「tools」の検索結果

Maintain go-authcrunch Make targets, pinned Go tools, build/test orchestration, maintenance scripts, generated artifacts, and embedded UI refresh workflows.

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 designing UI with OpenPencil — creating layouts via op CLI, batch design DSL, sandboxed JS scripts, or MCP tools. Covers PenNode schema, semantic roles, typography, color, spacing, and common component patterns.

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 reviewing, debugging, or giving feedback on Go code in this repo (exercises/part2, website/tools, or any *.go file) or when asked for a "code review", "review this Go", "revisa este código Go", or "crítica de código". Reviews for correctness, idiomatic Go, error handling, resource cleanup, concurrency, performance, and security, and always ends with a test-thinking pass (what a test would catch). For exercises/part2, prefer the go-exercise-reviewer skill which knows the module-mode quirk; this skill covers general Go anywhere in the repo.

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。

End to end Product Hunt launch skill for developer tools. Generates listing assets (tagline, description, maker comment, visual asset spec, social proof) and a dated launch plan with workstreams, asset inventory, day of runsheet, and post launch sequence. Use when: prepare a Product Hunt launch, plan a PH launch, write PH listing copy, build PH launch checklist, generate PH launch 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。

Analyze LinkedIn performance and manage engagement through Publora. Use when the user asks how a LinkedIn post or account performed (impressions, reach, reactions, comments, reshares, follower growth) or wants to react, comment, reshare or resolve an @mention. Statistics run over the REST API, engagement over MCP tools. Not for creating posts (use linkedin-post).

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。

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。

Build a plug-in-ready SDR operator package for sales development and outbound pipeline work: ICP definition, compliant lead-source plan, enrichment/research workflow, account scoring, outbound assets, CRM handoff, reply triage, meeting prep, approval gates, and loop memory. Use when "SDR", "sales development", "outbound SDR", "operator room", "lead gen pipeline", "prospecting pipeline", "ICP targeting", "Apify", "RapidAPI", "Clay", "Apollo", "agentic SDR workflow", "sales operating loop", "sales dashboard handoff", or any request to package outbound sales workflows that can later connect to data tools, CRMs, or operator surfaces.

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。

Drive the Voxt UI from Codex — chrome-devtools MCP setup, debugUI helpers, fast multi-user sign-in

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 wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'.

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。

Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence 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。

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。

Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence 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。

Scaffold, run, inspect, and evaluate model-agnostic agents that connect to MCP servers, skills, shell tools, and workflow packs.

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。

Bright Data Web MCP gives MCP-compatible agents live web search, scraping, and optional browser-automation access through Bright Data’s web-access platform. It is built for agents that need current information, anti-bot resilience, and structured extraction without maintaining their own scraping stack.

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-use is an open-source Python framework for letting AI agents interact with websites through a real browser. It supports local execution, optional cloud browsers, custom tools, and a CLI for persistent browser sessions.

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。

Browsertrix Crawler is a browser-based crawling system from Webrecorder for high-fidelity site capture, QA replay analysis, and configurable crawl behavior. It runs complex crawls in a single Docker container and uses Puppeteer plus Chrome DevTools Protocol under the hood.

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。

Budibase is an open-source low-code platform for internal apps, forms, portals, and approvals. It gives agents a practical way to stand up operational tools on top of PostgreSQL, REST APIs, and other business data sources with self-hosted deployment options.

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 Google Agent Development Kit to define, test, evaluate, and deploy code-first Python agents with tools, sessions, HITL confirmation, and multi-agent orchestration.

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。

Turn an existing coding agent into a supervised local work assistant that connects to workplace tools, reusable skills, workflows, and scheduled loops.

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 CC Safety Net when coding-agent CLIs need pre-execution hooks that block destructive commands, secret access, and unsafe file operations before tools run.

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 CodeBurn locally or as an MCP server so agents can inspect token usage, cost, model mix, project spend, and budget waste across coding 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 DeepAgents to build long-running task agents with a batteries-included harness for planning, tools, and workflow 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。

Insert a trace-aware guardrail layer between agents and their tools so unsafe message patterns or tool-call sequences are blocked by explicit rules.

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。

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

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。