## Upstream The pinned Microsoft SkillOpt repository and license are recorded in `resources/skillopt/skillopt.lock.json`. `bootstrap` downloads that exact commit to `.advx-data/tools/SkillOpt`, which remains outside version control.
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