Building LLM-Powered Applications with Claude

This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation.

Before You Start

Scan the target file (or, if no target file, the prompt and project) for non-Anthropic provider markers — import openai, from openai, langchain_openai, OpenAI(, gpt-4, gpt-5, file names like agent-openai.py or *-generic.py, or any explicit instruction to keep the code provider-neutral. If you find any, stop and tell the user that this skill produces Claude/Anthropic SDK code; ask whether they want to switch the file to Claude or want a non-Claude implementation. Do not edit a non-Anthropic file with Anthropic SDK calls.

Output Requirement

When the user asks you to add, modify, or implement a Claude feature, your code must call Claude through one of:

1. The official Anthropic SDK for the project's language (anthropic, @anthropic-ai/sdk, com.anthropic.*, etc.). This is the default whenever a supported SDK exists for the project.
2. Raw HTTP (curl, requests, fetch, httpx, etc.) — only when the user explicitly asks for cURL/REST/raw HTTP, the project is a shell/cURL project, or the language has no official SDK.

Never mix the two — don't reach for requests/fetch in a Python or TypeScript project just because it feels lighter. Never fall back to OpenAI-compatible shims.

Never guess SDK usage. Function names, class names, namespaces, method signatures, and import paths must come from explicit documentation — either the {lang}/ files in this skill or the official SDK repositories or documentation links listed in shared/live-sources.md. If the binding you need is not explicitly documented in the skill files, WebFetch the relevant SDK repo from shared/live-sources.md before writing code. Do not infer Ruby/Java/Go/PHP/C# APIs from cURL shapes or from another language's SDK.

Defaults

Unless the user requests otherwise:

For the Claude model version, please use {{OPUS_NAME}}, which you can access via the exact model string {{OPUS_ID}}. Please default to using adaptive thinking (thinking: {type: "adaptive"}) for anything remotely complicated. And finally, please default to streaming for any request that may involve long input, long output, or high max_tokens — it prevents hitting request timeouts. Use the SDK's .get_final_message() / .finalMessage() helper to get the complete response if you don't need to handle individual stream events

---

Subcommands

If the User Request at the bottom of this prompt is a bare subcommand string (no prose), search every Subcommands table in this document — including any in sections appended below — and follow the matching Action column directly. This lets users invoke specific flows via /claude-api <subcommand>. If no table in the document matches, treat the request as normal prose.

---

Language Detection

Before reading code examples, determine which language the user is working in:

1. Look at project files to infer the language:

   • *.py, requirements.txt, pyproject.toml, setup.py, Pipfile → Python — read from python/
   • *.ts, *.tsx, package.json, tsconfig.json → TypeScript — read from typescript/
   • *.js, *.jsx (no .ts files present) → TypeScript — JS uses the same SDK, read from typescript/
   • *.java, pom.xml, build.gradle → Java — read from java/
   • *.kt, *.kts, build.gradle.kts → Java — Kotlin uses the Java SDK, read from java/
   • *.scala, build.sbt → Java — Scala uses the Java SDK, read from java/
   • *.go, go.mod → Go — read from go/
   • *.rb, Gemfile → Ruby — read from ruby/
   • *.cs, *.csproj → C# — read from csharp/
   • *.php, composer.json → PHP — read from php/

2. If multiple languages detected (e.g., both Python and TypeScript files):

   • Check which language the user's current file or question relates to
   • If still ambiguous, ask: "I detected both Python and TypeScript files. Which language are you using for the Claude API integration?"

3. If language can't be inferred (empty project, no source files, or unsupported language):

   • Use AskUserQuestion with options: Python, TypeScript, Java, Go, Ruby, cURL/raw HTTP, C#, PHP
   • If AskUserQuestion is unavailable, default to Python examples and note: "Showing Python examples. Let me know if you need a different language."

4. If unsupported language detected (Rust, Swift, C++, Elixir, etc.):

   • Suggest cURL/raw HTTP examples from curl/ and note that community SDKs may exist
   • Offer to show Python or TypeScript examples as reference implementations

5. If user needs cURL/raw HTTP examples, read from curl/.

Language-Specific Feature Support

Managed Agents code examples: dedicated language-specific READMEs are provided for Python, TypeScript, Go, Ruby, PHP, Java, and cURL ({lang}/managed-agents/README.md, curl/managed-agents.md). Read your language's README plus the language-agnostic shared/managed-agents-*.md concept files. Agents are persistent — create once, reference by ID. Store the agent ID returned by agents.create and pass it to every subsequent sessions.create; do not call agents.create in the request path. The Anthropic CLI (ant) is one convenient way to create agents and environments from version-controlled YAML — see shared/anthropic-cli.md. If a binding you need isn't shown in the README, WebFetch the relevant entry from shared/live-sources.md rather than guess. C# does not currently have Managed Agents support; use cURL-style raw HTTP requests against the API.

