docs: sync documentation with post-v0.2 changes
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---
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name: skillopt-sleep
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description: Validate and refine agent skills through nightly sleep cycles with held-out gates. Wraps Microsoft's SkillOpt-Sleep engine for the OpenClaw/DeepSeek stack.
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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.
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---
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# skillopt-sleep — OpenClaw Adaptation of Microsoft SkillOpt-Sleep
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# SkillOpt-Sleep OpenClaw reference adaptation
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A nightly self-improvement loop that reads our session transcripts, mines recurring workflow patterns, replays them with proposed skill edits, and gates the proposals against a held-out test set. Only improvements that beat baseline are staged for human adoption.
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This directory is a contributed **reference**, not a supported, plug-and-play
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OpenClaw integration. It illustrates one way to connect the shared
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`skillopt_sleep` cycle to a custom DeepSeek Chat Completions backend and a set of
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environment-specific task fixtures.
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## When To Use
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Do not run or schedule the files unchanged. Several scripts and the sample
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configuration preserve assumptions from the contributor's original machine,
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and parts of the wrapper have not yet been ported to the current shared-engine
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interfaces. Start with the directory's [README.md](README.md), which is the
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authoritative status and adaptation guide.
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- After Hermes's Weekly Skill Review (or as its replacement)
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- When a skill is being used 10+ times/week and could be tighter
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- Before promoting a new skill from `skill-proposals/` to `skills/`
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- When a skill regresses in observed quality
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## What is included
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## What It Does (One Cycle)
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- `skillopt_sleep_openclaw.py` — a contributed DeepSeek backend prototype. It
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also contains an Ollama embedding helper, but that helper is not wired into
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the current shared sleep cycle.
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- `run_sleep.py` — a custom cycle wrapper with environment-specific paths and a
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backend-registration shim.
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- `slash_sleep.py` — an experimental command helper written for an older
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staging-manifest shape.
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- `run_sleep_cron.sh` — a machine-specific category runner, not a portable cron
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installer.
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- `config.json` — a sample configuration, not a set of guaranteed or enforced
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runtime limits.
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- `tests/*.json` — example task fixtures from one environment, not a universal
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OpenClaw benchmark.
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```
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harvest session transcripts -> mine recurring task patterns
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-> replay each pattern (current skill vs proposed)
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-> GATE: must improve held-out score
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-> stage proposal
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-> Ethan adopts (manual)
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```
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## Known porting gaps
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Nothing live changes until Ethan adopts. Every adopt backs up first.
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Before treating this as an integration, a maintainer must at least:
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## Architecture
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1. Replace every absolute workspace, repository, state, skill, log, and task
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path with explicit user configuration.
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2. Update the custom backend factory to the current `get_backend` call contract,
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including the project directory, and update its backend methods and edit
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records to the current protocol.
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3. Replace the experimental adoption logic with the current staging manifest
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and `skillopt_sleep.staging.adopt` behavior. Current staging artifacts use
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`proposed_SKILL.md` / `proposed_CLAUDE.md`, `manifest.json`, and report files;
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they do not expose the old `manifest.proposed_skill` field.
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4. Decide how real OpenClaw transcripts are converted into a supported session
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format. Pointing `claude_home` at an arbitrary agent directory does not by
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itself make its files Claude Code-compatible JSONL.
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5. Build scheduling around the adapted wrapper. The shared scheduler launches
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the shared CLI; it does not automatically preserve this custom backend or
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its category task-file flow.
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6. Add isolated end-to-end tests for dry-run, accepted/rejected gates, staging,
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adoption and backup, credential failure, and scheduled execution.
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```
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skills/skillopt-sleep/
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├── SKILL.md # this file
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├── config.json # engine config (backend, budgets, etc.)
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├── run_sleep.py # entry point
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└── skillopt_sleep_openclaw.py # DeepSeek/Ollama backend
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```
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Until those gaps are resolved, use the supported shared
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`python -m skillopt_sleep` CLI with `--backend mock` to test SkillOpt-Sleep itself,
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and treat this directory only as source material for a future OpenClaw port.
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The engine itself is at `~/.openclaw/workspace/SkillOpt/skillopt_sleep/` (cloned from microsoft/SkillOpt).
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## Shared-engine features are not wrapper features
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## Usage
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At this revision the supported shared CLI backends are `mock`, `claude`,
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`codex`, `copilot`, `handoff`, and `azure_openai`; the
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[plugin integration reference](../README.md#supported-cli-surface) is the
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authoritative list. The shared engine can consolidate a selected skill and
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project `CLAUDE.md` memory (controlled by `evolve_skill` and `evolve_memory`),
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and its `schedule` / `unschedule` actions manage shared-engine cron entries.
