「state」の検索結果

Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

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Build functional software prototypes and MVPs without deep technical skills by using AI agents as "developers in your pocket." Use this skill when you need to validate a new product concept, build custom back-office tools (e.g., a real estate data manager), or create high-fidelity demos to unblock engineering roadmaps.

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。

AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Build multi-agent AI workflows with orchestration, tool use, and state management

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Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

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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".

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。

Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.

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。

Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.

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。

Find and qualify real estate agencies in a given suburb

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。

Expert FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any question about technology cost or waste in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.

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Build a 10-component messaging library from your positioning + ICP + competitor aggregate. Produces positioning statement, value propositions, key differentiators, taglines, CTA variants, proof points, messaging hierarchy, status-quo alternatives. Writes to marketing/messaging/messaging.md as the canonical messaging library every downstream content / paid / outbound / lifecycle skill reads. Triggers - "product messaging", "messaging library", "value props", "messaging framework", "build messaging", "messaging components"

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Develop positioning strategy with anchor, differentiators, status-quo alternatives, and a positioning statement. Reads from marketing/icp/ICP.md + marketing/competitors/aggregate.md. Writes to marketing/positioning/positioning.md as the canonical positioning every downstream skill reads. Triggers - "positioning", "category definition", "how do we position", "positioning strategy", "anchor + differentiators"

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Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.

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The commit-R protocol: a test-driven rewrite of one compartment of Anabasis v2. Commit what is in flight, remove the compartment's tests in an "R:" commit, write them again from a stated hypothesis so the production code fails on purpose, remove the production files in a second "R:" commit, then write the production code again, rewrite the neighbouring functions the old shape bent, and delete what served only it. Use when the operator says "commit R", "commit-R protocol", "remove and rewrite the tests" or "rewrite from scratch", or asks for a test-first pass that looks for a faster, simpler path to the same outcome.

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Hand a question about Anabasis v2 to an outside Oracle model as one task document and one zip. The Oracle reads GitHub; the zip carries what it cannot fetch: campaign logs and kept harnesses through `bun run runs zip`, a reader's output, predictions with how each resolved, patches for commits never pushed. Use when the operator asks to prepare an Oracle question, a report request, a problem statement or a second opinion on a run, a reading or a design, or to send the latest runs to an outside model. Produces local files; uploading is the operator's.

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。

Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The plan-gate panel is proportional (0-5 seats, by governance weight) and runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/SKILL.md`.)

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。

Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The heavy multi-model plan-gate panel runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/SKILL.md`.)

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。

Read-only runbook for how the VNX fabric actually works — state resolution, the single-entry dispatch door, gate invocation, the horizon planning layer, the plan-gate panel, and the hard gotchas that repeatedly bite (PATH-break, dual-CLI, codex-cert, classifier-protected actions). Use when you need to look up a fabric operation instead of rediscovering it, or when a fabric command behaves unexpectedly. Companion to the t0-orchestrator skill (judgment) and vnx-manager (infra maintenance); this one is pure reference.

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Backward-compat alias for @horizon (this skill was renamed from `pm` to `Horizon` on 2026-07-05). USE THIS only when invoked directly as `/pm` or via an existing `@pm` delegation reference — it redirects immediately to @horizon, the strategic owner of the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. Prefer `@horizon` / `vnx horizon` for anything new; this alias exists only for transition safety.

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。

Design engineering principles for making interfaces feel polished. Use when building UI components, reviewing frontend code, implementing animations, hover states, shadows, borders, typography, micro-interactions, enter/exit animations, or any visual detail work. Triggers on UI polish, design details, "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, image outlines, box shadows.

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Read and manage Linear issues, projects, cycles, teams, comments, and labels. Use this whenever the user wants to list their issues, search issues, create or update an issue, move an issue between states, add a comment, check a project or cycle, look up a team, or ask "what's on my plate in Linear" — even if they don't say "API" or "GraphQL". Also use it for any linear.app URL or an issue identifier like "ENG-123". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。

Run SQL against Amazon Redshift — submit statements, poll status, page through results, and browse databases/schemas/tables. Use this whenever the user wants to query Redshift (provisioned cluster or Serverless), ask "what tables are in this schema", check a query's status, or mentions `redshift-data`, a Redshift cluster identifier / workgroup name, or a `redshift-data.{region}.amazonaws.com` endpoint. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。

Maintain the pure-Go SQLite notification outbox, public parser, runtime messaging-provider injection, worker leases, and registration delivery E2E tests. Excludes SMTP transports and registration enrollment state.

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。