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概要と使いどころ
Full-spectrum product research pipeline. Runs competitive analysis, technology trend scouting, user feedback analysis, and feature ideation. Trigger on: research, competitive research, market research, feature discovery, what should we build next, technology trends, user feedback, app store reviews, GitHub issues analysis, competitive landscape, product strategy.
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。
XSS attack prevention with input sanitization, output encoding, Content Security Policy. Use for user-generated content, rich text editors, web application security, or encountering stored XSS, reflected XSS, DOM manipulation, script injection errors.
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。
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
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。
Write outbound promotional copy for a product or project: launch and update posts for community platforms and social media, store page descriptions and short blurbs, landing page headlines and calls to action, press-style announcements, and the naming of a product for another language or market. Covers what may be disclosed publicly, keeping claims traceable to shipped changes, and writing a headline that carries information instead of hype. Use when drafting or revising anything aimed at people who do not use the product yet, and when deciding whether a link, key, price, or unreleased detail can appear in public copy.
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 software repository at the latest remote main commit and turn its implemented architecture into a citation-backed interactive isometric system map with a legend, selectable infrastructure buildings, dependency and payload flows, and plain-language learner explanations. For eligible public GitHub repositories, also contribute the verified map to tamdogood/CodeTerrain with a pull request. Use when a user provides a repository URL or asks to visualize, explore, learn, explain, or map a repo's architecture, infrastructure, runtime control flow, data flow, services, queues, stores, external systems, or deployment topology in an interactive UI.
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。
Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.
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。
Clear stored Woz credentials and log out.
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。
Think and work like an expert MLOps Engineer. Use when a task calls for MLOps Engineer judgment. Reasons from data contracts, feature parity, evaluation gates, and rollback-readiness through MLflow/W&B registries, Feast feature stores, KServe/Triton serving, Great Expectations/TFDV validation, and Evidently PSI/KS drift monitors while treating train-serve skew, data leakage, silent degradation, and schema/concept drift as first-class failure modes.
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。
Operational KPI tracking, headcount trends, utilization rates, backlog analysis, book-to-bill ratio, operational efficiency metrics, capacity utilization, productivity metrics, operational leverage, same-store sales
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。
Full enhancement pipeline for an existing Lattice skill — rewrites a molecule or atom to modern-capable-model grade: restores degraded grammar, deduplicates, hardens gates and branching to one-unambiguous-action precision, verifies zero behavioral loss against a pre-capture diff, then runs the combined QA gate (skill-review, skill-tighten, skill-validate) and an independent fresh-context verifier, fixing everything found. Consumer-first: optimized for first-time open-source users on fresh repos with minimal .lattice/ setup, running capable models. Use when the user says 'enhance this skill', 'upgrade this molecule', 'modernize this skill', 'optimize this skill', 'make this skill production grade', 'full enhancement', 'polish this molecule like design-blueprint', or names a molecule/atom to overhaul. For gap-finding only use skill-review; for conventions only use skill-validate — this skill runs the whole loop end to end.
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 this skill when an agent needs production image or video understanding with Amazon Rekognition: labels, objects, scenes, OCR, moderation, image properties, Custom Labels or moderation adapters, stored-video analysis, conditional streaming-video workflows for existing eligible accounts, searchable media libraries, confidence evaluation, S3/IAM/event architecture, privacy, biometric consent, cost control, lifecycle management, and QA.
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。
Internal — for Boundless team members only. Query Boundless broker telemetry tables on Redshift for prod/staging operational data. Use when the user asks about broker health, request evaluations, request completions, proving times, skip rates, telemetry data, or wants to run SQL against the telemetry database on live networks. Also covers historical telemetry through 2026-04-24 stored as Parquet archives in S3 (queried via DuckDB). Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.
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。
Symfony UX Map for interactive maps with Leaflet or Google Maps in Symfony. Covers markers, polygons, polylines, circles, info windows, and LiveComponent integration. Use when displaying maps, placing markers, drawing shapes or routes, handling map events, building store locators, using custom tile layers, or making maps reactive with LiveComponent. Code triggers: <twig:ux:map />, Map(), Point(), Marker(), Polygon(), Polyline(), Circle(), InfoWindow(), MapOptionsInterface, ComponentWithMapTrait, fitBoundsToMarkers, ux:map:marker:before-create, ux:map:connect, SYMFONY_UX_MAP_DSN. Also trigger when the user asks "how to display a map", "how to add markers", "how to draw a polygon on a map", "how to handle map click events", "how to make a reactive map", "how to use Leaflet in Symfony", "how to use Google Maps in Symfony", "map not showing", "map has zero height". Do NOT trigger for SVG icons (use ux-icons) or general frontend interactivity (use stimulus).