---

Which Surface Should I Use?

Start simple. Default to the simplest tier that meets your needs. Single API calls and workflows handle most use cases — only reach for agents when the task genuinely requires open-ended, model-driven exploration.

Note: Managed Agents is the right choice when you want Anthropic to run the agent loop and host the container where tools execute — file ops, bash, code execution all run in the per-session workspace. If you want to host the compute yourself or run your own custom tool runtime, Claude API + tool use is the right choice — use the tool runner for automatic loop handling, or the manual loop for fine-grained control (approval gates, custom logging, conditional execution).

Third-party providers (Amazon Bedrock, Google Vertex AI, Microsoft Foundry): Managed Agents is not available on Bedrock, Vertex, or Foundry. If you are deploying through any third-party provider, use Claude API + tool use for all use cases — including ones where Managed Agents would otherwise be the recommended surface.

Decision Tree

What does your application need?

0. Are you deploying through Amazon Bedrock, Google Vertex AI, or Microsoft Foundry?
   └── Yes → Claude API (+ tool use for agents) — Managed Agents is 1P only.
   No → continue.

1. Single LLM call (classification, summarization, extraction, Q&A)
   └── Claude API — one request, one response

2. Do you want Anthropic to run the agent loop and host a per-session
   container where Claude executes tools (bash, file ops, code)?
   └── Yes → Managed Agents — server-managed sessions, persisted agent configs,
       SSE event stream, Skills + MCP, file mounts.
       Examples: "stateful coding agent with a workspace per task",
                 "long-running research agent that streams events to a UI",
                 "agent with persisted, versioned config used across many sessions"

3. Workflow (multi-step, code-orchestrated, with your own tools)
   └── Claude API with tool use — you control the loop

4. Open-ended agent (model decides its own trajectory, your own tools, you host the compute)
   └── Claude API agentic loop (maximum flexibility)

Should I Build an Agent?

Before choosing the agent tier, check all four criteria:

• Complexity — Is the task multi-step and hard to fully specify in advance? (e.g., "turn this design doc into a PR" vs. "extract the title from this PDF")
• Value — Does the outcome justify higher cost and latency?
• Viability — Is Claude capable at this task type?
• Cost of error — Can errors be caught and recovered from? (tests, review, rollback)

If the answer is "no" to any of these, stay at a simpler tier (single call or workflow).

---

Architecture

Everything goes through POST /v1/messages. Tools and output constraints are features of this single endpoint — not separate APIs.

User-defined tools — You define tools (via decorators, Zod schemas, or raw JSON), and the SDK's tool runner handles calling the API, executing your functions, and looping until Claude is done. For full control, you can write the loop manually.

Server-side tools — Anthropic-hosted tools that run on Anthropic's infrastructure. Code execution is fully server-side (declare it in tools, Claude runs code automatically). Computer use can be server-hosted or self-hosted.

Structured outputs — Constrains the Messages API response format (output_config.format) and/or tool parameter validation (strict: true). The recommended approach is client.messages.parse() which validates responses against your schema automatically. Note: the old output_format parameter is deprecated; use output_config: {format: {...}} on messages.create().

Supporting endpoints — Batches (POST /v1/messages/batches), Files (POST /v1/files), Token Counting, and Models (GET /v1/models, GET /v1/models/{id} — live capability/context-window discovery) feed into or support Messages API requests.

---

Current Models (cached: 2026-04-15)

ALWAYS use {{OPUS_ID}} unless the user explicitly names a different model. This is non-negotiable. Do not use {{SONNET_ID}}, {{PREV_SONNET_ID}}, or any other model unless the user literally says "use sonnet" or "use haiku". Never downgrade for cost — that's the user's decision, not yours.

CRITICAL: Use only the exact model ID strings from the table above — they are complete as-is. Do not append date suffixes. For example, use claude-sonnet-4-5, never claude-sonnet-4-5-20250514 or any other date-suffixed variant you might recall from training data. If the user requests an older model not in the table (e.g., "opus 4.5", "sonnet 3.7"), read shared/models.md for the exact ID — do not construct one yourself.

A note: if any of the model strings above look unfamiliar to you, that's to be expected — that just means they were released after your training data cutoff. Rest assured they are real models; we wouldn't mess with you like that.

Live capability lookup: The table above is cached. When the user asks "what's the context window for X", "does X support vision/thinking/effort", or "which models support Y", query the Models API (client.models.retrieve(id) / client.models.list()) — see shared/models.md for the field reference and capability-filter examples.

---

Thinking & Effort (Quick Reference)

Opus 4.7 — Adaptive thinking only: Use thinking: {type: "adaptive"}. `thinking: {type: "enabled", budget_tokens: N}