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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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。

Coordinate multiple DeepSeek Harness plugin Skills across inspection, migration, runtime debugging, heavy dependencies, testing, naming, and release. Use for a guided lifecycle workflow, a pre-run capability menu, or several DSH plugin operations with one status report. Preserve the user's selected scope and existing authorization across stages.

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 an installed DSH Web plugin misbehaves only at runtime in the browser — paste/attachment/composer features that work once then fail, chips or panels showing stale placeholder state, update chips claiming the wrong version — and the fix must be diagnosed against the exact host API semantics rather than guessed from names. Also use when reviewing a plugin's calls into input-machine or facade verbs (insert, consume, remove, subscribe) before a release.

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。

When the user wants to set up, debug, or interpret app install attribution — including SKAdNetwork (SKAN), Apple's AdAttributionKit, Google Play Install Referrer, MMPs (AppsFlyer, Adjust, Singular, Branch, Kochava), deep links, deferred deep links, conversion values, postback windows, or privacy thresholds. Use when the user mentions "SKAdNetwork", "SKAN", "SKAN 4", "AdAttributionKit", "AAK", "MMP", "AppsFlyer", "Adjust", "Singular", "Branch", "attribution", "conversion value", "postback", "Install Referrer", "deferred deep link", "iOS 14.5", "ATT", "App Tracking Transparency", "IDFA", or "I can't measure my ad campaigns". For paid campaign strategy, see ua-campaign and apple-search-ads. For analytics events, see app-analytics.

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 users through creating, updating, debugging, and validating Agent Skills for AI agents. Use when the user wants to create, write, author, design, troubleshoot, validate, or improve a Skill, or needs help with SKILL.md, frontmatter, or skill 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。

Use the OpenClaw Windows app's local MCP server and winnode CLI for development, debugging, proof, and automation.

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。

Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protocol with a --use-proxy toggle. Use this skill whenever the user mentions Fireworks, FIREWORKS_API_KEY, api.fireworks.ai, fireworks_ai/ model ids, accounts/fireworks/models/..., a Fireworks deployment id, the Leanmcp gateway or LEANMCP_API_KEY, app.leanmcp.com observability, or gpt-oss / kimi / glm / qwen / deepseek served on Fireworks; wants to route LLM calls through a proxy for logging or cost tracking; is debugging empty responses, 404s, scope errors, cold starts, or "the gateway isn't working"; wants to pick a Fireworks model with tool calling; or wants to sweep several models over one benchmark. Reach for it even on vague asks like "why is this model returning nothing" or "set up inference for these models" when Fireworks is in play.

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。

Scaffold a native iOS app (SwiftUI, no dependencies) from nothing, run it in the Simulator, and archive + upload it to TestFlight entirely from the command line. Bundles working scripts for icon generation, simulator run, and signed upload via the App Store Connect API. Use this whenever the user wants to build, run, ship, or debug an iOS/iPhone/iPad app, mentions Xcode, SwiftUI, .xcodeproj, xcodebuild, simctl, provisioning, code signing, App Store Connect, TestFlight, archiving, or "get this on my phone" — and also when an existing iOS build is failing on icons, signing, teams, destinations, or duplicate build numbers.

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 tau2-bench task export files to identify exactly where and why tasks failed. Map failures to specific conversation rounds, show root causes, check whether relevant documentation was available, and cite sources. Use this skill whenever you need to debug a tau2-bench evaluation, understand why an agent made wrong decisions, identify missing knowledge, or review task performance—especially when context length, eligibility criteria, or policy compliance might be at fault.

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。

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

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, run, debug and ship 3D city games in Godot 4 — procedurally generated cities (block grids, districts, weighted building typologies, MultiMesh batching, facade shaders, terrain, day/night, landmarks), first-person controllers, headless verification, Blender-generated .glb assets, rigged character and animation packs, real OpenStreetMap-derived cities, WebAssembly export, and cloud GPU streaming on GCP. Use this skill whenever the work involves Godot or a .gd/.tscn/.gdshader/project.godot file; whenever someone wants to start, set up, clone onto a new machine, or stand up a 3D game or city generator; whenever they mention procedural city, city generator, block grid, building typology, MultiMesh, facade shader, terrain heightmap, navmesh, Godot export templates, export presets, headless Godot, .glb pipeline, Blender asset generation, character rigs or borrowed animations; and whenever they want to deploy or share a game — web export, SharedArrayBuffer/COOP/COEP, Selkies, pixel streaming, GPU VM, or "let people play this without installing anything." Reach for it even on vague asks like "get this running", "why is my city slow", "my model faces backwards", "make it work on a cloud machine", or "set this up somewhere else too" when a Godot 3D project is in play.

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。

Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Use this to instrument a service, fix alerting that is ignored, set error budgets or reliability targets, prepare for on-call, or run a blameless post-incident review.

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。

Finds the root cause of a bug, test failure, or unexpected behavior before proposing any fix. Use this whenever something is broken and the cause is not yet proven — a failing test, a production error, intermittent behavior, or a symptom that appeared after a change. Also use when a fix has been attempted and did not work, or when the same bug keeps coming back.

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。

Builds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. Use this to design or debug a pipeline, decide batch versus streaming, add data quality checks, handle late or duplicate data, or work out why a dashboard's numbers changed without anyone changing the dashboard.

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 when debugging. Trace each symptom back to the first place the system went wrong and fix it there. A guard is a fix only when the state it handles is legitimate.

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 encountering any bug, test failure, or unexpected behavior, before proposing fixes

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。

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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 browser page screenshot (webpage content only, not desktop). When you need to show page state, document results, or debug issues. For desktop screenshots, use desktop_screenshot instead.

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。

Builds robust Python MCP (Model Context Protocol) servers with FastMCP — tool design, error contracts, event-loop-safe blocking work, subprocess/CLI wrapping, single-file vs packaged distribution, global-state-free testing, and prompt-injection awareness. Use when writing an MCP server, exposing a tool or CLI to an LLM client, debugging tool registration/packaging, or testing MCP tools.

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。

Debug and fix failing GitHub PR checks for xquic. Use when a PR, branch, or commit has failed CI, GitHub Actions, build checks, test checks, or requested validation failures that should be diagnosed from logs before editing.

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。

Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says "archive this". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.

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。

Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.

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 and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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。

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

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。