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。
Mine AI coding-agent session transcripts into structured, gate-validated GPU-kernel optimization records for the wiki. Use when asked to turn vibe-coding sessions, Codex rollout logs, or Claude Code project transcripts into wiki records; to summarise what a kernel-optimization session achieved; to build or extend a session-trace store; or to re-run and validate one. Also use when asked how a session-derived record's number, snippet, or provenance was established.
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。
Mine a per-kernel optimization trace — a git repository capturing successive versions of one kernel being optimized — into structured, gate-validated optimization-experience records for the GPU kernel wiki. Use when asked to distill an optimization run, a kernel_opt trace directory or a version ladder into wiki records; to report what such a run actually achieved; to build, extend, re-run or validate the staging store behind those records; or to explain how a trace-derived record's number, snippet or provenance was established.
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。
Quickly extract a typed GPU-Wiki query intent from prose. Never inspect or query a knowledge store.
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 for 'why does X work this way', 'why we picked Y', '为什么这么设计', design rationale, regressions, or where a magic number came from. Fans out one investigator per evidence category — in this repo that starts with the local decision record (ADRs, docs/design, the daemon issue store, the changelog, wisp) plus git history, and adds MCP-backed categories when the session has them — then returns a cited read with the gaps named. Use `how` for what the code does at runtime.
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 a rough idea, a bug, or a batch of unsolved problems into well-structured GitHub issue(s) and file them with `gh`, auto-classifying type + labels from the content (recommend, then confirm) and following Rove's conventions (beginner-friendly framing, concrete file pointers, an acceptance checklist, zero AI/Anthropic attribution). Handles single, batch, and "file the unsolved problems" modes. Use when the user says "file an issue", "open a GitHub issue", "draft a good first issue", "batch these as issues", "上报 issue", "提个 issue", "报个 bug", "把没解决的发上去", or wants to hand low-priority tasks to outside contributors. Distinct from the daemon-owned internal issue store (`rove api issue-*`); this skill is for outward-facing GitHub issues.
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。
Dokku is an open-source, single-server platform-as-a-service: it builds an app from a git push (buildpacks or a Dockerfile), runs it in Docker containers behind nginx, and manages domains, TLS, config and datastores through one CLI. Use when a user asks to "self-host a Heroku alternative", "set up git push deploys on a VPS", "deploy my app with Dokku", "add Postgres or Redis to a Dokku app", "get Let's Encrypt on Dokku", or "fix ports for a Dockerfile app on Dokku". Covers installation from the apt repository, deploys, environment variables, plugins, zero-downtime checks, storage and the k3s scheduler.
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。
Implement GDPR and ePrivacy-compliant cookie consent: a banner with equal Accept and Reject buttons, per-category choices, stored consent records, consent-gated analytics with Google Consent Mode v2, and Global Privacy Control. Use when adding a cookie banner, managing marketing consent, achieving GDPR compliance for EU users, or integrating consent with analytics platforms.
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。
Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, vector database, embeddings, semantic search, similarity search, vector store, rag.
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。
Braintrust is a hosted platform for evaluating and tracing LLM applications: it runs a task over a dataset, scores the outputs, stores each run as an experiment and logs production traces. Use when a user asks to write or run a Braintrust eval, score LLM outputs with autoevals, compare prompt or model versions as experiments, run evals in CI and fail on regressions, trace OpenAI or Anthropic calls with Braintrust, manage evaluation datasets, or use the bt CLI.
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。
Tests and debugs APIs with Bruno, the open-source API client that keeps collections as plain files in a Git repository. Use when a user asks to create API requests, organize collections, write test scripts, use environments and variables, run a collection from the terminal or CI with the bru CLI, or collaborate on API workflows stored in Git.
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。
Amazon SES (Simple Email Service) sends and receives email through AWS: transactional and marketing mail, templates, bounce and complaint tracking, and inbound mail processing. Use when a user asks to send email with SES, verify a domain or set up DKIM, move an SES account out of the sandbox, create or bulk-send email templates, add an attachment or unsubscribe header, handle bounces and complaints with SNS, manage the suppression list, or store incoming mail in S3 with receipt rules. Covers the SES v2 API (aws sesv2, boto3 sesv2).
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。