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概要と使いどころ
Analyze Windows LNK shortcut files and Jump List artifacts to establish evidence of file access, program execution, and user activity using LECmd, JLECmd, and manual binary parsing of the Shell Link Binary format.
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。
10 C-level advisory agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. CEO, CTO, COO, CPO, CMO, CFO, CRO, CISO, CHRO, Executive Mentor. Multi-role board meetings, strategy routing, structured recommendations. For founders needing executive-level decision support.
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Explore and diagnose a PostHog endpoint's execution logs — error messages, failed runs, cache misses, slow runs, or unexpected row counts during endpoint invocations. Use when the user says "my endpoint is failing", "show me the logs for endpoint X", "what error did endpoint Y produce", "why did endpoint Z return no rows", "is this endpoint hitting cache", or "check the last N runs". Focused on a single named endpoint's runtime log entries, not project-wide auditing or query performance profiling.
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。
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
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Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.
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。
ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.
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。
Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.
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。
Operate E2B agent sandboxes using the CLI. Use when user needs to run code in isolation, test packages, execute commands safely, or work with binary files in a sandbox environment. Keywords: sandbox, e2b, isolated environment, run code, test code, safe execution.
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Upstash サーバーレスデータプラットフォームリファレンス。 @upstash/redis — REST API、pipelining、transactions、JSON、Search、グローバルレプリケーション。 @upstash/ratelimit — Fixed Window、Sliding Window、Token Bucket、limit、blockUntilReady。 QStash — publishJSON、schedules、queues、DLQ、URL Groups、callbacks、flow-control。 @upstash/vector — upsert、query、ANN、hybrid index、sparse index、embedding models、namespace。 Rust 製ローカル vector DB エンジン fandhe-db とは別（こちらはマネージド SaaS の JS SDK）。 @upstash/workflow — durable execution、serve、context.run/sleep/call/invoke、waitForEvent、parallel steps、agents。 Upstash Search — search、upsert、fetch、range、filtering、reranking、algorithm、@upstash/search。 Upstash Box — サンドボックス、agent、filesystem、git、browser（CDP/AI actions/recordings）、network policy、snapshots、schedules。
Windows アプリ開発 (Windows AI Foundry) の AI/ML 統合リファレンス。 Windows AI APIs、LanguageModel, TextRecognizer, ImageScaler, ImageObjectExtractor, ImageObjectRemover, ImageDescriptionGenerator, SemanticSearch, ContentModeration, Phi Silica LoRA、Windows ML、ONNX Runtime、execution providers、LearningModel、 Foundry Local、CLI、REST API、SDK、model catalog、MCP サーバー、App Actions、 IActionProvider、Agent Launchers、DirectML、IDMLDevice、operators、tensors。
親イシュー配下のサブイシュー（孫以下の全子孫を含む）を依存順を保ちつつ worktree で並列に自動実装・push 前 review・PR 作成・CI 監視・マージ可能状態化まで一括自動化。 「イシューツリーを並列実装」「配下のサブイシューをまとめて実装」「ツリー全体を並列で実装して」「イシュー階層を自動開発」で使用。 per-issue 計画立案（Plan: セッション継承モデル）→実装（Implement: sonnet）の分業。push 前 review（Review 通過後にのみ push・PR 作成して CI を 1 回だけ起動）。 外部チェック構成は args の externalChecks で明示（{"app", "context"} の組で宣言。[] で「なし」を確定して不要待機なし・未指定なら自動マージ停止・slug のみの旧形式は自動マージ fail-closed）。 自動 squash merge は autoMerge: true + externalChecks 明示（全 App の信頼済み context 宣言込み）の opt-in で実行（merge-exec の自己取得再検証 + サーバー側 branch protection 実測が前提。既定 false はマージ可能状態で停止し人間がマージ。サーバー側 workflow サンプル（upstream の docs/implement-issue-tree/auto-merge-sample.yml 参照）+ branch protection への委譲も可）。並列度（parallel）と依存（dependsOn）で実行順を制御。 Phase 単位で厳密に直列化したい場合は phaseGate: true（opt-in。ルート直下の子〔Phase 親〕を sub-issues リスト順に直列化し、前 Phase の全子孫が merged/closed になるまで次 Phase 配下に着手しない。既定 false は現行の post-order 優先度のみ）。 着手できる件数・blocked になる件数を事前に知りたい場合は dryRun: true（何も書かずに見積りだけ返す）。 単一イシューの実装は implement-issue、PR レビューは implement-review-pr を参照。
Inngest イベント駆動 Durable Execution プラットフォームリファレンス。 サーバーレス環境での長時間ワークフロー、バックグラウンドジョブ、スケジュールタスク実装。 createFunction, step.run, step.sleep, step.sleepUntil, step.waitForEvent, step.waitForSignal, step.invoke, step.sendEvent, step.fetch, step.ai.infer, step.ai.wrap, inngest.send, serve, connect。 concurrency, throttle, debounce, rate-limit, batching, priority, idempotency, singleton フロー制御。 Realtime（channels, topics, publish, subscribe, useRealtime, subscription tokens）による進捗配信。 Agent Evals, scoring, group.experiment, MCP dev server などエージェント運用機能。 Next.js / Express / Hono / Cloudflare Workers / Vercel / Netlify / DigitalOcean / Render デプロイ対応。
