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Cuts the next release of the unified Prisma CLI: bumps the root package.json version (on the Prisma 8 RC line: 8.0.0-rc.N → rc.N+1), propagates it to every lockstep workspace package, and opens a PR titled "chore(release): bump to <next-version>". When the maintainer merges the PR, the `Publish to npm` workflow ships @prisma/cli-engine and @prisma/cli under dist-tag `latest` (RC versions get a pre-release GitHub Release). Use when a maintainer asks to "cut the next RC", "cut the next release", "bump to the next version", "open a release PR", or "prepare a publish PR".

日本語の概要は準備中です。原文の説明を表示しています。

Adopt Prisma Next into a new project, onto an existing database, or as the first move after a bootstrap tool dropped you into a scaffold. Use for "what can I do with Prisma Next", "what can I do next with Prisma", "where do I start", "what should I do first", "just ran createprisma", "createprisma", "npx createprisma", "npx create-prisma", "first steps", "first query", "I have a scaffolded Prisma Next project what now"; for `pnpm dlx prisma-next init` greenfield setup; and for `prisma-next contract infer` + `db sign` against an existing database. Also covers the connect-write-read first-arc orientation, the day-to-day commands (`contract emit`, `db init`, `db update`, `migration plan`, `migrate`, `db schema`, `db verify`), and routing to `prisma-next-contract` / `prisma-next-queries` / `prisma-next-runtime` for the next move. Flags: --target, --authoring, --schema-path, --probe-db, --output.

日本語の概要は準備中です。原文の説明を表示しています。

Turn raw Lumen release content (a dump of the LedgerHQ/lumen GitHub Releases page) into one minimalist Slack announcement, drafted to #lumen-releases. Use whenever the user pastes one or more `@ledgerhq/lumen-*` package releases, a chunk of the Releases page, or asks to "announce", "format", "post", or "write release notes / a Slack message" for new Lumen versions. Trigger it even when the user just drops the raw release text with no instructions, or says things like "new Lumen release", "format these lumen versions for Slack", or "lumen changelog for the channel".

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Audit and repair Gaia user-tree timelines so every skill's current rank is explained by a chain of Hero's Journey events. Trigger this skill when: a contributor's profile chart shows a stale rank (e.g. still shows 3★ after a demotion to 1★); someone asks to "trace what happened" to a skill's rank; `validate_timelines.py` is failing in CI; you need to backfill missing `demote` or `rank_up` events before cutting a release; a skill was reclassified or Star-Bar-reset and the user tree was never updated; or any time the registry node and user tree disagree on a skill's current level. Uses `scripts/trace_timeline.py` (dry-run + apply) and `scripts/validate_timelines.py` (CI gate). Do NOT hand-edit `timeline[]` arrays — the script synthesises properly labelled events with `previousValue`/`newValue` so the rank chart plots correctly.

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Inspect or exercise the paused gaia curate orchestration scaffold for one source URL. The command currently persists an INITIALIZED run ledger and reports status; its 25 named workflow states and transition methods are a design skeleton, not an implemented automated curation or merge pipeline. Use only for scaffold development, status inspection, or dry-run contract work. For live curation, use gaia-full-pipeline or gaia-consult. Trigger phrases: "/gaia-quick-curate", "inspect gaia curate", "curate scaffold", "quick curate status". For batch/multi-page runs use gaia-curate-dynamic; for crawler branches use gaia-bot-curate; for pending intake proposals use gaia-draft-curate.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-agent exploration-to-PR pipeline for safely exploring, fixing, and shipping a feature or CLI. Use when the user says: "explore X", "test and ship X", "run the pipeline on X", "run /feature-pipeline on X", "let's properly investigate X", "stress-test X and file issues", or "take X through the full pipeline." Also triggers on: "find what's broken in X", "test X end-to-end", "do a sandbox review of X", "file issues for X and fix them." Do NOT use for single-file bug fixes or one-off edits — the overhead is only justified when exploration, planning approval, and CI watching all matter. Manages state via scripts/state.sh (jq required). Five phases: LIGHTER explores → ORCHESTRATOR drafts PR → HEAVIER plans (user approves first) → ORCHESTRATOR commits → HEAVIER sandbox review → LIGHTER iterates CI → LIGHTER drift check + summary. Four mandatory stop hooks (M1–M4) gate each phase transition.

日本語の概要は準備中です。原文の説明を表示しています。

Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.

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Atomically ingest one already-verified evidence row into one named Gaia skill using the CLI, then appraise Trust Magnitude and propose—never silently apply—a calibration. Use after /ev-pipeline has verified an approved intake source, or whenever a curator says "ingest evidence", "add verified evidence", "gaia-ingest", or "turn this source into registry evidence".

日本語の概要は準備中です。原文の説明を表示しています。

Gaia Skill Tree image-generation and deterministic export harness for Ascension Overdrive v3. Use when generating, iterating, refining, cropping, color-grading, making transparent cutouts, compressing, upscaling, or validating production image assets. Always use image gen 2 / gpt-image-2 and never nano-banana.

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End-to-end plan → implement → review pipeline that takes one triaged Gaia issue to a merged-ready PR, with Kill Criteria on the issue and zero follow-up debt. Use when someone says: "resolve this issue", "take #N end-to-end", "work the P0 queue", "fix and ship this issue", "run the resolver", "close out this issue properly", or /gaia-issue-resolver. Works any gaia-research org repo — gaia-skill-tree, gaia-research, gaia-skill-heaven — while the issue itself lives on the headquarters tracker, gaia-research/gaia-skill-tree. Assumes the issue is already triaged and carries a P0–P4 label; if it does not, run /gaia-triage first.

