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概要と使いどころ
Detect which external metric sources apply to this product (GitHub, Plausible, Stripe, etc.) and configure adapters. Retrofit entry point for projects that started before v0.14; also runnable to refresh source list when the product grows.
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Use when writing or modifying code for s&box, sbox, the Facepunch sandbox engine, or any Source 2 game project in C#. Triggers on mentions of s&box, sbox, sandbox game, Facepunch engine, Source 2 game, `.sbproj`, `.razor` with `PanelComponent`, `@inherits PanelComponent`, `Sandbox.Component`, `GameObject` + `Components.Get<T>`, `Scene.Trace`, `CharacterController` + `.Move()`, `SkinnedModelRenderer`, `NavMeshAgent`, `[Sync]`, `[Rpc.Broadcast]`, `[Property]`, `INetworkListener`, `ISceneEvent`, `PlayerController` in C#. Also triggers on any file with `using Sandbox;` or `using Sandbox.UI;` that isn't Unity/Godot/Unreal. Writes idiomatic s&box C# components, Razor UI, and networking code; prevents Unity-pattern leakage.
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。
Apply MCAF source-control guidance for repository structure, branch naming, merge strategy, commit hygiene, and secrets-in-git discipline. Use when bootstrapping a repo, tightening PR flow, or documenting branch and release policy.
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Builds mining project financial models with resource estimation, mine plan integration, and commodity price sensitivity analysis. Use when modeling mining investments, analyzing feasibility studies, or evaluating mineral assets.
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Assesses spin-off equity with forced selling dynamics, orphaned security identification, and standalone valuation analysis. Use when evaluating spin-off investments, identifying forced-sell situations, or analyzing newly public entities.
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Evaluates water rights valuation with seniority analysis, regulatory framework assessment, and allocation risk for resource investments. Use when analyzing water rights, evaluating water allocation, or assessing water 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。
Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.
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。
ALIGN for paper figures — draw a method-overview figure as an executable p5.js program that replays its own construction in named stages, exports SVG, and improves through pixel-only cross-model review whose gate is 'explain the method back from the figure alone, and is it worth looking at'. The method sets the relationships the figure must show, the chosen visual identity sets how its marks look, a composition source lends arrangements; the method may be the source's own (意临) or yours in its arrangement (风格迁移). Not for one-shot raster image generation.
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。
ALIGN for paper figures — draw a method-overview figure as an executable p5.js program that replays its own construction in named stages, exports SVG, and improves through pixel-only independent Codex subagent review whose gate is 'explain the method back from the figure alone, and is it worth looking at'. The method sets the relationships the figure must show, the chosen visual identity sets how its marks look, a composition source lends arrangements; the method may be the source's own (意临) or yours in its arrangement (风格迁移). Not for one-shot raster image generation.
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。
Restore legibility to an auto-generated graph diagram that has outgrown its canvas - rendering at single-digit percent scale with sub-pixel labels. Covers the levers that measurably reduce layout units (source-level label wrapping, invisible chain links so unconnected nodes stop spreading into one row, per-cluster splitting, layout direction picked by rendering both and measuring) and the rejected ones (raising the render font is a no-op because layout engines measure in text units; a topology heuristic for direction regressed 13 of 38 diagrams). Use when a generated Mermaid/Graphviz/DOT diagram is too small to read; not for hand-authoring a small diagram.
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。
Guide a person through a Coordinate Remote Viewing (CRV) session following the Stargate/SRI protocol. AI acts as the monitor/tasker role, managing protocol progression, catching Analytical Overlay (AOL), and redirecting the viewer through stages I-VI. Use when a person wants to practice CRV and needs a monitor to manage the session protocol, when training a viewer through the staged CRV process, facilitating a structured intuitive perception exercise, or developing non-local awareness skills that complement healing work.
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。
AI pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.
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Perform capacity planning using historical metrics and growth models. Use predict_linear for forecasting, identify resource constraints, calculate headroom, and recommend scaling actions before saturation. Use before seasonal traffic spikes or product launches, during quarterly capacity reviews, when resource utilization trends upward, or before budget planning cycles.
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。
Generate themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.
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。
Form a coherent gestalt — the whole that is more than the sum of its parts — from the panoramic perception produced by expand-awareness. Maps tensions and resonances between domains, identifies the emergent figure from the ground of multiple perspectives, tests the candidate whole for premature closure, and articulates the insight in a single sentence no single domain could have produced. Use after expand-awareness has surfaced raw multi-domain perception and before express-insight communicates the result.
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 applications to Kubernetes clusters using kubectl manifests for Deployments, Services, ConfigMaps, Secrets, and Ingress resources. Implement health checks, resource limits, rolling updates, and Helm chart packaging for production deployments. Use when deploying new applications to EKS, GKE, AKS, or self-hosted clusters, migrating from Docker Compose to container orchestration, implementing zero-downtime rolling updates, or setting up multi-environment deployments across dev, staging, and production.
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 an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.
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。
Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Documents the hook enforcement system, bypass mechanisms, and common failure patterns to help agents avoid getting stuck on protected path writes
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。
Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.
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 の構成図を draw.io の XML（.drawio）で描く。公式の AWS4 アイコンを使い、タイトルや凡例や番号バッジのような装飾を足さず、どのサービスがどこにあって何とつながっているかだけで読める図にする。AWS 構成図、アーキテクチャ図、インフラ構成図、ネットワーク構成図、VPC の図を描いてほしいとき、draw.io で AWS の図を作りたいとき、既存の構成図を直したいときは必ずこの Skill を使う。Use this whenever the user asks for an AWS architecture diagram, cloud infrastructure diagram, VPC diagram, or a draw.io file of AWS resources, even if they don't say "draw.io". Mermaid、PlantUML、Python の diagrams ライブラリなど、ほかの形式を指定されたときは使わない。Do not use it when the user explicitly asks for Mermaid, PlantUML, or the Python diagrams library.
Git-native issue tracking (beads), coding guidelines, knowledge injection, and spec-driven planning for AI agents. Drop-in replacement for bd/Beads with simpler architecture. Use for: tracking issues/beads with dependencies, creating bugs/features/tasks, planning specs, implementing features from specs, code reviews, committing code, creating PRs, loading coding guidelines (TypeScript, Python, TDD, golden testing, Convex, monorepo patterns), code cleanup, research briefs, architecture docs, agent handoffs, and checking out third-party library source code. Invoke when user mentions: tbd, beads, bd, shortcuts, issues, bugs, tasks, features, epics, todo, tracking, specs, planning, implementation, validation, guidelines, templates, commit, PR, pull request, code review, testing, TDD, test-driven, golden testing, snapshot testing, TypeScript, Python, Convex, monorepo, cleanup, dead code, refactor, handoff, research, architecture, labels, search, checkout library, source code review, or any workflow shortcut.
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。
Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.
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。