Skillopt Sleep Agent Skill

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:

• Replace every absolute workspace, repository, state, skill, log, and task

path with explicit user configuration.

• 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.

• 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.

• 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.

• 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.

• 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.