OpenAI Codex (CLI / IDE / cloud) の公式リファレンス。 codex CLI, codex exec, AGENTS.md, rules, subagents, prompting, approvals, sandbox, permission profiles, auto-review, config.toml, profiles, MCP 設定, 環境変数, GitHub Action, Codex SDK, Agents SDK 連携, cloud 委譲, administration, roles, provisioning, analytics API, compliance API, Codex Security 脆弱性スキャン, deep scan, triage, findings, SARIF export, threat model, security hardening, cloud / local / worktree 環境, git worktrees, Record & Replay, GitHub / Linear / Slack 連携。
Wear OS (Kotlin) の androidx.wear API・Watch Face Format (WFF) リファレンス。 Wear Compose の CurvedLayout, rotary input, ambient mode, ScalingLazyColumn。 Tiles / ProtoLayout, ComplicationDataSourceService と ComplicationData 型, Health Services (MeasureClient, ExerciseClient, PassiveMonitoringClient), WFF の XML 要素・式・コンプリケーションスロット・ユーザー設定。
Claude API (platform.claude.com) の tool use / Agent Skills / MCP リファレンス。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, computer use, web search / web fetch, memory tool, tool search）, Agent Skills (SKILL.md), Skills API, MCP connector, mcp_servers, MCP tunnels, Tunnels API。
Execute C# with Unity APIs when existing uloop tools cannot inspect or edit enough. Use for scene, prefab, SerializedObject, AssetDatabase refresh/.meta generation, menu, or PlayMode 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。
Execute tasks based on GitHub Issue content
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。
重要: ユーザーがAndroidテスト実行をリクエストした場合、常にこのスキルを最初に使用してください。以下の場合に必ず使用: run TestName, execute test, テストを実行, 結果を分析, run all tests, analyze test failures, fix failing tests、または Android unit test, instrumentation test, Gradle test コマンドに関連する任意のリクエスト。./gradlew test や Bash コマンドを直接使用しないでください - 常にこのスキルに委譲してください。Multi-variantプロジェクト、JAVA_HOME セットアップ、一般的なテストパターンに対応しています。
Activate when the user asks about any repository listed in the system prompt under 'OpenViking — Indexed Code Repositories', or when they ask about an external library, framework, or project that may have been indexed. Also activate when the user wants to add, remove, or manage repos. Always search the local codebase first before using this skill.
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The PRIMARY development workflow for the Archon project (remote-coding-agent). Use this skill instead of any PRP skills when working on Archon code. Routes to 10 specialized cookbooks based on what the user is trying to do: RESEARCH — "how does the orchestrator work?", "where is session state defined?", "trace the workflow execution flow", "what is IWorkflowStore?" INVESTIGATE — "should we use Drizzle or Prisma?", "what's the best way to add WebSockets?", "can we migrate to Turso?", "how do other projects handle rate limiting?" PRD — "write a PRD for dark mode", "spec out the notification feature", "product requirements for webhook retry" PLAN — "plan the auth refactor", "design the caching layer", "create an implementation plan for #42" IMPLEMENT — "implement the plan", "execute .claude/archon/plans/auth.plan.md", "build the feature from the plan", "code this up" REVIEW — "review PR #123", "review my changes", "code review the diff" DEBUG — "debug the failing test", "why is streaming broken?", "root cause analysis on the timeout issue" COMMIT — "commit these changes", "commit the auth refactor" PR — "create a PR", "open a pull request for this branch" ISSUE — "report this to gh", "create a gh issue", "log it in github", "file a bug for this", "create a feature request" This skill triggers on ANY development task: researching, investigating, planning, building, reviewing, debugging, committing, or shipping code. NOT for: Running Archon CLI workflows in worktrees (use /archon 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。
Execute a phased implementation plan using subagents. Use when asked to execute, run, or carry out a plan — especially one created by make-plan.
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 the user asks for the state of pwdev-feat plans — 'status do feat', 'quais planos estão pendentes', 'show feature status' — listing pending, complete, with-caveats, failed and resumable plans plus codebase-context health. Read-only. Do NOT use to execute (feat-exec) or query the audit trail (feat-audit).
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。
Decompose an approved PWDEV Flow specification into dependency-aware waves and atomic executable tasks. Use when design is approved and the user needs exact file scope, test-first actions, verification commands, and an execution gate.
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 approved plan needs executing task by task in this session, with a fresh implementer per task and review between tasks
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。