'agentic' 검색 결과

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Chooses and designs developer surfaces for an agentic product: SDK, CLI, MCP, GUI, API, webhooks, and pd tube-style listener/sender workflows. Use when deciding why a user belongs in an SDK versus CLI/MCP/GUI, planning Python SDK parity, or making agent invocation easy across languages. NOT for implementing the full SDK, generic API docs, or UI visual design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit and maintain design system consistency, UX/UI patterns, color palettes, and design best practices across frontend applications

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit whether an enforced-coordination MCP has actually shrunk its ~19 legacy etiquette verbs into the 5 enforced tools — work/act/ask/recall/status — each carrying one denial shape and one transcript event, migrated through a real retirement path (intake-metadata, alias, or doc-history — never a parallel runtime), with zero legacy-verb calls once a body reaches compliance mode C4+. Use when reviewing a broker-collapse migration plan, gating a body's move from advisory (C0-C3) to enforced (C4+) coordination, or verifying IT-018 Broker Collapse compliance. NOT for the inbound trigger→spawn boundary that decides whether an event may spawn an agent at all (use fleet-event-spawn-trust), the capability-token cryptographic mechanism behind a denial (use agentic-zero-trust-security), designing how a swarm of agents invokes and shards work (use swarm-invocation-designer), or auditing an individual PR's mergeability (use agent-pr-authoring).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview", "constitutional AI prep", "alignment interview", "safety interview", "RLHF opinion", "interpretability prep", "anthropic technical". NOT for general ML interview prep, system design interviews, coding challenges, or behavioral interview practice.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design scheduling agents. Use for timezone-safe negotiation, focus protection, and privacy-safe free-busy handling. NOT for calendar UI, project planning, or general agent architecture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

評価ファーストの実行、分解、コスト考慮型モデルルーティングによるエージェンティックエンジニアとしての運用

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deep knowledge expert for the 33GOD agentic pipeline system, understands component relationships and suggests feature implementations based on actual codebase state

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Codex CLI深度集成——接入OpenAI最新子Agent/并行编码能力。覆盖：Codex CLI安装配置、subagent工作流、parallel agent模式、worktree集成、OpenClaw+Codex混合调度。触发词：Codex、Codex CLI、OpenAI Codex、subagent、并行编码、云端开发、OpenAI编码代理、agentic coding

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decision frameworks and patterns for choosing and composing Claude Code components — skills, sub-agents, hooks, MCP servers, and slash commands — into effective multi-component agentic systems with proper orchestration and context management.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5. Операционная дисциплина мышления Fable 5 для менее сильных моделей. Загружай в начале любой нетривиальной задачи (дебаг, рефакторинг, ≥2 файлов, незнакомый код), когда работаешь НЕ на Fable 5 — и указывай сабагенту на Opus/Sonnet/Haiku прочитать этот скилл вместе с нетривиальным заданием. Триггеры — «думай как Fable», «fable-mode», «включи дисциплину», «работай аккуратно», запуск сабагента на слабой модели, повторяющиеся ошибки/переделки в сессии. НЕ нужен для тривиальных правок (rename, опечатка, one-liner) и чисто разговорных ответов.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Patterns and techniques for evaluating and improving AI agent outputs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use after building a feature — explore it in a real browser to find what nobody predicted, then turn each finding into a deterministic test that guards it forever. Covers the agentic pass, what to generate vs hand-write, and how to skip honestly when a capability is missing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when a Claude Code session loops, drifts from scope, claims a fix that CI rejects, hits context, PATH, or permission walls, or fails in a way another retry will not solve and a root cause diagnosis is needed instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation ...

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete reference for 26 web2 bug classes with root causes, detection patterns, bypass tables, exploit techniques, and real paid examples. Covers IDOR, auth bypass, XSS, SSRF (11 IP bypass techniques), SQLi, business logic, race conditions, OAuth/OIDC, file upload (10 bypass techniques), GraphQL, LLM/AI (ASI01-ASI10 agentic framework), API misconfig (mass assignment, JWT attacks, prototype pollution, CORS), ATO taxonomy (9 paths), SSTI (Jinja2/Twig/Freemarker/ERB/Spring), subdomain takeover, cloud/infra misconfigs, HTTP smuggling (CL.TE/TE.CL/H2.CL), cache poisoning, MFA bypass (7 patterns), SAML attacks (XSW/comment injection/signature stripping), error disclosure / debug endpoints (stack trace regex per framework, chain templates), CSS injection (attribute-selector exfiltration, opacity clickjacking, @import). LFI / file inclusion -> RCE (php://filter source disclosure, iconv filter-chain RCE with no upload, log/environ poisoning, .user.ini/.htaccess auto_prepend, data:// + expect:// wrappers, session inclusion, traversal bypass table). Insecure deserialization (PHP __wakeup bypass / phar:// POP chains, Java ysoserial CommonsCollections gadgets + magic bytes, Python pickle __reduce__ + signed-cookie forgery, Node node-serialize). Dependency confusion / supply chain (internal package-name discovery, unclaimed-name confirmation, callback-only PoC, npm/pip/Maven/RubyGems variants). Use when hunting a specific vuln class or studying what makes bugs pay.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.