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description: Run the SkillOpt-Sleep cycle with the handoff backend — no API subprocess; this session answers the engine's model calls via prompt/answer files, in isolated fresh-context subagents argument-hint: "[run | dry-run] [--preferences "..."] (default: run)" allowed-tools: Bash, Read, Write, Task

/skillopt-sleep-handoff — session-executed sleep cycle

You are driving SkillOpt-Sleep in handoff mode: the Python engine runs every deterministic stage (harvest → mine → replay scoring → gate → stage) and outsources each model call (attempt / judge / reflect) to YOU via prompt files. No claude -p subprocess, no API key — the model work runs on this session's budget, but each prompt MUST be answered in a fresh, isolated context so the validation gate stays honest.

Requested action: $ARGUMENTS

(If $ARGUMENTS is empty, treat it as run.)

The loop

Repeat until the engine exits 0 (done) — at most 8 rounds:

1. Run the engine via the bundled runner. Split $ARGUMENTS into the action and remaining options, and preserve those options on every resumed round:

   "${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" <action> --backend handoff --project "$(pwd)" --scope invoked <remaining options>
   

   • exit 0 → the night is complete; go to "Finish" below.
   • exit 3 → pending model calls; continue with step 2.
   • anything else → stop and show the user the error output.

2. Read the batch: Read .skillopt-sleep-handoff/pending.json in the project. Each entry has id, prompt, max_tokens, answer_file.

3. Answer each prompt in ISOLATION — this is the integrity rule:

   • For each entry, launch a subagent (Task tool) whose ENTIRE input is the prompt text verbatim. Add nothing: no summary of this session, no mention of SkillOpt, no other prompts from the batch.
   • Take the subagent's reply and Write the raw answer text (no commentary, no code fences) to the entry's answer_file.
   • NEVER answer from this session's own context — you have seen the mined tasks and their references, so inline answers would contaminate the held-out gate and fake the improvement score.

4. Re-run the same engine command — it resumes from the answers directory and either finishes or stages the next batch.

Finish

• For run, if the engine prints a staging directory, Read its report.md and show the user: held-out baseline → candidate score, the gate decision, the proposed edits, and where the proposal is staged. If an accepted proposal was staged, tell the user nothing live changed, inspect status, and offer the exact reviewed selection: adopt --legacy, repeatable adopt --skill NAME, or adopt --all-skills.
• For dry-run, no staging directory or report.md is created; summarize the final stdout instead.
• The engine archives .skillopt-sleep-handoff/ on a completed real run; do not delete it yourself.

Safety reminders

• Never edit CLAUDE.md or SKILL.md yourself — only adopt does that, with a backup.
• Mined tasks are pinned to .skillopt-sleep-handoff/tasks.json on round one, so sessions created while answering prompts cannot shift the task set. Do not edit that file.
• If a batch looks like it contains secrets or content the user would not want re-processed, stop and ask before answering.
• Handoff files apply pattern-based secret redaction, but that is not a guarantee that prompts are free of sensitive data. Treat the pending batch as private user data and do not copy it into chat, logs, or commits.