36건 · 관련도순
Design file-based storage for agent context, including large tool results, plans, logs, and handoff notes. Keep information available outside the conversation and retrieve relevant sections when needed, instead of loading everything at once.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Manage a shared skill library for Claude Code, Codex, Cursor, and other agents. Search, install, update, and deploy skills to selected agents, organize reusable groups and tags, and check where each skill is available.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search one AI agent's raw history for a specific topic, ingest only matching sessions, and return a synthesized answer. Use for targeted cross-agent recall; use wiki-history-ingest for bulk archival ingestion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ingest Hermes agent history into Obsidian as distilled knowledge. Use for importing or mining ~/.hermes sessions, memories, or logs; use wiki-agent for targeted topic-only cross-agent recall.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ingest OpenClaw session/history data into Obsidian as distilled knowledge. Use for importing or mining ~/.openclaw sessions, memory files, or logs; use wiki-agent for targeted topic-only cross-agent recall.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Complete AI coding workflow system. Orchestration patterns, 18 hook events, 8 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Trellis channel for live multi-agent collaboration, spawned workers, cross-agent review, progress inspection, forum channels, and channel log debugging.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cross-agent collaboration protocols, workflow handoff patterns, and commit message formats for TDD/Mikado/refactoring workflows
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Orchestrating ecosystem self-evolution: lifecycle-phase detection, agent relevance, cross-agent knowledge synthesis, evolution proposals. Use when auditing skill-ecosystem health or fitness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use HarnessKit to inventory, audit, enable, disable, and deploy skills, MCP servers, plugins, hooks, CLIs, configs, memory, and rules across multiple coding agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Chops to discover, search, edit, and organize local or remote agent skills across Claude Code, Cursor, Codex, Windsurf, Amp, Copilot, and Aider.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when creating, auditing, or maintaining AGENTS.md files and Codex CLI configuration for any project—including initializing AGENTS.md for cross-agent compatibility (Codex, Cursor, Copilot, Devin, Jules, Amp, Gemini CLI), generating config.toml or .rules files, scaffolding .agents/skills/, converting CLAUDE.md to AGENTS.md, or auditing existing agent configs for bloat and staleness. Complementary to codex-orchestrator (which executes subagents; this skill creates the config files they consume).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deep code review after beads have landed — random exploration + cross-agent review, alternating. Use every 30–60 min during active implementation, or after any epic lands.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deep review — random exploration + cross-agent review, alternating. Finds integration bugs that self-review misses. Run every 30–60 min during active implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validates and scores SKILL.md files against the agentskills.io specification; use when linting skills for cross-agent compatibility, description quality, or capability graph structure.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.