# Claude Api

> |-

- **Type:** Skill
- **Install:** `agentstack add skill-rakib-nyc-skillassay-claude-api`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [rakib-nyc](https://agentstack.voostack.com/s/rakib-nyc)
- **Installs:** 0
- **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [rakib-nyc](https://github.com/rakib-nyc)
- **Source:** https://github.com/rakib-nyc/skillassay/tree/main/test/fixtures/anthropic-official/skills/claude-api
- **Website:** https://www.npmjs.com/package/skillassay

## Install

```sh
agentstack add skill-rakib-nyc-skillassay-claude-api
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

**If WebFetch or repository access fails** (network restricted, timeouts, clone blocked): do not keep retrying — write code from the patterns and namespace/package tables in the `{lang}/` file, run the compiler or interpreter on it, and iterate on the error output. For statically-typed SDKs (C#, Java, Go) a compile-fix loop against local errors reaches working code faster than blocked network research.

## Defaults

Unless the user requests otherwise:

For the Claude model version, please use Claude Opus 5, which you can access via the exact model string `claude-opus-5`. 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

## ⚠️ API Drift — Your Training Prior May Be Stale

Several common Claude API shapes changed in 2025–2026. If you recall a pattern from training, verify it against the `{lang}/` files in this skill before writing — the rows below are the most frequent drift points:

| Area | Stale prior | Current API |
|---|---|---|
| Extended thinking | `thinking: {type: "enabled", budget_tokens: N}` | On Claude 4.6+ models: `thinking: {type: "adaptive"}`. `budget_tokens` is deprecated on Opus 4.6 / Sonnet 4.6 and **rejected with a 400** on Fable 5 / Sonnet 5 / Opus 5 / 4.8 / 4.7. Pre-4.6 models still use `budget_tokens`. |
| Web search / web fetch tool type | `web_search_20250305`, `web_fetch_20250910` | `web_search_20260209`, `web_fetch_20260209` (dynamic filtering) on Opus 5/4.8/4.7/4.6, Sonnet 5, and Sonnet 4.6. Older models keep the basic variants; on Vertex AI only basic `web_search_20250305` is available (web fetch is not on Vertex) — see the Server Tools QR below. |
| PHP parameter names | snake_case wire names as named args (`max_tokens`) | Top-level named args are camelCase (`maxTokens`). Nested array keys vary by feature (e.g. `'taskBudget'`, `'skillID'`, `'mcp_server_name'`) — copy the exact key from the documented example; do not bulk-convert. |
| Managed Agents credentials | Keep secrets host-side via custom tools (the only option before vaults shipped) | Vault `environment_variable` credentials — stored by Anthropic, substituted at egress, never visible in the sandbox (`shared/managed-agents-tools.md` → Vaults). Host-side custom tools remain the fallback for self-hosted sandboxes. |

The `{lang}/` files in this skill are authoritative over recalled patterns.

---

## 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 `. If no table in the document matches, treat the request as normal prose.

| Subcommand | Action |
|---|---|
| `migrate` | Migrate existing Claude API code to a newer model. **Read `shared/model-migration.md` immediately** and follow it in order: Step 0 (confirm scope — ask which files/directories before any edit), Step 1 (classify each file), then the per-target breaking-changes section. Do not summarize the guide — execute it. If the user did not name a target model, ask which model to migrate to in the same turn as the scope question. After the per-target changes are applied, audit the in-scope prompt text, tool descriptions, and request code against `shared/prompt-audit.md` — prompting written for the source model is part of every migration, and it does not announce itself. |
| `prompt-audit` | Audit existing prompts, skills, and tool descriptions for dated patterns ("cruft") written for older models. **Read `shared/prompt-audit.md` immediately** and follow it in order: Step 0 (establish scope and target model from the request and the repository — state the assumptions in the report, do not stop to ask), inventory, provenance, then the pattern scan. Produce both deliverables in full — the audit report (findings with `file:line`, pattern, why it's obsolete for the target model, confidence) and a proposed diff — without pausing for confirmation; apply edits only if the request explicitly asked for them. Do not summarize the guide — execute it. |

---

## Language Detection

First decide whether the request involves a specific SDK language at all. Some tasks don't: auditing prompt text (`prompt-audit`), choosing a model, pricing and limits questions, and conceptual API questions are language-agnostic. For those, skip this section and don't ask the user for a language.

When the task does involve reading or writing SDK code, determine which language the user is working in before reading code examples:

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

Every SDK language above supports both the beta Tool Runner and Managed Agents (beta) — Python (`@beta_tool` decorator), TypeScript (`betaZodTool` + Zod), Java (annotated classes), Go (`BetaToolRunner` in the `toolrunner` pkg), Ruby (`BaseTool` + `tool_runner`), C# (`BetaToolRunner` + raw JSON schema), PHP (`BetaRunnableTool` + `toolRunner()`); code entry points are in the Tool Use Patterns quick reference below. cURL is raw HTTP (no SDK features) and supports Managed Agents.

> **Managed Agents code examples**: see the reading guide in the `## Managed Agents (Beta)` section below.

---

## 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. "Simplest" means the least code you own: for a hosted, scheduled, or memory-backed agent, Managed Agents is usually the simplest option (no loop code, no state files, no scheduler), even though it's a bigger platform.

| Use Case                                        | Tier            | Recommended Surface       | Why                                                          |
