---

name: skillopt-sleep description: Reference-only OpenClaw adaptation of SkillOpt-Sleep. Use it to study or port the contributed DeepSeek wrapper, not as a ready-to-run installation.

SkillOpt-Sleep OpenClaw reference adaptation

This directory is a contributed reference, not a supported, plug-and-play OpenClaw integration. It illustrates one way to connect the shared skillopt_sleep cycle to a custom DeepSeek Chat Completions backend and a set of environment-specific task fixtures.

Do not run or schedule the files unchanged. Several scripts and the sample configuration preserve assumptions from the contributor's original machine, and parts of the wrapper have not yet been ported to the current shared-engine interfaces. Start with the directory's README.md, which is the authoritative status and adaptation guide.

What is included

• skillopt_sleep_openclaw.py — a contributed DeepSeek backend prototype. It also contains an Ollama embedding helper, but that helper is not wired into the current shared sleep cycle.
• run_sleep.py — a custom cycle wrapper with environment-specific paths and a backend-registration shim.
• slash_sleep.py — an experimental command helper written for an older staging-manifest shape.
• run_sleep_cron.sh — a machine-specific category runner, not a portable cron installer.
• config.json — a sample configuration, not a set of guaranteed or enforced runtime limits.
• tests/*.json — example task fixtures from one environment, not a universal OpenClaw benchmark.

Known porting gaps

Before treating this as an integration, a maintainer must at least:

1. Replace every absolute workspace, repository, state, skill, log, and task path with explicit user configuration.
2. Update the custom backend factory to the current get_backend call contract, including the project directory, and update its backend methods and edit records to the current protocol.
3. Replace the experimental adoption logic with the current staging manifest and skillopt_sleep.staging.adopt behavior. Current staging artifacts use proposed_SKILL.md / proposed_CLAUDE.md, manifest.json, and report files; they do not expose the old manifest.proposed_skill field.
4. Decide how real OpenClaw transcripts are converted into a supported session format. Pointing claude_home at an arbitrary agent directory does not by itself make its files Claude Code-compatible JSONL.
5. Build scheduling around the adapted wrapper. The shared scheduler launches the shared CLI; it does not automatically preserve this custom backend or its category task-file flow.
6. Add isolated end-to-end tests for dry-run, accepted/rejected gates, staging, adoption and backup, credential failure, and scheduled execution.

Until those gaps are resolved, use the supported shared python -m skillopt_sleep CLI with --backend mock to test SkillOpt-Sleep itself, and treat this directory only as source material for a future OpenClaw port.

Shared-engine features are not wrapper features

At this revision the supported shared CLI backends are mock, claude, codex, copilot, handoff, and azure_openai; the plugin integration reference is the authoritative list. The shared engine can consolidate a selected skill and project CLAUDE.md memory (controlled by evolve_skill and evolve_memory), and its schedule / unschedule actions manage shared-engine cron entries. Those capabilities do not make the custom OpenClaw wrapper portable: the shared scheduler will not invoke the prototype backend or its category fixtures. Use the shared documentation for those features, not this reference SKILL.

Data and credential boundary

The prototype DeepSeek backend sends task, skill, memory, response, rubric, and reflection content to its configured Chat Completions endpoint. Its source also contains a helper that can send text to an Ollama service if a future port wires that helper into the cycle. Neither path should be assumed to remove every secret or private detail.

Before any port is tested with real data:

• use isolated, synthetic or explicitly reviewed task files;
• replace sample business names, personal references, URLs, and machine paths;
• load credentials through the operator's secret-management mechanism;
• verify TLS and retention policy for every remote endpoint; and
• inspect all staged artifacts before adoption.

The bundled fixtures are examples only. Their scores and any old cost estimates do not establish effectiveness, safety, or a stable nightly price for another OpenClaw deployment.

Further information

• OpenClaw README — current reference status and adaptation checklist
• plugin integration reference — supported shared-engine CLI surface and data boundary
• SkillOpt-Sleep documentation — concepts, results, and limitations

Contributions that turn this reference into a portable integration should add tests and update all three documents together.