/skillopt-sleep-handoff

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.