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概要と使いどころ
Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign
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 autonomous task execution using the codex CLI. Use when the user asks to "codex exec", "run codex exec", "execute a task with codex", or "delegate to codex".
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。
Automated contract verification, detection, and remediation across multiple languages using formal preconditions, postconditions, and invariants. This skill provides both reference documentation AND execution capabilities for the full PLAN -> CREATE -> VERIFY -> REMEDIATE workflow.
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 invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible
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。
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
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 user asks to leverage codex, gpt-5, or gpt-5.1 to implement something (usually implement a plan or feature designed by Claude). Provides non-interactive automation mode for hands-off task execution without approval prompts.
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。
Claude Code plugin enforcing operational guidelines, terse responses, sequential execution, and no destructive operations without confirmation.
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。
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
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。
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
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。
Complete UI/UX design capability — a wiki-style knowledge map of design theory (UX laws, Nielsen heuristics, usability, visual hierarchy, typography, contrast/WCAG, design systems, aesthetics-vs-conversion) fused with a hands-on execution playbook for building distinctive, production-grade frontend interfaces. Use when designing or building any UI (websites, landing pages, dashboards, components, apps), reviewing or critiquing UI/UX, diagnosing why a design doesn't convert, or styling/beautifying web interfaces. Grounds every visual decision in UX principles and avoids generic AI aesthetics.
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 attacking or auditing a CI/CD pipeline or software supply chain — pwn requests, poisoned pipeline execution, compromised/mutable-tag actions, dependency confusion, registry worms, runner backdoors, OIDC trust abuse, SLSA/provenance
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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。
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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 adding durable execution to a TypeScript project — building retry-safe webhook handlers, background jobs that survive crashes, scheduled tasks, or long-running workflows that outlive a single request. Covers Inngest SDK installation, client config, environment variables, serve endpoints (Next.js, Express, Hono, Fastify), connect-as-worker mode, and the local dev server.
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 implementing delays that must survive process restarts (e.g., 24-hour cart abandonment, scheduled follow-ups), waiting for human approval or external events with timeouts (review gates, webhook callbacks, async API completion), polling external services without losing state on crashes, calling other functions and awaiting their results, memoizing expensive operations so they don't re-run on retry, or running async work in parallel inside a workflow. Covers Inngest step methods: step.run, step.sleep, step.waitForEvent, step.waitForSignal, step.sendEvent, step.invoke, step.ai, plus patterns for loops and parallel execution.
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 building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.
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。
Help users connect Arch Studio, understand its access and memory, choose a first workflow, and troubleshoot from reported evidence. General architecture and project execution stays with its owning skill.
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。
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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。
Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
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。
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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。
Find and place the best 0DTE (zero-days-to-expiration) credit spreads from Interactive Brokers. Default execution route is the EMA9/EMA21 + VIX/VXN regime strategy (ema_vix_0dte.py), which auto-selects bull_put or bear_call from a bare EMA cross and skips the trade when the vol index is elevated (VXN ≥ 35 for NDX/QQQ, VIX ≥ 20 otherwise). Optional --rr-gate and --time-gate add red→red and bar-timing confirmation. Supports cash-settled indices (SPX, NDX, RUT, VIX) and any optionable stock/ETF. Manual spread type override is available via zero_dte.py. Requires TWS or IB Gateway running locally.
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 Qwen Coder CLI delegation workflows for coding tasks using Qwen2.5-Coder and QwQ models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Qwen for tasks such as code generation, refactoring, debugging, or architectural analysis. Triggers on "use qwen", "use qwen coder", "delegate to qwen", "ask qwen", "second opinion from qwen", "qwen opinion", "continue with qwen", "qwen session".
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 Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex 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。