2.1천건 · 관련도순
When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' or 'partner program.' This skill covers program design, incentive structure, and growth optimization.
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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
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Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
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You are an expert in the interface between LLMs and the outside world. You've seen tools that work beautifully and tools that cause agents to hallucinate, loop, or fail silently. The difference is almost always in the design, not the implementation.
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Full adversarial quality loop — implement, self-attack, parallel verification, quality gates, final validation. 全品質循環：實現、自攻、並行驗證、質量關卡、最終確認. Use when: implement task with quality, run adversarial review, verify code quality, run quality gates, complete workflow task
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Analyze changes and create a meaningful commit with agent authorship. Internal skill for evolve_loop.
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This skill should be used when the user says "add a billing agent", "add a bookkeeping agent", "set up a support tier-1 agent", "add a recruiting agent", "add a compliance agent", "add a CRM agent", "add a contracts agent", "add an analytics agent", "add a marketing email agent", "add a CMS agent", "scaffold a function-specific agent", or wants to add a single business-function agent backed by a verified-write MCP integration. Each agent ships with explicit scope, "what this will NOT do" constraints, and recommended human-in-the-loop checkpoints.
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[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
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MANDATORY deploy after every code change. Typecheck → deploy → purge CDN → E2E on production → visual verify → fix-forward loop. Workers Builds native CI/CD, D1 Time Travel PIT recovery, D1→R2 long-term backups, wrangler rollback, wrangler secrets management, structured observability, cross-browser smoke tests, rollback procedures, and GitHub auto-configuration.
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Read and drive Agentic Table (magic table / due-diligence) sheets through the unique-cli agentic-table command. Use when the user or task involves an Agentic Table: inspecting a sheet's state, a cell's value or lock state, a cell's edit history or its export artifacts; writing text into a specific cell; or running the full loop — creating a sheet, importing a questionnaire and sources, waiting for the agent to answer, and exporting the result. Access is enforced per sheet by the platform and varies from sheet to sheet; a denial is reported as `agentic-table: permission denied`.
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Vercel AI SDK (TypeScript LLM ツールキット, ai / @ai-sdk/*, v7)。 generateText, streamText, Output, ToolLoopAgent, createMCPClient, useChat, UIMessage, embed, generateImage, wrapLanguageModel, WorkflowAgent, HarnessAgent, AI Gateway, @ai-sdk/rsc、 v4 から v7 への移行。
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React Three Fiber (R3F, three.js 用 React レンダラー @react-three/fiber v9) のリファレンス。 R3F Canvas props, createRoot, useFrame, useThree, useLoader, extend, attach, primitive, args, piercing, pointer イベント / pointer capture, frameloop demand / invalidate, ThreeElements 型, @react-three/test-renderer, @react-three/eslint-plugin, React Native (Expo), v9 移行。
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Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.
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Run the full design-doc-writer and design-doc-reviewer loop until consensus. Produces a polished design document with a PR plan.
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Create PostHog error tracking suppression rules to drop high-volume, low-value errors at ingestion. Use when the user asks "stop capturing this error", "drop browser extension errors", "ignore ResizeObserver loops", "suppress bot-driven errors", or wants to reduce ingestion cost from noisy unactionable errors. Identifies suppression candidates, scopes the filter tightly, decides between full suppression and sampling, and confirms the rule before creating it. Suppressed errors are dropped permanently — this skill defaults to caution.
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Focused Signals scout for PostHog projects using error tracking. Watches `$exception` bursts, stuck loops, multi-fingerprint clusters, status regressions, and stack-trace activity-name patterns. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
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Iterate on a PR until actionable CI passes and high/medium review feedback is addressed. Use for PR CI failures, review feedback, or green-check loops; do not wait for human approval, draft status, or merge gates.
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Keep iterating on code changes until the tests pass, the build succeeds, or linting is clean. Runs in a tight loop of fix → run → check → repeat. Use when you want the agent to autonomously grind through test failures or build errors.
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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
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How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".
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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the self-improvement loop, do a debug/test round, expand tool coverage, improve tool quality, or evolve ToolUniverse.
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