feat(sleep): add handoff backend — session-executed model calls, no API subprocess (#125)

Adds --backend handoff: the engine runs all deterministic stages and
outsources attempt/judge/reflect to prompt/answer files an interactive
agent session fills between runs (exit 3 = pending batch, re-run to
resume). Deterministic replay + the prompt-hash answer cache make resume
stateless; sentinel detection aborts any call built from unanswered
output so placeholders never reach scores or staging. Session digests
and mined tasks are pinned per night (secret-redacted) so the sessions
answering prompts cannot shift the task set, and LLM mining is routed
through the same handoff files. Ships a /skillopt-sleep-handoff Claude
Code command that answers each prompt in a fresh-context subagent to
protect the held-out gate.

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

plugins/claude-code/README.md

@@ -60,6 +60,9 @@ they shell out to the CLIs you already have.
 /skillopt-sleep run         # full cycle: stages a reviewed proposal (still no live edits)
 /skillopt-sleep status      # see history + the latest staged proposal
 /skillopt-sleep adopt       # apply the staged proposal to CLAUDE.md / SKILL.md (with backup)
+
+/skillopt-sleep-handoff run # same cycle, but THIS session answers the model calls
+                            # (no claude -p subprocess, no API key — subscription-friendly)
 ```
 
 Or call the engine directly (Python ≥ 3.10):
@@ -74,6 +77,26 @@ Default backend is **`mock`** — deterministic, no API spend — so you can try
 plumbing for free. Switch to `--backend claude` or `--backend codex` for genuine
 improvement on your own budget.
 
+### Handoff mode (session answers the model calls)
+
+`--backend handoff` runs the cycle without any model subprocess: the engine
+executes the deterministic stages and writes every model call it needs to
+`.skillopt-sleep-handoff/PROMPTS.md` + `pending.json` (exit code 3). You (or
+the `/skillopt-sleep-handoff` command, which automates the loop with isolated
+fresh-context subagents) write each raw answer to `answers/<id>.md` and re-run
+the same command; it resumes from the answers and either finishes or stages
+the next batch. Typically 3–6 rounds per night.
+
+```bash
+python -m skillopt_sleep run --backend handoff --project "$(pwd)"
+# ... answer .skillopt-sleep-handoff/PROMPTS.md into answers/<id>.md ...
+python -m skillopt_sleep run --backend handoff --project "$(pwd)"   # resume
+```
+
+Answer every prompt in a **fresh context** — a session that has already seen
+the mined tasks and their references would contaminate the held-out gate.
+Details: [the plugins README](../README.md#--backend-handoff--session-executed-calls-no-api-subprocess).
+
 ## Does it actually improve? (real models, public benchmark)
 
 SkillOpt-Sleep is validated against [gbrain-evals](https://github.com/garrytan/gbrain-evals)'

plugins/claude-code/commands/skillopt-sleep-handoff.md

@@ -0,0 +1,67 @@
+---
+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:
+
+   ```bash
+   "${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" <action> --backend handoff --project "$(pwd)" --scope invoked
+   ```
+
+   - 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
+
+- `Read` the `report.md` in the staging dir the engine printed and show
+  the user: held-out baseline → candidate score, the gate decision, the
+  proposed edits, and where the proposal is staged.
+- Tell the user nothing live changed; offer `/skillopt-sleep adopt`.
+- 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.