Remove every non-ASCII/CJK character for a professional open-source repo:
- harvest.py: drop hardcoded Chinese feedback phrases; add an env-based
extensibility hook (SKILLOPT_SLEEP_NEG_FEEDBACK / _POS_FEEDBACK) so any
locale can be added without baking one in. Verified with a German example.
- rollout.py / consolidate.py: English comments.
- README.md section heading + anchor, CONTROLLABLE_DREAMING.md, plugin.json,
marketplace.json (also fixed stale path skillopt-sleep-plugin ->
plugins/claude-code), SKILL.md: English only.
- Remove the internal WAKE_UP_SUMMARY.md note (not user-facing, not referenced).
Verified: zero CJK chars remain anywhere; 29 tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Restructure into plugins/{claude-code,codex,copilot}/ — one engine, three thin
shells, all calling the shared plugins/run-sleep.sh -> python -m skillopt_sleep.
- claude-code/: existing plugin moved here; runner delegates to the shared
launcher (fixes repo-root resolution after the move).
- codex/: ~/.codex/prompts/sleep.md custom prompt + ~/.agents/skills SKILL.md +
install.sh + AGENTS.md hint — Codex's documented, stable extension surfaces.
- copilot/: a stdlib-only MCP server (mcp_server.py) exposing sleep_* tools,
plus mcp-config.example.json and a copilot-instructions snippet. Verified end
to end (initialize -> tools/list -> tools/call returns real engine output).
- plugins/README.md overview table; main README News + a dedicated SkillOpt-Sleep
section; pyproject lists skillopt_sleep as a first-class package.
Decoupling emphasized throughout: open-source tool (skillopt_sleep/) with zero
dependency on the research package. 29 tests pass; all three shells resolve.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Add docs/guideline.html, a single self-contained documentation guide
(left-nav + content + on-this-page TOC) covering installation, data
preparation, training/eval, full configuration reference, framework
internals, and an API reference. Link it from the README with local,
htmlpreview, and GitHub Pages access instructions.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Move the soft/mixed gate-metric configuration introduced in PR #25 out of
the base default config and into a standalone example config so that
default SkillOpt runs (and paper reproduction) remain bit-for-bit on the
original hard gate.
- configs/_base_/default.yaml: drop gate_metric / gate_mixed_weight keys.
The trainer's cfg.get("gate_metric", "hard") fallback preserves the
original behavior unchanged.
- configs/examples/soft_gate.yaml: new standalone reference config with
a header explaining when to consider it (small selection split with
continuous rewards) and when not to (paper reproduction, large or
binary-reward settings).
- README.md: add a short "Community-contributed configs" section that
clearly flags this as user-contributed and non-default.
The yaml default `azure_openai_auth_mode: azure_cli` was silently
overwriting `AZURE_OPENAI_AUTH_MODE` exported by the user, because
`configure_clients()` treats any non-empty config value as an explicit
override. Switching the three auth_mode defaults (shared / optimizer /
target) to "" lets `_clean()` drop them and restores the intended
fallback chain: yaml → env var → module default ("azure_cli").
Also update README and .env.example to document the openai_compatible
mode introduced in d5c5b61, and remove the misleading `OPENAI_API_KEY`
snippet — SkillOpt reuses the `AZURE_OPENAI_*` env vars in this mode.
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
