「agents」の検索結果

Multi-agent adversarial code review. Spawns three reviewer subagents (Auditor, Adversary, Pragmatist) on a single model, runs a cross-examination round, then deterministically synthesizes a single ranked report. Use this for non-trivial PRs, security-sensitive changes, refactors that touch many files, or any change where one perspective has obvious blind spots. Trigger phrases include "adverse review", "adversarial review", "multi-perspective review", "review my changes from multiple angles", "panel review". NOT suitable for trivial diffs (typos, dependency bumps, formatting).

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。

Control Herdr, a terminal multiplexer for coding agents. Use only when the user explicitly mentions Herdr or asks to use Herdr to inspect or control panes, tabs, workspaces, commands, or another agent. Do not use merely because a task could benefit from a background terminal, delegation, or parallel work. Requires HERDR_ENV=1.

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。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web 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。

Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.

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 revise repo-local skills connected by actionable routing statements from AGENTS.md through broad and increasingly specialized skills. Use when documenting AuthCrunch engineering knowledge, adding or routing a skill, reorganizing the skill hierarchy, or auditing that every skill is reachable and useful to contributors.

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。

Require corresponding tests for every Go code change, E2E coverage whenever developing tests, and immediate diagnostic remediation while agents edit code; select, run, and maintain AuthCrunch tests through pinned tested, browser tests, automation fixtures, coverage artifacts, and CI gates.

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。

Interactive workshop for designing new skills or agents. Walks through best practices, helps decide between skill vs agent, and outputs a complete SKILL.md or agent .md file.

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。

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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。

Full Crypto Analysis Orchestrator — launches 5 parallel subagents for comprehensive multi-dimensional token analysis with composite Crypto Score

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。

60-Second Token Snapshot — fast assessment with signal, key factors, and price levels without launching subagents

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。

Participate in DojoZero prediction games, including NBA and FIFA World Cup trials. Use when user wants to find games, join them, check scores/odds, place bets or prediction-window picks, or view leaderboards.

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。

Participate in DojoZero prediction contests. Use when user wants to find games, join prediction-mode trials, check event status, submit predictions, or view leaderboards.

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 more than one agent may need the same single-instance resource at the same time — most of all the local server loop, which holds the one URL (https://local.voxt.ai, port 7080) the app can run on. Also when you are about to dispatch several agents that will test the UI, when you find another agent already using the loop, or when the user says "coordinate", "shared resource", "queue for the 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。

Session wrap-up: consolidates every descriptive markdown file (README.md, AGENTS.md, HANDOFF.md, docs/*) into ONE canonical CLAUDE.md, then reconciles it against the current code state so a fresh session or new teammate can pick up immediately. CLAUDE.md keeps a mandatory PROGRESS block at the top answering two questions: where are we now and what's the next concrete action. Trigger when the user says: "wrap", "wrap up", "/wrap", "tidy docs", "sync docs", "update docs", "archive", "this phase is done", or the Chinese equivalents "收尾", "同步文档", "整理文档", "整理一下", "更新文档", "存档", "梳理一下", "这阶段做完了". A bare "tidy" / "整理" with prior dev context counts. Do not under-trigger.

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。

Search and read locally-stored Claude documentation covering Claude Code CLI, Claude API (Messages, tool use, vision, streaming, batch), Agent SDK (Python and TypeScript), prompt engineering, and all Anthropic platform docs. Use this skill whenever the user asks about Claude Code features (hooks, MCP servers, skills, plugins, settings, permissions, keybindings, sub-agents), the Anthropic API or any of its SDKs (Python, TypeScript, Go, Java), the Agent SDK (sessions, hooks, custom tools, MCP), model capabilities (context windows, extended thinking, pricing, rate limits, vision), prompt engineering best practices, or troubleshooting any Claude-related error. This skill provides instant access to official documentation files without web searches — always prefer it over web lookups for Claude and Anthropic topics.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AD CS, certificate templates, enrollment rights, EKUs, SAN controls, PKINIT, certificate mapping, and cert-based privilege paths. Use when the user asks about ESC-style abuse, certificate templates, enrollment agents, EKUs, SAN or subject controls, smartcard or PKINIT logon, CA policy, or how an issued cert turns into accepted privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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 imsg CLI from OpenClaw agents for iMessage/SMS DMs, groups, replies, reactions, polls, watching, and private-API actions.

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 Claude Code to investigate one API, Claude Code or MCP question against a local documentation snapshot with paths, provenance and freshness limits.

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 a bounded, report-only audit of coding-agent settings, instruction reachability, hooks, MCP wiring, and verification evidence with actionable findings.

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。

Inspect supported Codex rollout logs locally for duplicate records, retry amplification, and repeated tool failures, preserving unknown evidence and comparing before-and-after snapshots.

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 Agent Media when an agent needs to create captioned vertical UGC videos, narrated b-roll overlays, reusable characters, images, and voice through a hosted MCP server and companion skills.

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。

Retrieve Chinese AI news, recent topic search, event timelines and briefs from AIHOT's anonymous read-only Agent API. Use for current AI developments with original-source links.

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 Squirrelscan from a coding agent to audit SEO, performance, security, accessibility, and agent-experience issues, then feed exact fixes into an agent repair loop.

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。