479 件 ・ 関連度順
概要と使いどころ
Use when cutting a release, writing a CHANGELOG entry, or summarizing what changed since a tag — when the repo's commits follow (or mostly follow) Conventional Commits and a mechanically-accurate section breakdown beats a model's reading of the diff.
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 ARIS to run Markdown-based agent skills for literature review, idea discovery, cross-model critique, experiment planning, and paper-writing support.
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。
Expert performance marketing guidance for paid acquisition, CRO, and growth optimization. Use when creating ad campaigns, writing ad copy, optimizing landing pages, setting up A/B tests, analyzing CAC/LTV, building attribution models, or planning retargeting strategies. Use for Google Ads, Meta Ads, LinkedIn Ads, conversion tracking, budget allocation, and funnel optimization.
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。
Blender MCP expert for scene inspection, Python scripting, GLTF export, and material/animation extraction. Activate when: (1) using Blender MCP tools (get_scene_info, execute_python, screenshot, etc.), (2) writing Blender Python scripts for extraction or manipulation, (3) exporting scenes to GLTF/GLB for web (Three.js, R3F), (4) debugging material or texture export losses, (5) optimizing GLB files with gltf-transform, (6) using asset integrations (PolyHaven, Sketchfab, Hyper3D Rodin, Hunyuan3D). Covers critical export gotchas, material mapping survival, texture optimization pipeline, headless CLI patterns, and known 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。
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。
Design a rifle-scope reticle from a plain-language description and emit it as a BallisticCalculator `.reticle` file (BXml) plus a rendered SVG preview. Covers the complete `.reticle` element/attribute specification, reticle design principles (mrad vs MOA subtension, FFP/SFP, stroke-weight hierarchy, hash-mark ladders, christmas-tree wind holds, clutter budget, BDC anchors) and worked, verified patterns for duplex, mil-dot, hash-ladder, christmas-tree, chevron, circle-dot, dense-grid and rangefinding-stadia reticles. Also covers reproducing an existing commercial reticle from its manufacturer's technical data sheet. Use this skill whenever the user wants to create, modify, review or render a scope reticle, crosshair, mil-dot / MOA grid, holdover ladder, christmas tree, BDC ladder or rangefinding stadia — including any mention of a `.reticle` file, `ReticleDefinition`, `ReticleDrawController`, `SvgCanvasFactory`, or "draw me a reticle", and including requests to clone or model a named commercial reticle (Horus H58/H59, TReMoR, Mil-C, MOAR, Tremor3, EBR, and the like). Trigger it even when the user only says something like "make a crosshair with 0.2 mil hashes out to 5 mils" or "turn this reticle picture into a file", and trigger it before hand-writing reticle XML or hand-rolling reticle geometry, because the file format silently ignores misspelled attributes and mis-cased booleans.
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 working on imp's quantization formats, loaders, or dequant paths - GGUF Q4_0...Q8_0/Q*_K/IQ4/MXFP4, NVFP4 two-level scaling (Modelopt vs compressed-tensors layouts), FP8 E4M3, StorageTier, decode cache, KV-cache dtypes (auto/fp16/fp8/int8/int4/nvfp4/mxfp4), "which quant should I use", "which KV dtype", scale-factor layout, dequant kernel wiring, imp-quantize / AWQ, judging quant quality (PPL). Do NOT use for writing/optimizing GEMM/GEMV kernels (sm120-cuda-expert) or measuring quant perf (benchmark-cuda).
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 or update an ADR under `docs/ADR/` for architectural decisions, dependency changes, data-model changes, or cross-cutting policy shifts. Use when the user asks to write, update, or document an ADR, record a design decision, capture architecture trade-offs, or justify a repo-wide technical policy.
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。
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper 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。
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
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。
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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。
Conversational profile builder. Produces validated JSON config files for any rootnode Skill that consumes profile-shaped configuration (handoff trigger check, mode router, critic gate, future Skills). Reads a target JSON Schema (draft-07), conducts a progressive-depth interview using plain-language questions, validates answers, and writes the resulting JSON to a destination path. Schema-agnostic — works with any profile schema conforming to draft-07. Use when a user needs to create, clone, or revise a profile and you do not want them hand-editing JSON. Trigger on: "build me a profile," "create a profile," "I need a profile for X," "set up my profile," "edit my profile," "clone the X profile," "walk me through making a profile." Do NOT use when the user already has a working profile and only wants to invoke the consuming Skill, when the schema cannot be located, or for non-profile JSON config work. Always validate before writing.
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。
Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.
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 creating AF_XDP sockets, configuring UMEM and XSK rings, writing an XDP redirect program, or choosing copy versus zero-copy mode. Not for full kernel bypass: use dpdk.
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 making an architectural decision, changing a public API, shipping a feature, or recording a codebase term. Not for domain language: use domain-modeling. Not for docs: use docs-writing.
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 modernizing APIs, removing compat shims, killing feature flags, or rewriting a subsystem cleanly. Not for additive refactors that must preserve the old path.
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。
Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, SDK usage, and vector search. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations.
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。
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
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 modifying the pythinker-code terminal UI in apps/pythinker-code/src/tui — components, dialogs/selectors, slash commands, themes, streaming render, or the PythinkerTUI controllers. Covers the architecture, where new features go, test placement, the theme system mechanics, and the dialog interaction/visual spec (DESIGN.md).
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 developing in packages/agent-core-v2 (the DI × Scope agent engine) — adding or modifying a domain Service, choosing a LifecycleScope, wiring DI dependencies, splitting a domain across scopes, owning or migrating a config section, gating behavior behind an experimental flag, raising coded errors, working on the permission system, writing DI/Scope tests, porting business logic from agent-core (v1) to v2, triaging a main-branch commit against v2, or exposing a v2 domain over server-v2 while keeping the /api/v1 wire contract compatible with released clients. Self-contained guide organized by development stage (orient → design → implement → test → verify) plus align workflows for v1→v2 migration, main-branch commit triage, and server-v2 wire exposure; each file carries the rules, examples, and red lines for its step.
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。
TypeScript and JavaScript performance and code-quality rules, usable in any framework. Use when writing, reviewing, or refactoring hot paths, data-structure choices, async flows, or bundle-size-sensitive code. Triggers include "optimize this function", "too slow", "reduce allocations", "bundle size", "lazy load", "speed up imports", or general code review of utilities and services.
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。
The complete Go development skill. REQUIRED for ALL Go work — writing, refactoring, reviewing, generating, scaffolding, or discussing Go code, including any change to .go files. Use whenever the user mentions Go, Golang, go.mod, a Go package, or asks to build a Go backend service, CLI, library, script, MCP server (Model Context Protocol), or AI/LLM agent with tool calling or ReAct-style reasoning. Routes by project type to the right architecture, conventions, and review checklist. Highly opinionated; overrides generic Go style and lint guidance on conflicts.
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 writing sprint contracts in VCSDD strict mode. Provides grading criteria patterns, dimension weight guidelines, and pass threshold formulation for the 5 review dimensions.
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 you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.
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。