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Those capabilities do **not** make the custom OpenClaw wrapper portable: the
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shared scheduler will not invoke the prototype backend or its category
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fixtures. Use the shared documentation for those features, not this reference
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SKILL.
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```bash
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# Run one cycle with current config
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cd ~/.openclaw/workspace/skills/skillopt-sleep
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python3 run_sleep.py
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## Data and credential boundary
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# Dry run (report only, no staging)
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python3 run_sleep.py --dry-run
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The prototype DeepSeek backend sends task, skill, memory, response, rubric, and
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reflection content to its configured Chat Completions endpoint. Its source also
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contains a helper that can send text to an Ollama service if a future port wires
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that helper into the cycle. Neither path should be assumed to remove every
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secret or private detail.
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# Use a pre-built task set (recommended for testing)
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python3 run_sleep.py --tasks tests/research-cron-tasks.json
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```
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Before any port is tested with real data:
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## Scheduling
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- use isolated, synthetic or explicitly reviewed task files;
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- replace sample business names, personal references, URLs, and machine paths;
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- load credentials through the operator's secret-management mechanism;
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- verify TLS and retention policy for every remote endpoint; and
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- inspect all staged artifacts before adoption.
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```bash
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python3 slash_sleep.py schedule --hour 3 --minute 17
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python3 slash_sleep.py unschedule
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python3 slash_sleep.py unschedule --all
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```
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The bundled fixtures are examples only. Their scores and any old cost estimates
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do not establish effectiveness, safety, or a stable nightly price for another
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OpenClaw deployment.
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Installs a nightly cron entry using the shared SkillOpt-Sleep scheduler. This is an alternative to the external `run_sleep_cron.sh` script.
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## Further information
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## Alternative backends
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- [OpenClaw README](README.md) — current reference status and adaptation checklist
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- [plugin integration reference](../README.md) — supported shared-engine CLI
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surface and data boundary
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- [SkillOpt-Sleep documentation](../../docs/sleep/README.md) — concepts,
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results, and limitations
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While OpenClaw defaults to `openclaw-deepseek` (DeepSeek V4 Pro + Ollama), the shared engine also supports:
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- `--backend mock` — deterministic, no API spend (for testing)
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- `--backend claude` — uses the Claude CLI
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- `--backend codex` — uses the Codex CLI
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- `--backend copilot` — uses the GitHub Copilot CLI
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These can be used via the engine directly (`python -m skillopt_sleep`).
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## Shared-engine flags
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When invoking the engine directly, all standard flags are available:
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- `--source codex` / `--source auto` — harvest from Codex Desktop sessions
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- `--tasks-file PATH` — use a pre-built task set
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- `--target-skill-path PATH` — explicit SKILL.md target
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- `--max-tasks N` / `--max-sessions N` — cap workload
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- `--progress` — print phase progress
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- `--json` — machine-readable output
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- `--auto-adopt` — auto-adopt if gate passes
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Config keys: `preferences`, `gate_mode`, `gate_metric`, `dream_rollouts`, `recall_k`, `evolve_memory`, `evolve_skill`.
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## Config (config.json)
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Key knobs:
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- `backend: "openclaw-deepseek"` — our custom backend
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- `model: "deepseek-v4-pro"` — optimizer model
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- `edit_budget: 3` — max bounded edits per night
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- `gate_mode: "on"` — validation-gated (rejects regressions)
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- `auto_adopt: false` — require Ethan to adopt manually
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- `max_tasks_per_night: 12` — cap to control cost
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## Cost Estimate
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Per night: 12 tasks × (1 attempt + 1 judge + 1 reflect) × ~$0.005/1K tokens × ~3K tokens/call ≈ **$0.50-2.00/night**.
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## Outputs
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- Report: `~/.skillopt-sleep/state.json` (running totals)
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- Staging: `~/.skillopt-sleep/staging/<night>/`
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- `report.md` — readable summary
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- `best_skill.md` — proposed skill
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- `edits.json` — bounded edit list
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- `before.md` / `after.md` — diffs
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## Held-Out Test Sets (Phase 2)
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Located at `tests/<category>-tasks.json`. Each task has:
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- `prompt` — the recurring task
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- `reference` — exact-match gold answer
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- `rubric` — soft score rubric (0-1)
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- `domain` — research/devops/wiki/etc.
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Currently building for 3 categories:
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- research-cron-output
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- devops-infrastructure-check
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- wiki-canonical-guide
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## When NOT To Use
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- For a one-off workflow (not a recurring pattern)
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- During a crisis/incident (humans must lead)
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- When session transcripts are < 24h old (not enough signal)
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- For skills < 300 tokens (over-optimization risk)
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Contributions that turn this reference into a portable integration should add
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tests and update all three documents together.
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