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Helps the user prompt Claude to generate and iteratively refine temporary, inline interactive visuals (charts, diagrams, and visualizations) during a conversation. Trigger when the user asks to draw a diagram, visualize a trend over time, or wants an explanation supported by interactive visuals.

A practical, structured workflow for deploying Claude in a law firm or in-house legal team, including choosing the right product for the job and rolling out connectors and practice-area plugins.

Write and refine Claude Skills (SKILL.md): naming, description writing for reliable triggering, instruction structure, testing/validation, and context-size management via companion files. Use when creating or reviewing a Skill, or when debugging why a Skill triggers incorrectly.

Configure and maintain Claude Code hooks: choosing lifecycle events, writing matchers, selecting hook types (command vs prompt), and applying safety/operational best practices. Use when a user wants to automate Claude Code workflows via hooks or needs help authoring or reviewing hook JSON and companion scripts.

Provides reusable frontend UI aesthetics guidance (typography, theme, motion, backgrounds) to avoid generic "AI default" designs when generating web UIs, landing pages, dashboards, and app layouts. Use when prompts mention frontend UI, landing pages, design polish, Tailwind/React components, themes, typography, motion, or visual distinctiveness.

Applies the Opus 4.7 prompting and session-tuning guidance from "Best practices for using Claude Opus 4.7 with Claude Code". Use when opening a Claude Code session with Opus 4.7, choosing an effort level (high / xhigh / max), batching questions, or deciding whether to nudge toward more tool use and parallel subagents.

Practical heuristics from the Claude Code team for designing, iterating, and pruning agent tools by thinking from the model’s perspective (“seeing like an agent”), with an emphasis on progressive disclosure.

Enables delegating well-defined coding tasks to a cloud sandbox from a web browser (useful for bug backlogs, routine fixes, and parallel coding work). Trigger when the user asks to connect a GitHub repo and have Claude implement changes without using a local terminal.

Build reliable, reviewable extraction prompts by assembling the right domain evidence and explicit time anchors (e.g., “most recent value before procedure start time”), and by using granular evaluation to locate failures in prompts vs context vs retrieval.

Guides building and deploying AI agents in financial services with emphasis on integration, regulation, and real-time risk controls. Trigger when the user asks how to safely operationalize agents for banking, insurance, wealth management, fraud, compliance, or financial operations.

Turn recurring sales leadership routines (meeting prep, weekly forecast rollups, and large-scale account scoring) into scheduled, repeatable workflows that assemble data from multiple systems and produce standardized outputs with human approval.

Prioritizes and operationalizes software patching for an environment where AI models quickly reverse patches into exploits. Use when planning patch policy, reducing time-to-patch on internet-exposed systems, triaging large CVE backlogs with KEV and EPSS, or rolling out automated patch deployment.

Position the six benefits of transitioning to agentic coding (timeline acceleration, onboarding compression, autonomous problem solving, scaling without linear headcount growth, systematic code quality, democratized access) and pair each with a concrete starter task. Use when building a business case for Claude Code, choosing which benefit to lead with for a specific audience, or onboarding a team that needs evidence before delegating real work.

Use Claude's Integrations and advanced Research together to pull context from your work apps and produce comprehensive cited reports. Apply when deciding which third-party app to connect via remote MCP servers, when planning multi-source investigations that span the web, Google Workspace, and connected services, or when standardizing how a team uses Integrations + Research instead of building bespoke automation.

Introduce engineers to agentic coding with Claude Code — how it differs from autocomplete and chat-based assistants, how a two-phase workflow (context gathering + planning, then implementation + coordination) plays out, what permission model to expect, and which onboarding prompts produce immediate value. Use when explaining Claude Code to a new team, deciding which workflows to delegate first, or grounding a pitch in the Rakuten case study.

Plan and implement resilient third-party API integrations with Claude. Use Claude.ai to enumerate failure modes, authentication risks, and rate-limit strategies before coding, then use Claude Code to generate typed clients, OAuth/JWT/key-rotation flows, and contract/refresh tests across the codebase. Apply when onboarding a new API, hardening a flaky integration, or standardizing how a team splits planning vs implementation work for integrations.

Get the most out of the bundled claude-api skill (now shipping in CodeRabbit, JetBrains, Junie, Resolve AI, Warp, and Claude Code) by triggering the four post-named workflows — improving cache hit rate, adding context compaction, upgrading to the latest Claude model, and onboarding a deep research agent — instead of looking up API parameters manually.

Use Anthropic Console's prompt generator to bootstrap a production-ready prompt template from a short task description, then know what prompt-engineering techniques the output already carries (role setting, chain-of-thought scratchpad, XML tags, handlebars variables) and how to edit, version, and evaluate the result.

Plan and run a Claude Cowork enterprise rollout the way Anthropic's own April 29, 2026 deployment guide recommends — pick a first business function, structure a pilot, walk the five-level maturity model, and execute a six-month champion-to-org-wide roadmap, anchored on the post's named internal teams and customer references (Thomson Reuters, Zapier, Jamf).

Apply Claude's web search feature (announced March 20, 2025, made globally available May 27, 2025) effectively. Helps users decide when web search is the right tool, enable it correctly with Claude 3.7 Sonnet, get cited responses, and reuse the four use cases the post calls out — sales, finance, research, and shopping.

