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概要と使いどころ
Captures and evolves user preferences with confidence levels. Maintains Voice of the Customer model with exact language, dissatisfaction and aspiration signals. Handles promotion/demotion, global vs project scoping, auto memory system, and Omi wearable data integration.
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。
Every delivered app — the non-inferable business requirements + exact stack/brand choices every build must satisfy. Loads on every app/site build.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
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。
Drain the Pharos Yield Intelligence coverage-audit queue into reviewed source coverage, documented intentional gaps, or watchlisted deferrals. Use monthly after `yield-coverage-audit` runs, after yield coverage drops, or when promoting queue candidates such as `native-exact-pool`, `lending-allowlist`, `source-family-adapter`, or `stale-auto-lending-override`.
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。
Upgrade Prisma Next in your extension. Bumps every `@prisma-next/*` dependency to the requested target (or npm `latest`), runs the per-transition upgrade instructions for the extension SPI (middleware lifecycle, codec / migration-tools / framework-components churn, seed-migration on-disk shape), verifies the pins are correctly exact via `prisma-next-check-pins`, runs the extension's own typecheck and tests, and commits each minor step on its own. Use when the user asks to "upgrade Prisma Next" in an extension package, or to update an extension's `@prisma-next/*` deps to a new minor.
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。
Use when writing or editing Storybook MDX docs (*.mdx) — the two-tab Overview/Implementation structure, story-backed `<Source>` examples, and doc table guidelines.
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。
Batch wrapper for /gaia-ingest. Ingests a bounded set of already-verified evidence rows through CLI-only writes, uses --no-build on every row, appraises Trust Magnitude for every affected named skill, then runs exactly one build and validation pass. Use for an L4-approved intake after /ev-pipeline; benchmark-result rows also require a Phase 2B report and explicit human gate approval.
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。
Pick up one bounded Gaia Steward maintenance dispatch, do exactly it, and hand the result back for independent verification. Use when the user says "check the steward lane", "pick up maintenance work", "is there anything for me to fix", "run the steward lane", "/gaia-steward-lane", or when a scheduled routine wakes you with no other instruction. Also use before starting speculative repository cleanup: if Steward has not found the work, the work probably does not exist.
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。
Lays a music bed under a finished video — the music is trimmed and faded to the video's exact length and ducks beneath the voice, while the video stream is copied untouched. Use when the user asks to add music, add a background track, add a music bed or soundtrack, or asks for something like "make a lofi mix of this": "put music under this ad", "add a background track", "give me a couple of music versions". Entered from a finished cut — captions already burned is the preferred input, not a problem. It never edits the picture and never changes the video's length.
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。
Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.
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。
Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. Activates for requests to hash a sample, compute imphash, or fingerprint files for similarity clustering.
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。
Analyzes legacy Excel 4.0 (XLM) macro malware by parsing extracted macro-sheet formulas for auto-executing names, obfuscation (FORMULA.FILL, CHAR concatenation), and download or execution primitives (EXEC, CALL, REGISTER). Activates for requests to analyze XLM macros, examine Excel 4.0 macro sheets, or deobfuscate legacy spreadsheet macros.
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。
Analyzes malicious and vulnerable Windows kernel drivers (.sys) by parsing the PE for the native subsystem, identifying DriverEntry/IRP dispatch and IOCTL handlers, and flagging BYOVD and kernel-callback abuse. Activates for requests to analyze a Windows driver, examine a .sys sample, or assess a BYOVD/kernel driver threat.
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。
Analyzes bootkit and rootkit samples by identifying boot-process tampering (MBR/VBR/ UEFI), kernel-mode components, and stealth hooking techniques from static indicators. Activates for requests to analyze a bootkit or rootkit, examine MBR/UEFI tampering, or identify kernel-mode stealth components.
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。
Pulp musical/media time primitives, exact beat divisions, tempo and meter maps, transport-range grid projection, inline and order-preserving groove projection, coordinate randomness, streaming cursors, and quantization arithmetic.
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。
Update Pulp's pinned Skia, Dawn, and optional V8 prebuilts as one milestone-matched render-toolchain release. Use for requests such as "update Skia/Dawn", "move to chrome/mNNN", "find the matching V8", "update the GPU toolchain", or "use the Skia/Dawn/V8 tuple that goes together". Distinguishes milestone-matched V8 releases from v8-builder's newer weekly LKGR releases, verifies exact upstream provenance and asset hashes, updates every Pulp mirror, and runs the provider-identity/ODR gates.
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。
Work in Pulp's bounded JSFX lane using source-only examples, subset validation, and explicit exclusions like no `@gfx`.
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。
How to write source comments, doc comments, and test names/tags that have lasting value — and what to never write. Consult before adding or editing comments in core/**, examples/**, tools/**, test/**, docs/**, or any shipped source. Handles "write a comment", "is this comment ok", "comment hygiene", and keeping new code from adding the breadcrumbs the cleanup stream removes.
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。
Route repository-qualified changes across Pulp and Vellum using Pulp's exact ownership projection. Use when a change touches design import, Chromium authoring, DesignIR, visual harness, screenshot, Skia/Dawn, runtime assets, or another path represented by `.github/vellum-ownership.json`; when deciding whether work originates in Pulp or Vellum; or when validating the cross-repository routing contract.
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。
Research libraries and APIs automatically by pulling live documentation into the agent's context before answering. Use when writing code against unfamiliar or fast-moving libraries so examples match current versions.
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。
Clean/hexagonal architecture: dependency rules, ports and adapters, layering, and testable boundaries. Use when structuring applications for maintainability and testability.
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。
Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.
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 an existing mathematical, logical, algorithmic, or technical proof from its stated conclusion backward as a bounded AND/OR graph of exact obligations. Use to reconstruct conclusion dependencies, determine whether a proof-supplied route establishes the conclusion, or expose hidden assumptions, unmet side conditions, cycles, overly strong steps, or silent route switches. For a source-mapped Lamport rendering use convert-lamport, and for forward hierarchy, construct, and scope conformance use forward-lamport before this skill. This does not decide full theorem-level entailment by searching for a different proof; do not turn the audit into a linear reversed proof, exhaustive proof search, or replacement proof.
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。
N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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。