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

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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>
This commit is contained in:
dimitarvdenev
2026-07-12 18:23:53 +02:00
committed by GitHub
parent 7df49656d1
commit b309723baa
10 changed files with 745 additions and 6 deletions
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@@ -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)'