| ----------------------------------------------- | --------------- | ------------------------- | ------------------------------------------------------------ |
| Classification, summarization, extraction, Q&A  | Single LLM call | **Claude API**            | One request, one response                                    |
| Batch processing or embeddings                  | Single LLM call | **Claude API**            | Specialized endpoints                                        |
| Multi-step pipelines with code-controlled logic | Workflow        | **Claude API + tool use** | You orchestrate the loop                                     |
| Custom agent with your own tools                | Agent           | **Claude API + tool use** | Maximum flexibility                                          |
| Server-managed stateful agent with workspace    | Agent           | **Managed Agents**        | Anthropic runs the loop and hosts the tool-execution sandbox |
| Persisted, versioned agent configs              | Agent           | **Managed Agents**        | Agents are stored objects; sessions pin to a version         |
| Long-running multi-turn agent with file mounts  | Agent           | **Managed Agents**        | Per-session containers, SSE event stream, Skills + MCP       |
| Agent that runs on a schedule (cron, "every night") | Agent       | **Managed Agents** — scheduled deployments | Deployments fire sessions autonomously; no client-side scheduler |

> **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 the agentic loop — its per-turn hooks still give you approval gates, logging, error interception, and conditional execution (see `shared/tool-use-concepts.md`) — or the manual loop when you want to own the entire loop yourself.

> **Cloud-provider access.** **Claude Platform on AWS** is Anthropic-operated with same-day API parity — see `shared/claude-platform-on-aws.md` for client setup. For per-feature availability on **Claude Platform on AWS**, **Amazon Bedrock**, **Google Vertex AI**, and **Microsoft Foundry**, see `shared/platform-availability.md` — that table is the single source of truth in this skill; do not infer availability from anywhere else.

### Building an Agent: Four Approaches

Once you've decided you actually need an agent (open-ended, model-driven tool use), there are four distinct ways to build one. Two independent questions separate them: **who supplies the harness** (the agent loop + context management) and **who supplies the deployment** (the infra the agent runs on). The Tool Runner and the Claude Agent SDK both supply a *harness only* — you still host and deploy them yourself — which is why they're easy to conflate. Managed Agents (CMA) is the only option that supplies **both** the harness *and* managed deployment; the manual loop supplies neither.

| # | Approach | You write | Harness & deployment | Tools available | Use when |
|---|----------|-----------|----------------------|-----------------|----------|
| 1 | **Claude API — manual loop** | The `while stop_reason == "tool_use"` loop yourself | You build the harness; you host | Only tools you define | You want to own the *entire* loop — no beta dependency, or a control flow the Tool Runner's per-turn hooks don't fit |
| 2 | **Claude API — Tool Runner** (`client.beta.messages.tool_runner` + `@beta_tool` / `betaZodTool`) | Just the tool functions | SDK supplies the loop (**harness only**); you host | Only tools you define | A custom-tool agent without hand-writing the loop (most cases). Per-turn hooks still give you approval gates, error interception, result modification (e.g. `cache_control`), retries, streaming, and compaction |
| 3 | **Managed Agents** (REST, beta) | Agent config + your tool results | Anthropic supplies the harness **and** hosts a per-session sandbox (**harness + deployment**) | Anthropic-hosted sandbox (bash, files, code exec) + Skills/MCP + your tools | You want Anthropic to run the loop *and* host the per-session workspace; persisted/versioned configs; long-running sessions |
| 4 | **Claude Agent SDK** — *separate product* (`claude-agent-sdk` / `@anthropic-ai/claude-agent-sdk`) | A prompt + options | SDK supplies the Claude Code harness + built-in tools (**harness only**); you host | Built-in Read/Write/Edit/Bash/Glob/Grep/WebSearch/WebFetch + MCP + subagents | You want a batteries-included coding/filesystem agent running on your own infra |

The harness/deployment split is the key mental model: options 1, 2, and 4 all **leave deployment to you**; only option 3 (CMA) adds managed deployment. Options 1–3 are what this skill generates; option 4 is a different library with its own docs — see the disambiguation below.

> **Tool Runner ≠ Claude Agent SDK.** These sound alike but are different packages:
> - **Tool Runner** is part of the regular Anthropic API SDK (`anthropic` / `@anthropic-ai/sdk`), reached via `client.beta.messages.tool_runner`. It automates the request → execute → loop cycle *for tools you define*. No built-in tools, no filesystem access, no sandbox — you supply every tool and host the compute. It is option 2 above, a thin helper over `POST /v1/messages`.
> - **Claude Agent SDK** (`claude-agent-sdk` / `@anthropic-ai/claude-agent-sdk`) is Claude Code packaged as a library. It ships built-in tools (file read/write/edit, bash, grep, web search), the full agent loop, context management, hooks, subagents, permissions, and sessions. You call `query(prompt, options)` and it drives everything.
>
> Both are **harness-only — you host and deploy them.** The difference is scope of harness: the Tool Runner loops over tools *you* define (with per-turn hooks for approval, interception, result modific

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [rakib-nyc](https://github.com/rakib-nyc)
- **Source:** [rakib-nyc/skillassay](https://github.com/rakib-nyc/skillassay)
- **License:** Apache-2.0
- **Homepage:** https://www.npmjs.com/package/skillassay

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-rakib-nyc-skillassay-claude-api
- Seller: https://agentstack.voostack.com/s/rakib-nyc
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.