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概要と使いどころ
Design, analyze, and document A/B tests for conversion, onboarding, pricing, lifecycle, and product experiments. Use when the user asks for `/ab-test-setup`, experiment design, sample size, statistical significance, A/B test analysis, ICE-scored test backlogs, or avoiding common testing mistakes.
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Review contracts with clause-by-clause risk scoring, market benchmarks, negotiability ratings, and redline suggestions. Use when the user pastes an NDA, SaaS/MSA, M&A LOI, or payment agreement and wants a Contract Safety Score, CUAD-based risk breakdown, or /review as [position] analysis.
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。
Validates and scores SKILL.md files against the agentskills.io specification; use when linting skills for cross-agent compatibility, description quality, or capability graph 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。
Broad, pre-curation evidence reconnaissance for a declared Gaia skill, intake batch, upstream repository, or proposed generic capability. Run before choosing a discovery strategy or mapping topology, to give the curator independent research leads and counterevidence. Context only: never scores, grades, ratifies, ingests, or mutates the registry. Later ev-discovery and ev-pipeline phases validate/promote any selected leads.
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Use when hosting, deploying, or operating an app on the Prisma Platform: projects, branches, preview environments, services and their versions, Prisma Postgres databases, object-store buckets, environment variables, custom domains, logs, or the GitHub integration. Triggers on "Prisma Platform", "Prisma Compute", "Prisma Postgres", "Prisma Storage", "preview environment", `prisma deploy`, `prisma dev`, `prisma auth`, `prisma project`, `prisma branch`, `prisma service`, `prisma postgres`, `prisma bucket`, `prisma git`, `prisma.config.ts`, promote, rollback.
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 designing or changing a component's public API, its composition, or its state model in libs/ui-react or libs/ui-rnative — layering (core vs internal vs primitives), BaseProps/Props splits, converting a component to compound / changing its composition with createSafeContext, controlled/uncontrolled state, prop-naming conventions, and cross-platform API parity. Load this before shaping a new component, changing its props, or refactoring its composition.
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。
Review a PR or the current branch for a Lumen design-system change — routes each changed file to React (web/Tailwind) or React Native (useStyleSheet) guidelines, checks the Nx version plan, and reports a flat, severity-scored finding list. Use when the user asks to review a PR, review code changes, check a branch, do a local code review, or provides a GitHub PR URL.
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。
Dry-run Trust Magnitude for proposed named skills or suites before curation. Use when deciding whether a proposed suite deserves A/S treatment, when checking Fusion Score structural inputs, when comparing repo stars vs suite component counts, or when asked to appraise a candidate before adding it to the registry.
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。
Post-review intake closing skill. After an intake is resolved (whether accepted via review/meta PR merge or rejected/deferred at triage/evidence review), run this skill to post standardized, personalized closing comments on the PR (if any) and each intake issue. Comments include: evidence pipeline findings with /trust-appraise TM output, per-row artifact scores, decisions rationale, path-to-promotion/acceptance guidance, and a mandatory badge status note including the universal Powered by Gaia badge. Use after curation completes, before or immediately after merge for accepted intakes, or immediately upon rejection.
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.
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。
Load, run, and test VST3 / AU / CLAP / LV2 plugins from Pulp code. Use when working on `core/host/` (scanner, plugin_slot, signal_graph), when adding a new format backend, when wiring a plugin into a SignalGraph, or when writing an integration test that needs a real plug-in binary.
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。
Capture faithful PNGs of Pulp view trees / imported UIs headlessly. Covers render_to_png backends (Skia vs CoreGraphics), the image-compositing gotcha, capture_png from a live GPU host, and the --screenshot-backend validate flag. Use whenever you render a UI to a PNG to eyeball or montage it.
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.
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Prepare an outside contribution to Pulp or Forge that a maintainer can land with minimal rework — routing (Core vs Forge), local build and test on a plain Mac, the checks that are worth running without Shipyard/Tart/VMs, and the handoff format. Use when contributing without write access or without the maintainer CI fleet.
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。
iOS platform development for Pulp — iPhone/iPad AUv3 app extensions, iOS Simulator builds, UIKit window host, CoreAudio IO audio, touch & Apple Pencil input, XcodeBuildMCP automation. Covers configure, build, deploy to simulator/device, and the gotchas discovered during iOS bringup.
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。
Keep a plugin/demo repo's screenshots in sync with its UX. When editor / UI / design-token code changes in an opted-in repo (one carrying .pulp/screenshots.toml), re-capture and refresh every place each shot is consumed — README images, web-demo Open Graph (og.png) images, and gallery thumbnails — consistently across the WAM and WCLAP demo sites. Two capture routes: native headless (render_to_png / pulp-screenshot, Skia) for README / native-editor shots, and live-web headless Chrome (playwright-core) for WAM/WCLAP demo OG + gallery images. Backfills demos missing OG images. This is a PORTING / consistency tool — it re-shoots a repo's OWN UI, it never clones another product's art.
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。
Query, recommend, and switch the Pulp JS engine backend (QuickJS, JavaScriptCore, V8). Handles "which JS engine", "switch to V8", "engine for Three.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。
Entry point for documentation skills covering Upstash Search quick starts, core concepts, and TypeScript SDK usage. Use when a user asks how to get started, how indexing works, or how to use the TS client.
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。
Google Cloud guidance — core services, IAM, VPC networking, GKE/Cloud Run choices, BigQuery, and cost control.
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。
AWS guidance — core services, IAM least privilege, networking, cost control, and production architecture patterns.
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。
Writing abstracts that get papers read — structured content, the 5-sentence core, and journal-specific constraints.
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。
Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.
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。
Comprehensive social media operations for B2B SaaS brand building and engagement. Use this skill to develop LinkedIn strategy, decide whether to run a LinkedIn newsletter and how to scope it, capture consented registrations from LinkedIn Events, check LinkedIn Live eligibility, manage Twitter/X presence, create YouTube content, engage on Reddit communities, decide whether and how to answer on Quora, build community, develop influencer and creator partnerships, vet a creator before paying them, start or fix a B2B podcast, book podcast guests and guest appearances, judge whether a podcast is worth appearing on when you cannot audit its numbers, write show notes and transcripts, run social listening (what are people saying about us, share of voice, brand monitoring, validated sentiment, alert thresholds, routing signals to the right owner), and execute organic social campaigns. Also triggers on: LinkedIn, LinkedIn newsletter, LinkedIn Events, LinkedIn Live, event registration form, subscribe to our newsletter on LinkedIn, marketing consent checkbox, Notify Employees, Twitter/X, YouTube, Reddit, community, influencer, creator partnerships, influencer vetting, engagement rate, fake followers, engagement pods, FTC disclosure, sponsored post disclosure, media kit, social media strategy, organic social, employee advocacy, podcast, podcast strategy, podcast guesting, how do I know if this podcast has real listeners, the host says they get 50k downloads, is this podcast worth going on, podcast RSS feed research, IAB podcast measurement certification, Listen Score, Apple Podcasts reviews, show notes, audiogram, episode transcript, social media account access, who has admin on our LinkedIn page, the person who ran our social accounts left, locked out of our company account, social media offboarding, YouTube channel owner, Slack primary owner, social account MFA, social media approval workflow, pause all scheduled posts, social listening, what are people saying about us, brand monitoring, brand mentions, share of voice, social share of voice vs competitors, sentiment analysis, is our sentiment dashboard accurate, listening query, boolean query, mention alerts, spike alert, CrowdTangle replacement, Reddit API access, which platforms can our listening tool see, someone on Reddit asked for a tool like ours, comment mining, voice of customer from social.
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。
Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.
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。