OpenClaw reference adaptation for SkillOpt-Sleep

This directory is a contributed reference for connecting SkillOpt-Sleep to OpenClaw with a custom DeepSeek/Ollama backend.

Reference status. This is not one of the shared, plug-and-play skillopt_sleep wrappers. Several scripts and the sample config contain environment-specific absolute paths and assumptions from the original setup, and the contributed wrapper has unresolved Python 3.10 syntax and backend factory-signature gaps. The current checkout is not directly runnable; treat it as porting source material, not an installation.

Included components

File	Purpose
`run_sleep.py`	custom cycle entry point
`skillopt_sleep_openclaw.py`	DeepSeek Chat Completions backend plus local Ollama embeddings
`run_sleep_cron.sh`	category-oriented cron wrapper
`slash_sleep.py`	experimental `/sleep` command helper
`config.json`	example engine configuration
`SKILL.md`	OpenClaw skill manifest
`tests/*.json`	example task sets for research, DevOps, and wiki workflows

The adaptation imports the shared engine but registers its own backend and maintains its own wrapper behavior. Changes to the shared CLI documentation do not automatically make every option available through these custom scripts.

Intended cycle

harvest supported session data or load a task file
  → replay with the current skill
  → propose bounded edits
  → validate the candidate on held-out tasks
  → stage a proposal for operator review

The intended safety boundary is manual adoption: review the generated report and staged files before changing a live skill.

Adapt before use

Clone SkillOpt into a location you control:

git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt/plugins/openclaw

Inspect and replace the sample absolute paths in run_sleep.py, slash_sleep.py, run_sleep_cron.sh, and config.json. Confirm the engine checkout, OpenClaw workspace, state directory, skill directory, and task-file paths all point to isolated test locations.
Review config.json. In particular, do not assume that values such as max_tokens_per_night or replay_mode are enforced by this custom wrapper merely because they appear in the example config.
Supply credentials through your normal secret-management mechanism. Do not commit a DeepSeek key or place it in a world-readable file.
Resolve every known porting gap listed in SKILL.md, add isolated tests for your adapted backend, and verify that --help imports cleanly on Python 3.10+. Only then start with a dry run and one reviewed task file. The target command should be shaped like:
```
cd /path/to/SkillOpt/plugins/openclaw
python3 run_sleep.py --config /path/to/reviewed-config.json \
  --tasks tests/research-cron-tasks.json --dry-run
```
Inspect the report, paths, network destinations, and proposed edits before considering a non-dry run or scheduling.

Data boundary

The custom openclaw-deepseek backend sends task, skill, response, rubric, and reflection content to the configured DeepSeek endpoint. Its embedding helper can send truncated text to the configured local Ollama service. Do not assume these outbound prompts have been fully redacted; inspect transcript/task inputs and the provider's retention policy before using real data.

Use HTTPS for a remote DeepSeek-compatible endpoint. Keep any plaintext Ollama endpoint on a trusted loopback interface. For a network-free engine smoke test, use the shared SkillOpt-Sleep CLI with --backend mock rather than assuming this custom wrapper is isolated.

Scheduling

run_sleep_cron.sh and the scheduling helpers are examples, not portable installers. Adapt their paths, create log directories, verify their environment, and run the exact command manually before adding a cron entry. Scheduled runs must preserve the same manual-adoption and credential boundaries as interactive runs.

Validation scope

The bundled JSON files are example held-out task sets, not a universal OpenClaw benchmark. Provider cost and quality depend on the selected model, task content, number of calls, and pricing at run time; this reference does not promise a fixed nightly cost. Validate the adapted workflow in an isolated workspace before using it on live skills.

For the supported shared-engine CLI and its current flags, see the integration reference. For measured SkillOpt-Sleep results and limitations, see docs/sleep/RESULTS.md.

License

MIT, consistent with SkillOpt core.

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