Make Codex integration skill-first
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Kirill Kostarev
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carpedkm
carpedkm
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1b2652c6f8
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1953484822
@@ -1,49 +1,93 @@
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---
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name: skillopt-sleep
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description: Nightly offline self-evolution for a Codex agent. Reviews past sessions, replays recurring tasks, and consolidates validated memory + skills behind a held-out gate. Use when the user wants Codex to learn from past usage, run a "sleep"/"dream" cycle, or schedule offline self-optimization.
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description: "Use when the user wants Codex to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, wants Codex to review past sessions, learn preferences, consolidate memory/skills, run dry-run/run/adopt/status for SkillOpt-Sleep, or schedule offline self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay offline -> consolidate validated memory + skills behind a held-out gate."
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---
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# SkillOpt-Sleep (Codex skill)
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# SkillOpt-Sleep: offline self-evolution for a local Codex agent
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This skill drives the `skillopt_sleep` engine — an offline "sleep cycle" that
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makes a Codex agent better at the user's recurring work without retraining.
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SkillOpt-Sleep gives the user's Codex agent a sleep cycle. While the user is
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offline or on demand, it reviews past local sessions, re-runs recurring tasks
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on the user's own budget, and consolidates what it learns into memory and
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skills. It keeps only changes that pass a held-out validation gate, and live
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files change only after the user explicitly adopts a staged proposal. There is
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no model-weight training.
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## When to use
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Trigger when the user wants to: review past sessions, learn their preferences,
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consolidate feedback into long-term memory/skills, run a nightly/offline
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self-improvement cycle, or adopt a staged proposal.
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Trigger when the user wants any of:
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## How to run it
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- Codex to learn from past sessions or get better the more they use it;
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- a nightly/scheduled or on-demand sleep/dream/offline self-improvement run;
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- to review past sessions and distill recurring tasks;
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- to consolidate feedback into memory or managed skills;
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- to run `status`, `harvest`, `dry-run`, `run`, or `adopt` for SkillOpt-Sleep.
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## The cycle
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1. **Harvest** - read local session transcripts according to the engine
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configuration and normalize them into session digests.
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2. **Mine** - turn digests into recurring `TaskRecord`s with outcomes and
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checkable references where possible.
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3. **Replay** - re-run mined tasks offline under the current skill and memory.
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4. **Consolidate** - reflect on failures and propose bounded edits.
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5. **Gate** - accept edits only when the held-out validation score improves.
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6. **Stage** - write the proposal under
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`<project>/.skillopt-sleep/staging/<date>/`; nothing live changes.
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7. **Adopt** - only after explicit user approval, copy staged files over live
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files with backups.
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## How to drive it
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Invoke the bundled runner via shell (Codex `exec` has shell access). The runner
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finds the engine and a Python ≥ 3.10 automatically:
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finds the engine and a Python >= 3.10 automatically.
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```bash
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# point at the repo if it isn't auto-detected from CWD:
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export SKILLOPT_SLEEP_REPO=/path/to/SkillOpt-Sleep
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" <action> --project "$(pwd)"
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" status --project "$(pwd)"
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" harvest --project "$(pwd)"
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" dry-run --project "$(pwd)" --backend mock
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" run --project "$(pwd)" --backend codex
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" adopt --project "$(pwd)"
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```
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`<action>` ∈ `status | dry-run | run | adopt | harvest`. Use `--backend codex`
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for real improvement on the user's own Codex budget (default `mock` = no spend).
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Actions are `status`, `harvest`, `dry-run`, `run`, and `adopt`.
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- Default backend is `mock`, which is deterministic and spends no API budget.
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- `--backend codex` uses the user's Codex budget for real improvement.
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- Keep `dry-run --backend mock` as the first smoke check unless the user
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explicitly asked for a real optimization run.
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## Steps
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1. Run the requested action; capture stdout.
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2. For `run`/`dry-run`: read the staged `report.md` it prints and show the user
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the held-out baseline → candidate score and the exact proposed edits.
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3. `run` only **stages** a proposal under `<project>/.skillopt-sleep/staging/`;
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nothing live changes until `adopt`. Offer `/skillopt-sleep adopt`.
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4. Never hand-edit the user's `AGENTS.md` / skills yourself — only `adopt` does,
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and it backs up first.
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2. For `dry-run` and `run`, report the held-out baseline -> candidate score,
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gate action, task count, session count, and exact proposed edits.
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3. If a staging directory is printed, read `report.md` before summarizing.
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4. `run` only stages a proposal; nothing live changes until `adopt`.
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5. Offer adoption only after the user has reviewed the staged proposal.
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6. Never hand-edit the user's `AGENTS.md`, memory, or skills as a substitute
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for `adopt`; adoption is the safety boundary and writes backups first.
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## Hard rules
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- Harvest is read-only. Do not edit archived sessions or raw transcripts.
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- Keep raw secrets, credentials, private user data, and unsanitized transcript
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contents out of messages, logs, generated artifacts, and commits.
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- Show validation evidence before recommending adoption.
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- Treat generated edits as proposals, not as source of truth.
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- Do not rely on deprecated custom prompts or `/sleep` slash commands for this
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Codex integration. This skill is the entrypoint.
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## Validate
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```bash
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python -m skillopt_sleep dry-run --project "$(pwd)" --backend mock --json
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python -m skillopt_sleep.experiments.run_gbrain --backend codex \
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--seeds brief-writer --data-root /path/to/gbrain-evals/eval/data/skillopt-v1 \
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--nights 2 --limit-replay 3 --limit-holdout 3
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```
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A deficient skill goes 0.00 → 1.00 on a held-out set; the optimizer's edits are
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gated on real-task performance.
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A deficient skill goes 0.00 -> 1.00 on a held-out set; the optimizer's edits
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are gated on real-task performance.
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