Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.

Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.

Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.

Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.

Sets up Caliber for automatic AI agent context sync. Installs pre-commit hooks so CLAUDE.md, Cursor rules, and Copilot instructions update automatically on every commit. Use when Caliber hooks are not yet installed or when the user asks about keeping agent configs in sync.

Query the local AST-derived code graph (functions, classes, calls, imports) for structural codebase questions — what calls X, what does Y import, where is Z defined, blast radius of a change. The graph rebuilds automatically after each agent turn; use hivemind_graph_search and hivemind_graph_neighborhood tools (no manual build step).

Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/. Use whenever the user mentions a goal, objective, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X", or any actionable work item — the goal system replaced the legacy `hivemind tasks` CLI and now covers both objectives and tasks.

Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).

Deploy, configure, and maintain web applications, static sites, and edge functions on Cloudflare Pages and Workers. Use when configuring wrangler.toml or wrangler.jsonc, managing _routes.json, _headers, and _redirects, binding storage (KV, D1, R2), debugging edge functions, or executing deployments with Wrangler CLI.

Define product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category. Use when the user mentions "positioning", "competitive alternatives", "how to position", "market category", "positioning canvas", "repositioning", "category creation", "what category are we in", or "why prospects dont get what we do". Also trigger when launching a new product, entering a crowded market, or diagnosing why prospects dont grasp the product's value. Covers the positioning canvas and team workshops. For customer jobs analysis, see jobs-to-be-done. For go-to-market, see crossing-the-chasm.

根据提供的示例或品牌指导，以独特的语气撰写文章、指南、博客帖子、教程、新闻简报等长篇内容。当用户需要超过一段的精致书面内容时使用，尤其是当语气一致性、结构和可信度至关重要时。

Conventions every RSpec spec in ruby-git follows, unit or integration: naming the subject in `RSpec.describe`, one `describe` per method, the spec path, testing through the public interface, `context` and `it` wording, nesting, `subject` and `let`, `allow` and `expect`, verifying doubles, error assertions, edge-case placement, observable behavior, random order, the forbidden stubbing forms, no conditional logic or declared constants in a spec, and where shared contexts live. Use when writing, reviewing, or auditing any spec under spec/.

Guide for using ToolHive CLI (thv) to run and manage MCP servers, skills, and AI-client plugins. Use when managing `thv ai-plugin` installs, project sync or upgrades, or plugin publishing for Claude Code or Codex; also use for MCP server and skill lifecycle commands. Covers server lifecycle, registries, secrets, client registration, builds, permissions, skills, and AI plugins. NOT for Kubernetes operator usage, ToolHive development/contributing, or general AI-client configuration unrelated to ToolHive.

Runtime guard refactoring guidance for lodash-es predicates, import style and verification. Use when explicitly refactoring runtime type or empty-string guards.

Review a concrete Antigravity Manager diff for semantic defects, boundary violations and evidence gaps. Use for pull request reviews or the final review after implementation validation; this skill does not select or rerun tests.

Pre-PR code review gate. Run before opening a pull request, and again before pushing a substantive update to an open one — not per commit. Required when the PR's branch diff touches Python under veomni/, tasks/ or tests/, or CI workflows, pyproject.toml, uv.lock, docker/ or configs/. Also trigger proactively for runtime or configuration changes that span multiple files, touch shared infrastructure (BaseTrainer, distributed, model loading, data pipeline, ops dispatch), or have uncertain safety. Docs, comments and .agents/-only changes use the self-check below. The review launches a subagent that checks implementation quality, multi-file consistency, and known constraint violations, then rates the change as safe/needs-attention/risky.

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

Implement authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web applications.