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Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.
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Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.
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Create patent line drawings from text or reference images through the PatentFig AI API. Convert existing drawings into scalable vector files for CAD, increase image resolution, and export figures in formats used for patent filing.
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Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.
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Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
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Cloudflare platform management via Wrangler CLI, Agents SDK (v0.13+), and Browser Rendering API. Deploy Pages, Workers, KV, R2, D1, Queues, Vectorize, Workflows, Hyperdrive. Build stateful AI agents with the full SDK stack — Agent, AIChatAgent, Think, Voice, sub-agents, fibers, HITL, scheduling, observability, Code Mode (Dynamic Worker sandboxes). Budget web scraping via cf_browser.py. Use when deploying to Cloudflare, managing CF infrastructure, configuring wrangler.toml/wrangler.jsonc, building AI agents or chat agents on Workers, adding voice, orchestrating sub-agents, or for cheap web scraping. Triggers: Cloudflare, wrangler, Pages, KV, R2, D1, Workers, Vectorize, Queues, Workflows, Hyperdrive, cf_browser, Browser Rendering, Agents SDK, Code Mode, codemode, MCP, Think, AIChatAgent, Voice agent, sub-agents, fibers, HITL, Dynamic Workers.
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Cloudflare全体の製品選択と対象Accountの文脈を確定するルータースキル。Workers、Pages、KV、D1、R2、Workers AI、Vectorize、Agents SDK、Flagship、Tunnel、WAF、Terraform等のどの製品・参照・専用Skillを使うか判断する。Cloudflare要件が複数製品にまたがる、製品名が未確定、Accountの選択が必要な場合に使う。Wrangler操作、Durable Objects、Turnstile、Email Service、Workersコード、本番デプロイは責務表の専用Skillへ委譲し、本Skillで実行手順を重複定義しない。数値・API・設定は記憶よりCloudflare公式ドキュメントの取得を優先する。
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Cloudflare Workers CLIでWorkers、KV、R2、D1、Vectorize、Hyperdrive、Workers AI、Containers、Queues、Workflows、Pipelines、Secrets Storeを開発・デプロイ・管理する。Wranglerコマンドを実行する前に使用し、最新の公式ドキュメントに基づく構文と安全規則を確認する。
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Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.
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Investigate a GitHub issue or reported problem, check whether it is a bug, and test a proof of concept. Explain the behavior changes and supporting evidence so the user can decide whether to proceed with the full fix.
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Store team conventions, troubleshooting notes, and project knowledge on a self-hosted Hindsight server. Retrieve relevant context before new tasks and draw on recorded experiences, including successful procedures, failed attempts, and individual preferences attributed to specific people.
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Find context from past coding sessions in Hindsight long-term memory. Reuse memories already loaded for the session, search for missing details, and reason over accumulated records when answering questions about decisions, project context, or preferences.
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Create a specialized Claude Code subagent with Hindsight long-term memory. Start with an empty agent or import prepared documents, then equip it with knowledge pages and memory search to reuse information across sessions.
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Store preferences, task lessons, and procedure outcomes in Hindsight, then retrieve relevant context before starting new work. It records detailed observations and can synthesize past experience into guidance for approaching the next task.
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Create an animated diagram showing request flows, background jobs, or feature changes. The result is a single SVG image for README files, pull requests, issues, or blog posts, with support for light and dark themes.
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Retrieve relevant Hindsight memories before planning or answering a request. Searches the agent’s own and shared memory stores, plus other relevant stores when needed, to bring past decisions, user preferences, and project context into the task.
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Carry context between scheduled or event-triggered routine runs using Hindsight memory. Add recall and recording steps to routine instructions so each run can use previous findings, resume unfinished work, compare changes, and leave guidance for the next run.
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Manage Hindsight memory for coding sessions: retrieve past decisions, store documents and initiative plans, correct outdated information, and configure memory by project. It also explains automatic conversation capture and helps investigate memory configuration problems.
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Record task handoffs between Bots in shared Hindsight memory, including progress, remaining work, sources, and caveats. Other Bots can retrieve the record later, continue unfinished work, and report the outcome back through shared memory.
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Plan how an AI application should use Hindsight memory. Identify useful information to retain, define user-scoping tags and memory bank settings, and specify how retrieved facts and synthesized knowledge should reach the model.
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Answer questions about past decisions, usual practices, and changes over time by reasoning across Hindsight memories. When information spans multiple memory banks, combine the answers and identify their sources, while acknowledging gaps or contradictions.
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Prepare and publish a core Hindsight release, then open a pull request for its changelog and announcement blog post. Covers version updates, package publication checks, and synchronization of the accompanying documentation references.
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Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.
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Take a pull request through repeated code review and fixes, verify that automated checks pass for the latest commit, and combine its changes into one commit when merging. Conflicts with the request’s goal require confirmation.
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