Use the upgraded Anthropic Console as a single workspace to build, evaluate, and iterate Claude prompts before shipping. Walks through Workbench authoring, the prompt generator, side-by-side evaluation, prompt improvement, "Get Code" export, shareable prompts for team collaboration, and the extended thinking budget for Claude 3.7 Sonnet — based strictly on the March 6, 2025 launch post.

Decide when prompt caching helps, structure prompts so the cacheable prefix and dynamic suffix are cleanly separated, estimate cost and latency wins from the benchmarks reported in Anthropic's launch post, and pick the right Claude model among those that support caching.

Delegates research, parallel edits, or fresh-eyes reviews to subagents (separate AI instances with isolated context) in Claude Code. Use when a task requires reading 10+ files, has 3+ independent subtasks, needs a second opinion before commit, or a complex pipeline like design-then-implement-then-test. Covers four invocation methods (conversational, custom agent file, project policy, slash command skill), four practical patterns, and when NOT to use subagents.

Applies Anthropic's prompt engineering techniques to fix prompts that return generic, off-topic, inconsistent, or hallucinated output. Use when a user asks how to write better prompts, wants to improve an existing prompt, asks about chain-of-thought, response prefilling, output format control, or prompt chaining, or is debugging unreliable model behavior. Covers five core techniques and four advanced techniques with examples and a troubleshooting cheatsheet.

Applies three patterns for building applications on a model whose intelligence keeps improving — use general tools the model already knows, remove scaffolding the model can now do itself, and set boundaries only where UX, observability, or security demand it. Use when the user is designing an agent, LLM-powered app, or tool layer and is deciding how much structure to impose around the model.

Plan and run a Claude Security public-beta rollout for a Claude Enterprise organization — choose the access path (direct, partner platform, or services partner), scope scans by repository / directory / branch, schedule recurring runs, route findings into Slack/Jira/audit systems, and use the dismiss-with-reason and confidence-rating workflow to triage results, all anchored in the April 30, 2026 launch post.

Keep prompt cache hit rate high in long-running agents by following the patterns Claude Code's team uses — lay out static-then-dynamic, push updates into messages instead of editing the system prompt, never change models or tools mid-session, model state transitions as tools (Plan Mode), defer tool loading instead of removing tools, and implement compaction as cache-safe forking. Treat cache breaks as incidents.

Package and share Claude Code customizations using plugins, with a focus on when to use plugins and how to install them from Claude Code.

Add a structured planning-orchestration layer before code generation to make outcomes, assumptions, edge cases, and validation criteria explicit, producing a reviewable PRD-style plan.

A systematic workflow to debug faster with Claude.ai for rapid hypothesis generation and Claude Code for autonomous investigation, fixes, and verification.

Checklist and implementation notes for adopting Claude API capabilities for agentic applications (code execution, MCP connector, Files API, and extended prompt caching).

Use a lightweight checklist and prompt templates to evaluate a Claude connector from a directory, clarify required permissions, and define safe success criteria before you connect it.

Select, launch, and scale AI agent use cases in a startup with a pragmatic approach to risk, modularity, and human oversight.

Decide where to place Claude Code instructions (CLAUDE.md, rules, skills, subagents, hooks, output styles, or appended system prompt) based on load timing, compaction behavior, context cost, and authority.

Practical checklist for evaluating and integrating Claude tool use (now generally available) into an application, based on the official announcement.

Identify rigid CSS layout constraints and refactor them into responsive patterns with breakpoint checks and device-coverage tests.

Build and evaluate “grounded answers” experiences using Anthropic API Citations so users can trace each claim back to source passages.

Turn a Claude Code project case study into reusable implementation notes (problem, approach, agents, tests, constraints, and takeaways) without inventing details.

A repeatable workflow to import design systems into Claude Design, keep output on-brand, and hand off designs to Claude Code without restarting from screenshots.

Checklist for implementing model usage on Amazon Bedrock based on this Claude GA announcement, including considerations for agentic orchestration via AWS Lambda and enterprise customization.

Use Claude with Microsoft 365 context (SharePoint/OneDrive, Outlook, Teams) by framing questions for cross-tool enterprise search and producing source-backed reports.

Create a domain-expert partnership plan to benchmark and improve model performance on specialized agentic tasks.

Decision checklist for using latency-optimized Claude 3.5 Haiku on AWS Trainium2 and Amazon Bedrock Model Distillation for Claude 3 Haiku, based on the official announcement.

Produce structured first-pass finance deliverables and iterate with human validation, keeping work close to spreadsheets, decks, and source files.

Decide when to use Skills vs prompts, Projects, MCP, and subagents, and combine them into a coherent workflow.

Request and iteratively publish shareable Claude Code artifacts (live, interactive pages) built from full session context.

Package domain expertise into scalable Agent Skills using progressive disclosure (metadata, SKILL.md, and on-demand references).

Stage-based playbook for using Claude across Idea, MVP, Launch, and Scale to build an AI-native startup.

Reusable planning and iteration patterns drawn from multiple winning projects in Claude’s “Built with Opus 4.7” hackathon.

Pair a lower-cost executor model with a higher-capability advisor model to get targeted guidance for hard decisions without paying frontier-model cost for every token.