Yif-Yang
116 lines
4.7 KiB
Markdown
116 lines
4.7 KiB
Markdown
# OpenClaw reference adaptation for SkillOpt-Sleep
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This directory is a contributed reference for connecting
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[SkillOpt-Sleep](https://github.com/microsoft/SkillOpt) to
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[OpenClaw](https://github.com/openclaw/openclaw) with a custom DeepSeek/Ollama
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backend.
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> **Reference status.** This is not one of the shared, plug-and-play
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> `skillopt_sleep` wrappers. Several scripts and the sample config contain
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> environment-specific absolute paths and assumptions from the original setup,
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> and the contributed wrapper has unresolved Python 3.10 syntax and backend
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> factory-signature gaps. The current checkout is not directly runnable; treat
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> it as porting source material, not an installation.
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## Included components
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| File | Purpose |
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|---|---|
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| `run_sleep.py` | custom cycle entry point |
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| `skillopt_sleep_openclaw.py` | DeepSeek Chat Completions backend plus local Ollama embeddings |
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| `run_sleep_cron.sh` | category-oriented cron wrapper |
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| `slash_sleep.py` | experimental `/sleep` command helper |
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| `config.json` | example engine configuration |
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| `SKILL.md` | OpenClaw skill manifest |
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| `tests/*.json` | example task sets for research, DevOps, and wiki workflows |
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The adaptation imports the shared engine but registers its own backend and
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maintains its own wrapper behavior. Changes to the shared CLI documentation do
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not automatically make every option available through these custom scripts.
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## Intended cycle
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```text
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harvest supported session data or load a task file
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→ replay with the current skill
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→ propose bounded edits
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→ validate the candidate on held-out tasks
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→ stage a proposal for operator review
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```
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The intended safety boundary is manual adoption: review the generated report and
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staged files before changing a live skill.
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## Adapt before use
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1. Clone SkillOpt into a location you control:
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```bash
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git clone https://github.com/microsoft/SkillOpt.git
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cd SkillOpt/plugins/openclaw
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```
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2. Inspect and replace the sample absolute paths in `run_sleep.py`,
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`slash_sleep.py`, `run_sleep_cron.sh`, and `config.json`. Confirm the engine
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checkout, OpenClaw workspace, state directory, skill directory, and task-file
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paths all point to isolated test locations.
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3. Review `config.json`. In particular, do not assume that values such as
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`max_tokens_per_night` or `replay_mode` are enforced by this custom wrapper
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merely because they appear in the example config.
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4. Supply credentials through your normal secret-management mechanism. Do not
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commit a DeepSeek key or place it in a world-readable file.
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5. Resolve every known porting gap listed in [`SKILL.md`](SKILL.md), add
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isolated tests for your adapted backend, and verify that `--help` imports
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cleanly on Python 3.10+. Only then start with a dry run and one reviewed
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task file. The target command should be shaped like:
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```bash
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cd /path/to/SkillOpt/plugins/openclaw
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python3 run_sleep.py --config /path/to/reviewed-config.json \
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--tasks tests/research-cron-tasks.json --dry-run
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```
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6. Inspect the report, paths, network destinations, and proposed edits before
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considering a non-dry run or scheduling.
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## Data boundary
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The custom `openclaw-deepseek` backend sends task, skill, response, rubric, and
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reflection content to the configured DeepSeek endpoint. Its embedding helper can
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send truncated text to the configured local Ollama service. Do not assume these
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outbound prompts have been fully redacted; inspect transcript/task inputs and the
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provider's retention policy before using real data.
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Use HTTPS for a remote DeepSeek-compatible endpoint. Keep any plaintext Ollama
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endpoint on a trusted loopback interface. For a network-free engine smoke test,
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use the shared SkillOpt-Sleep CLI with `--backend mock` rather than assuming this
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custom wrapper is isolated.
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## Scheduling
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`run_sleep_cron.sh` and the scheduling helpers are examples, not portable
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installers. Adapt their paths, create log directories, verify their environment,
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and run the exact command manually before adding a cron entry. Scheduled runs
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must preserve the same manual-adoption and credential boundaries as interactive
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runs.
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## Validation scope
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The bundled JSON files are example held-out task sets, not a universal OpenClaw
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benchmark. Provider cost and quality depend on the selected model, task content,
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number of calls, and pricing at run time; this reference does not promise a fixed
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nightly cost. Validate the adapted workflow in an isolated workspace before
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using it on live skills.
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For the supported shared-engine CLI and its current flags, see the
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[integration reference](../README.md#supported-cli-surface). For measured
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SkillOpt-Sleep results and limitations, see
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[`docs/sleep/RESULTS.md`](../../docs/sleep/RESULTS.md).
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## License
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MIT, consistent with SkillOpt core.
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