「planner」の検索結果

Use when the user asks to "launch on Product Hunt / Hacker News", "prepare community or directory launch submissions", or "plan the launch submission waves"; produces per-platform submission packages — a Product Hunt tagline / gallery / first-comment skeleton, a factual Show HN title and text, per-subreddit posts with a self-promotion rules table, tiered directory waves, and a regional channel matrix including Chinese communities — plus a platform red-line check (never solicit votes or organize voting rings) and T-0 submission-status lines for the launch registry. Not for paid amplification — use content-amplifier; not for creator channels — use campaign-planner; not for launch telemetry readouts — use launch-monitor; not for ongoing community presence or pre-launch karma-building outside the launch window — use participation-warmup-planner. 社区发布/PH提交包/Show HN/目录波次/平台红线/中文渠道

日本語の概要は準備中です。原文の説明を表示しています。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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planner エージェントによるタスク分解。design.md を基に TaskCreate でネイティブタスクに登録。「タスクに分解して」「タスク分解して」「実装計画を立てて」「何から始める？」で発動。

Use when: planner mode, planning mode, proposal, scope definition, estimate, timeline, client message, external communication, pricing, delivery confidence, commercial risk, or project delivery planning.

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Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types. Use when creating agents in source control, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship'. NOT for Apex-based agent actions (use custom-agent-actions-apex). NOT for UI-based agent creation (use agentforce-agent-creation).

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Build production-grade AI agents from scratch using patterns inspired by production-grade agentic systems like Claude Code. Covers two audiences — (1) workflow builders who need planner+executor pipelines, human-in-the-loop checkpoints, and multi-agent orchestration, and (2) application embedders who need HTTP/SSE APIs, multi-user session management, and React frontend integration. Use when asked to build an AI agent, create a coding assistant, embed an agent in an app, build an API agent, clone Claude Code, build a CLI agent, design an agent architecture, scaffold an agentic system, or automate a multi-step workflow. Triggers on keywords like agent builder, agent blueprint, coding assistant, CLI agent, agentic tool, claude code clone, agent scaffold, agent architecture, workflow automation, multi-agent, planner executor, SSE streaming, agent API.

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Run `@metaharness/darwin evolve {repo}` to mutate a harness's seven policy surfaces (planner/contextBuilder/reviewer/retryPolicy/toolPolicy/memoryPolicy/scorePolicy), sandbox-score each variant, and promote only measured wins. The model is frozen; the harness evolves. Closes the loop ADR-150 opens (score+genome describe; evolve changes). Degrades gracefully when @metaharness/darwin is absent (ADR-150 + ADR-153 architectural constraints).

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Multi-stage series — a request for several classrooms that belong together, like 「7 天学 Python」, a four-week onboarding track, a semester unit split across lessons, or "turn this book into a lesson per chapter". Clarifies the series brief, gets the user's explicit sign-off on the full lesson list before spending anything, then builds the stages one at a time into a shared folder. Use when the ask is a sequence of classrooms; not for a single stage, however large.

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Use this skill whenever you need the allowed_tools / skills preset for a sub-agent role before calling spawn_subagent. Trigger especially when user mentions: "role", "spawn_subagent", "allowed_tools", or any OMP role name (executor, architect, analyst, critic, security-reviewer, code-reviewer, qa-tester, planner, explore, debugger, verifier). Also use when a /ultrawork, /ralph, /autopilot, /ultraqa, or /team workflow step asks for role tool/skill configuration.

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Consensus planning stage for Planner -> Architect -> Critic handoff to $ultragoal

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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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Use Codex (OpenAI's codex app-server) as a full agent provider — planning, tool orchestration, MCP tools, server-side history, session resume — alongside or instead of Claude. ChatGPT subscription or OpenAI API key, vault-only via the selected gateway. Per-group via `ncl groups config update --provider codex`. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).

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Works out what to reorder and when, from what is actually selling: pulls sales history and stock on hand from Shopify, Square, NetSuite, or an uploaded CSV, computes 7-day and 28-day sales velocity per item, projects a conservative stockout date for each, sizes a 60-day reorder quantity, and drafts the purchase orders and vendor emails to cover it. Flags the money sitting still too — overstock, dead stock, and zero-velocity items, reported as slow movers to clear rather than things to buy more of. Seasonality is applied where the history supports it and named where it does not. Nothing is ordered and no vendor is emailed without approval. Reach for this whenever stock comes up — "what do I need to reorder," "am I going to run out," "what should I order this month," "we keep running out of filters," "how much cash is sitting in the warehouse," "what is not moving," or "draft a PO for my supplier."

日本語の概要は準備中です。原文の説明を表示しています。

Use when adding, retiring, or auditing feature flags. Triggers on "add a flag", "ship behind a flag", "rollout plan", "kill switch", "stale flags", "flag debt", "LaunchDarkly", "GrowthBook", "Statsig", "Unleash", "Flipt", or any progressive-delivery question. Ships flag debt scanner, rollout planner, and kill-switch auditor (all stdlib Python), 4 references on flag taxonomy + provider trade-offs + rollout strategies + lifecycle, plus a /flag-cleanup slash command.

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Use when an ops leader (Director of CX, Head of Support, VP Ops, Head of BizOps, Head of IT ops, Head of Finance ops) is sizing ops capacity, building a headcount plan, modeling utilization risk, planning Q3 capacity or annual support capacity, or designing CS coverage — and needs Erlang-C queueing math, P90 demand sizing, shrinkage-adjusted FTE, manager-trigger thresholds, and a quarterly hiring sequence with ramp + attrition. Apply when sustained team utilization is above 80% or when the team is growing >50% in 12 months. Run before committing the headcount budget. This is NOT engineering capacity (see vpe-advisor for DORA + cycle time) and NOT strategic 3-year workforce planning (see chro-advisor).

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Use when running, diagnosing, or designing internal business operations — process documentation, vendor SLAs, capacity planning, internal comms, SOP/runbook authoring, procurement spend. Triggers on "BizOps review", "where's the bottleneck", "vendor health", "internal SOP", "all-hands deck", "spend categorization", "capacity for Q3", "process mapping". Forks context to route to one of six BizOps sub-skills (process-mapper, vendor-management, capacity-planner, internal-comms, knowledge-ops, procurement-optimizer) and returns a digest. Distinct from business-growth (external sales motion) and c-level-advisor (strategic, not operational).

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Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).

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Drive OpenAI's Codex CLI (`codex exec`) as a non-interactive coding sub-agent from inside Claude Code. Load WHENEVER you want to delegate a coding/analysis/refactor task to Codex, get a second opinion / adversarial review from another model, fan out parallel agents across files or worktrees, or run a long mechanical job while you stay the planner. Covers the exact `codex exec` flags, sandbox tiers, output capture, JSON/schema modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.

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Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.

日本語の概要は準備中です。原文の説明を表示しています。

Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.

日本語の概要は準備中です。原文の説明を表示しています。

Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.

日本語の概要は準備中です。原文の説明を表示しています。

Plan and organize your content calendar with AI-driven scheduling and topic suggestions.

日本語の概要は準備中です。原文の説明を表示しています。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

日本語の概要は準備中です。原文の説明を表示しています。

Use when planning legacy system migrations, codebase modernization, monolith decomposition, microservices consolidation, cross-language rewrites, or framework upgrades. Invoke for strangler fig pattern, incremental migration strategy, or refactoring roadmaps. Do NOT use for domain analysis (use domain-analysis), component sizing (use component-identification-sizing), or step-by-step decomposition plans (use decomposition-planning-roadmap).

日本語の概要は準備中です。原文の説明を表示しています。