754 件 ・ 関連度順
概要と使いどころ
The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.
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 agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops, memory, and the 15+ multi-agent architectures (SequentialWorkflow, ConcurrentWorkflow, GraphWorkflow, HierarchicalSwarm, SwarmRouter, and more). Use whenever writing, reviewing, or debugging code that imports `swarms`.
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 deploy AI sales development reps, automate sales qualification, build signal-to-action routing, or design AI agent architecture for sales. Also use when the user mentions 'AI SDR,' 'AI sales agent,' 'automated qualification,' 'signal routing,' 'sales automation,' '11x,' 'Artisan,' 'AiSDR,' 'AI BDR,' or 'autonomous sales.' This skill covers AI SDR deployment, qualification automation, and agent architecture for sales development. Do NOT use for technical implementation, code review, or software architecture.
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。
Integrate with Octav API for cryptocurrency portfolio tracking, transaction history, and DeFi analytics across 50+ blockchain networks. Use when building applications that need to: (1) Track wallet balances and net worth across multiple chains, (2) Query transaction history with filtering and search, (3) Monitor DeFi protocol positions (Aave, Uniswap, etc.), (4) Access historical portfolio snapshots, (5) Analyze token distribution and holdings, (6) Pay per request as an autonomous agent via x402. Triggers on: "Octav API", "crypto portfolio API", "blockchain portfolio tracking", "DeFi analytics API", "wallet balance API", "transaction history API", "multi-chain portfolio", "Octav x402".
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 and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.
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。
Comprehensive guide for building AI agents that interact with Solana blockchain using SendAI's Solana Agent Kit. Covers 60+ actions, LangChain/Vercel AI integration, MCP server setup, and autonomous agent 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。
Metaplex development on Solana — NFTs, tokens, compressed NFTs, candy machines, token launches, autonomous agents. Use when working with Token Metadata, Core, Bubblegum, Candy Machine, Genesis, Agent Registry, or the mplx CLI.
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 iteration loop: modify, verify, keep/discard against any metric
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。
Darwin Skill 2.0 (达尔文.skill 2.0): autonomous skill optimizer, v2.0 integrates Microsoft Research SkillLens (arXiv 2605.23899) 9-dim rubric + SkillOpt (arXiv 2605.23904) validation-gated design + human-in-the-loop checkpoints. Evaluates SKILL.md files using a 9-dimension rubric (structure + effectiveness + meta-skill blacklists), runs hill-climbing with git version control, spawns independent judge agents for blind evaluation, validates improvements through test prompts with auto-break on diminishing returns, and generates visual result cards. Use when user mentions "优化skill", "skill评分", "自动优化", "auto optimize", "skill质量检查", "达尔文", "darwin", "帮我改改skill", "skill怎么样", "提升skill质量", "skill review", "skill打分".
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 improvement loop: scan codebase metrics, scaffold experiment files, run agent-driven iterations until metric improves
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。
Hunt LLM/AI feature bugs — prompt injection, indirect injection, exfiltration via tool-use/markdown, ASCII smuggling, agentic AI security (OWASP Agentic Apps 2026, ASI01-ASI10). Patterns: direct injection ('ignore previous instructions'), indirect injection via documents/web pages/email the model reads, ASCII smuggling (Unicode Tags block U+E0000-U+E007F, invisible to humans, decoded by the model), tool-use exfiltration (model has fetch/browse tool, attacker injects OOB URL, model exfils chat history/secrets), markdown-image zero-click exfil, system-prompt extraction, IDOR-via-AI (cross-tenant data). Targets: chatbots, RAG, summarizers, agentic copilots, MCP tools. Detection: any LLM-backed endpoint, doc upload triggering AI processing, autonomous agent with tools. Validate: OOB/Collaborator callback for exfil, verbatim-reproducible system-prompt leak (run twice), verifiable cross-tenant leak or RCE. Confabulation is NOT a finding. Use when hunting AI features, chatbots, RAG, agentic systems, MCP.
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 establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.
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 autonomous research workflow using swarm mode for 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。
Full autonomous research workflow — brainstorm, plan, implement, review, and document
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 explicitly wants autonomous progress without routine clarification stops: "plow ahead", "do not stop", "use your best judgment", "keep going until done", "finish while I am away", "do not ask questions unless truly blocked", or similar. Convert ordinary ambiguity into stated assumptions, proceed through implementation and validation, stop only for true blockers, and end with a clear recap of decisions, changes, verification, and residual risk.
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。
Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or host automations.
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。
Draft /goal prompts and explain persistent agent loops. Use for goal or Ralph loops, autonomous run setup, monitoring, and troubleshooting.
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 performance optimization: research, PoC, benchmark, implement, review, PR
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。
Enforces completion discipline for substantial autonomous work by writing acceptance gates before execution, decomposing work with the Depth Tree, running approved checks, and re-verifying evidence before reporting. Use when an agent faces a long or multi-part task, work that has returned half-done, an exhaustive audit or build, parallel leaves or pipelines, or explicit triggers such as /unlazy, $unlazy, "tree N", "gates", and "do not stop until it is done".
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。
Implement approved tasks using TDD with native subagent dispatch. Runs all pending tasks autonomously or selected tasks manually.
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。
Implement approved tasks using TDD with subagent dispatch. Runs all pending tasks autonomously or selected tasks manually.
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-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
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。
YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltered Hard Truths report. After user types YOLO, autonomously runs the business for a day using /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。
Autonomous audit-to-fix pipeline — find issues, classify, fix, test, commit
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。