“planner”的搜索结果

Generate marketing ad images using Nano Banana Pro (Gemini 3 Pro Image). Accepts campaign-planner creative briefs, reads brand bible for visual style, constructs marketing-optimized prompts, and produces platform-ready images at correct aspect ratios. Supports 1:1, 9:16, 16:9, 4:5 formats. Includes self-review loop to catch hallucinated logos, wrong text, and quality issues. Draft-first workflow (1K fast iteration, 4K final). Outputs to /tmp/marketing/assets/images/.

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AI planning coach using the 4To1 Method™ — turn 4-year vision into daily action. Connects to Notion, Todoist, Google Calendar, or local Markdown. Use when user wants to plan goals, do weekly reviews, track projects, or set up a planning system.

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Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.

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Plan database migrations before executing them: risk assessment, dependency mapping, multi-service sequencing, rollback budgets, and stakeholder communication plans. Use when deciding HOW to migrate, not when writing the actual DDL. NOT for: writing migration SQL (use database-migration-manager), data ETL pipelines (use data-migration-specialist), query optimization, or ORM configuration.

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Organizing digital life for legacy, emergency access, and death preparedness. Specializes in password management, account documentation, digital asset preservation, and ensuring loved ones can access what they need.

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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Modifies DAG structure during execution in response to failures, new requirements, or runtime discoveries. Supports node insertion, removal, and dependency rewiring. Activate on 'replan dag', 'modify workflow', 'add node', 'remove node', 'dynamic modification'. NOT for initial DAG building (use dag-graph-builder) or scheduling (use dag-task-scheduler).

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Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).

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Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.

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Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

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AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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Runs autonomous multi-hour green-field builds: planner, contract, generator/evaluator loop, scaffolding density per model tier. NOT for scoped tasks (mk:cook); NOT for scaffolding only (mk:bootstrap).

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Use when running an autonomous multi-hour build of a green-field product — orchestrates planner → contract → generator ⇄ evaluator loop with adaptive scaffolding density per model tier. Triggers on /mk:autobuild, "build me a kanban app", "build a retro game maker", "autonomous build", or any green-field product spec. NOT for scoped single-task work (see mk:cook); NOT for initial project scaffolding only (see mk:bootstrap).

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Plan vulnerability research using conventional bug-hunting and pentest stage names, specialty playbooks, and a research-hypothesis-experiment loop. Use when designing a bug bounty workflow, selecting tools and strategies, building a decision tree, or turning a target/scope into a staged hunting plan.

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Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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Find Amazon Ads search terms ready for exact-match harvesting from auto, broad, phrase, or other discovery campaigns. Use when Codex needs standalone or Rocketcart MCP-aware search-term harvesting: classify terms by harvest readiness, resolve live IDs, choose safe destination campaigns/ad groups, preflight duplicates/current negatives/destination feasibility, decide whether source negatives are justified, execute only explicitly approved rows, and read back/monitor Sponsored Products or Sponsored Brands actions without unsafe negatives, duplicate routing, stale approvals, brand-defense cuts, own-ASIN-defense cuts, launch/rank mistakes, or low-sample overfitting.

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Plan complete day trips, walking tours, and multi-stop itineraries with time budgets using Camino AI's journey.

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Use when planning the migration of a legacy system or monolith — analyzes the source codebase (current repo, or a repo URL cloned to a temp dir) and produces an evidence-based Strangler Fig migration plan with per-domain SPECs. Default target is .NET (Blazor WebAssembly, .NET MAUI, ASP.NET Core); other stacks allowed by user decision. Do NOT use to implement code or when approved SPECs already cover the migration.

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Break an application or feature into thin vertical user-story slices with clear acceptance criteria and user-observable value. Use when planning a backlog, decomposing a feature, writing implementation-ready stories, or turning requirements into deliverable slices.

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