Results for “coherent-action”

Merges multiple research, analysis, or brainstorming inputs into a single coherent synthesis with key insights and a recommended action.

Action (araz-action)

通用行动抽象接口。当定义行动执行、行为模式、动作序列时调用此技能。

Action (apaz-action)

Action (ajaz-action)

Action (akaz-action)

当寻求个人行动或项目在更宏大系统中的意义，或需要将日常工作与长远价值连接时

Action (aiaz-action)

Composite skill — full debugging workflow from "this is broken" to root cause and fix. Chains systematic-debugging (root-cause hypotheses) → tracer agent (evidence walk) → sentry (production correlation if applicable) → ci-watch (regression check) → incident-response (if production-impacting). Use when a bug needs deep investigation, not just a quick fix.

当需要建立可持续的创造节奏，将灵感转化为行动时

Action (ataz-action)

Action (afaz-action)

Action (avaz-action)

Action (aqaz-action)

Action (ayaz-action)

Action (abaz-action)

Action (aaaz-action)

Action (adaz-action)

Action (axaz-action)

Action (aoaz-action)

Action (alaz-action)

当需要进入高度专注、高效且愉悦的工作或学习状态时

Plain-object Redux actions for the chota-react-saga template — three-phase async triples (REQUEST / SUCCESS / ERROR) per CRUD verb, single-phase actions for sync ops, UPPER_SNAKE type constants, and explicit hand-written action creators (no createAsyncThunk).

Action (ahaz-action)

Action (awaz-action)

Action (asaz-action)

Action (acaz-action)

Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach, using network segmentation, endpoint isolation, credential revocation, and access control modifications.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

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.

Action (amaz-action)

Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrievals) → handoff (durable snapshot if session-ending). Use when the work involves "what did we decide", "remember this", "save where we are", or any closing checkpoint.

Improves multi-step research reliability with structured protocols for source validation, error recovery, and transparent reasoning.

Deep Confidence Harness — a thinking, planning, and execution framework that forces structured reasoning before acting. Combines Monte Carlo scenario analysis, calibrated confidence, multi-perspective debate, and optimal path planning. Use before any complex decision, build, or task. Activates on: 'deep confidence', 'think before you act', 'plan first', 'Atlas mode', 'reason through this', 'what should I do', 'think this through', 'best approach', 'reason carefully', 'plan and execute'.

Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.