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Builds a stylized 3D animated ad on the Novoads REST API as a STORYBOARD: four to six beats, each rendered from its own key frame, narration laid into the gaps, a music bed, captions burned on, assembled locally with ffmpeg. Carries the genre's beat formulas (anthropomorphized problem character, protagonist reveal, mascot mechanism scene, composited end card). Use for ANY Pixar-style, Disney-style, 3D animated or animated-film-look ad at ANY length: "Pixar style ad", "3D animated ad", "animated movie look", "cute 3D character ad", "a quick animated ad", "15 second animated ad", "30 second animated ad", "storyboard ad", "several scenes", "animated ad with a voice-over", "little mascots", or a product URL or photo for an emotional animated spot. No one-call tier, so ffmpeg is required. Not for stop-motion clay (use claymation-ad), talking-head UGC (novoads-api), video cloning, or static image ads.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze a reference video and reverse-engineer its style into a reusable Seedance 2.0 prompting template for the Novoads API. The output is a new formula file — like seedance-2-ugc.md — that captures the video's structure, pacing, camera work, edit style and tone so it can be recreated with any product, any person, in any setting. Use this whenever someone provides a video they want to use as a style reference, says "I want to make videos like this", "deconstruct this video", "turn this into a template", "analyze this style", or drops a video file and wants to recreate that format repeatedly.

日本語の概要は準備中です。原文の説明を表示しています。

Audit essays, reports, literature reviews, dissertations, proposals, and source-backed assignments for high-score academic quality. Use when the user asks whether a paper can get a high grade, wants teacher/rubric feedback addressed, needs a pre-submission review, or needs checks for strict rubric compliance, academic standards, prompt/rubric alignment, argument quality, comparative analysis, evidence hierarchy, table/figure integration, citation specificity, conclusion strength, academic language, source-claim integrity, or evidence boundaries.

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Execute a project from a folder of raw materials. Use when the user drops messages, screenshots, documents, drafts, and references into a project directory and wants Codex to identify the real goal, build a plan, do the work, drive the project to a submission-ready state, verify completion, and return a clear final summary of what was required, what was completed, and how it was done.

日本語の概要は準備中です。原文の説明を表示しています。

Plan a feature before writing any code. Use this skill at the START of non-trivial feature work — when the user says they want to "add", "build", "implement", "create", "develop", or "ship" a feature, endpoint, component, page, service, or capability. Especially trigger when the user's prompt is short or under-specified ("add a dashboard", "build an auth flow", "let's do user activity reports") — those need planning, not direct execution. The skill walks through requirements clarification, codebase exploration, sketches a Mermaid architecture/flow diagram of the proposed design, and produces a plan.md document the user can hand off to a fresh AI session for execution. Skip only when the change is genuinely trivial (typo fixes, single-line tweaks, renames). When in doubt, plan.

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Use when the user wants to be taught an Anthropic product or feature - "teach me Claude Code", "learn Cowork", "academy lesson", "train me on MCP or the Claude API", "quiz me", "next lesson" - or asks for structured learning rather than a one-off answer. Covers Claude Code, Claude.ai, Cowork, Tag, Platform/API, and AI Fluency. Do NOT use for auditing how the user already works (use academy-review) or for mid-task help where the user just wants their task done.

日本語の概要は準備中です。原文の説明を表示しています。

Create a new component in the Modules folder from template with proper documentation and placeholders replacement.

日本語の概要は準備中です。原文の説明を表示しています。

Reverses proprietary command-and-control protocols: locating send/recv routines, recovering the message framing and encryption/encoding, and reconstructing the command set to build a decoder or emulator. Activates for requests to reverse a custom C2 protocol, decode beacon traffic, or document a malware command structure.

日本語の概要は準備中です。原文の説明を表示しています。

Manually unpacks a runtime-packed Windows binary by finding the original entry point (OEP) through tail-jump and entropy analysis, then guiding a memory dump at OEP. Activates for requests to manually unpack a sample, find the OEP, or unpack a custom/unknown packer that generic tools cannot handle.

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Analyzes malicious Office documents by extracting and reviewing VBA macros and OLE/OOXML structure: auto-exec triggers, obfuscation, dropped payloads, and shell/PowerShell invocation. Activates for requests to analyze a malicious Word/Excel document, VBA macro, or maldoc.

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Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

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Statically analyzes Linux ELF malware: ELF header and sections, dynamic symbols and imports, segment permissions, embedded strings, and packing indicators to infer capability without execution. Activates for requests to analyze an ELF binary, Linux malware, or shared object.

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Turn a moment of friction — a conflicting PR, a wedged runner, a mysterious red check, a repeated manual chore — into a durable, actionable report. Sizes up the scenario, captures the evidence, separates one-off from SYSTEMIC, proposes a fix, and routes it (fix now, or hand off). Reports are LIVING documents that the fixing agent updates with the outcome. Invoke whenever something feels like it shouldn't have been this hard, or when the same annoyance shows up twice.

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Style React with Emotion: css prop, styled API, theming, SSR, and zero-config patterns. Use when choosing or using Emotion for CSS-in-JS.

日本語の概要は準備中です。原文の説明を表示しています。