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MILD and WBTB protocol implementation for Lantern OS. Practical techniques, intention generators, reality-check scaffolding, and scheduling helpers. Designed to increase dream lucidity scores that feed the Dream Journal skill and Bayesian World Model.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Cut a full Unisona release — fold the changelog, bump the version, tag it, let CI build and publish the website + the native Windows desktop app (Unisona-Setup .exe), then rewrite the GitHub Release post for humans with the live URL, the .exe download, a features list, and current pricing. Use whenever the user types `/release` or `!release`, or asks to "cut a release", "ship a version", "release the website and exe", "tag a release", "publish a new version", "make a GitHub release", "do a release post", or "bump and release". Trigger even if they don't say "release" — any request to tag a version and publish a GitHub release with the app + download is this skill. Do NOT use it to hot-deploy master to GCE without a version (that's the deploy runbook), to merge one PR, or to only bump the changelog.
Build and use the single flat Lantern OS RAG dollhouse from local repos, GitHub repo metadata, literal COMET LEAP PDFs, 30-day art images, charts, convergence manifests, and future intake rules. Use when Codex needs a flat RAG-ready file, literal PDF/image assets in a skill, cross-repo Lantern/COMET LEAP consolidation, or a mass-clone/intake plan.
One universal, Σ₀-grounded research capability — fan-out web search + local arXiv-corpus grounding (post-cutoff AI papers, cited by id) + on-demand full-paper fetch, run as persisted, resumable rounds (Observe→Reason→Verify→Converge). Powers chat `!research`, plain-language "research/investigate X", `!convergance` grounding, and autowork issue research. Use whenever the user wants something researched, looked into, grounded in current sources, or wants the actual text of a paper.
Review past benchmark runs and plan the next ones for the local model (Ouro / Σ₀ coder) — per-benchmark leaderboard tables placing the local stack against web-validated public SOTA on HumanEval, MBPP, SWE-bench, LongMemEval and honesty/HaluEval evals, plus a prioritized "run next" plan. Use whenever the user types `/benchmarks` or `!benchmarks`, or asks to "check the benchmarks", "how's the local model doing on evals", "plan the next benchmark runs", "where do we stand on HumanEval/SWE-bench/HaluEval", "compare Ouro to the council", "what should we benchmark next", or "benchmark the local model". Trigger even when the user names only one benchmark (e.g. "check our SWE-bench number") — reviewing one mark in the context of the whole ledger is this skill. Do NOT use it to grade the whole app (that's report-card), to review a PR diff (that's code-review), or to actually train/serve a model.
Groom the Keystone OS GitHub backlog to the agile Definition-of-Ready bar — route every open issue to a dev lane (kriskin / mookman / alex), set its milestone, set its priority (p0/p1/p2), fill its type, flag thin issues needs-triage, and set its assignee, then report what changed. Use this whenever the user types `/refinement`, or asks to "refine the backlog", "groom the backlog", "triage the issues", "prioritize the backlog", "assign lanes", "label the backlog by lane", "set milestones on the issues", "assign the issues to kriskin/mookman/alex", "sort the backlog into lanes", or otherwise wants open issues distributed across the three contributor lanes with labels, milestones, priorities, and assignees. Trigger even when the user doesn't say "refinement" — any request to organize/distribute/triage/prioritize the open GitHub issues is this skill. Do NOT use it to actually write code for an issue (that's autowork), to grade the app (that's report-card), or to review a single PR diff (that's code-review).
Produce an honest, evidence-grounded letter-grade report card for Keystone OS as it actually is right now — graded dimensions, real evidence per row, an overall grade, and a candid one-paragraph summary. Use this whenever the user types `!report-card`, or asks to "grade the app", "evaluate the app as it is now", "how are we doing", "give me an honest assessment", "where do we stand", "what's the state of the project", or otherwise wants a frank, no-flattery scorecard of the system's current quality. Trigger even when the user doesn't say the words "report card" — any request for a candid overall grade of the app or project is this skill. Do NOT use it for grading a single PR diff (that's a code review) or for a roadmap/planning ask.
| name | lucid-dreaming |
| description | MILD and WBTB protocol implementation for Lantern OS. Practical techniques, intention generators, reality-check scaffolding, and scheduling helpers. Designed to increase dream lucidity scores that feed the Dream Journal skill and Bayesian World Model. |
Status: active development (codex/dream-journal-structured)
Scope: operator personal practice tools for lucid dream induction; integration points for Dream Journal and daily templates
Source: classic LaBerge MILD research + WBTB field reports, adapted to Lantern OS evidence and flourishing frame
Validation: module loads cleanly; protocols expressed as executable generators + documentation
A focused skill module that gives you concrete, repeatable MILD (Mnemonic Induction of Lucid Dreams) and WBTB (Wake Back To Bed) procedures. It generates personalized intention statements, reality-check prompts, and timing guidance that increase the probability of lucidity. Results (lucidity scores, dream reports) flow naturally into skills/dream_journal/.
generate_mild_intention(last_dream_summary, personal_goals) — Produces a concise, present-tense mnemonic phrase for use during the WBTB window and at sleep onset.wbtb_schedule(sleep_time, target_wake_hours=5.5) — Calculates recommended wake window, activity suggestions (reading dream journal, light movement, no screens), and return-to-bed timing.reality_check_prompts(count=5) — Supplies varied, non-obtrusive reality checks (text, time, hands, environment, breathing) suitable for daytime practice and dream use.daily_lucid_ritual_template() — Returns a minimal daily structure (morning recall, daytime RC practice, evening intention setting) that can be dropped into templates/daily/./dream commands, data/dreamer/notebooks/) and the new structured dream_journal module.Proven:
solo-mining/examples/ and other skills.Held (explicit boundaries):
Local-only:
mild_wbtb_protocol.py and run its __main__ demo.generate_mild_intention with a recent dream from your journal.dream_journal.get_recent() + Bayesian World Model if wired.python -m skills.lucid_dreaming.mild_wbtb_protocoltests/skills/test_lucid_dreaming.py with property-based checks on intention length and schedule sanity.Classic MILD core (LaBerge): While falling asleep, repeat a short phrase that includes the critical state ("Next time I’m dreaming, I will remember I’m dreaming") while vividly imagining a recent dream in which you became lucid.
WBTB core: After 5–6 hours of sleep, wake for 20–60 minutes. Stay upright, read dream journal or this skill’s output, perform gentle reality checks, then return to bed with fresh MILD intention.
See mild_wbtb_protocol.py for the exact generators used in this skill.
Related skills: dream-journal, bayesian-world-model.