# Live Docs Lookup

> Use when building with any AI SDK or API — Anthropic, OpenAI, or Google. Fetches current documentation in real time before answering, so stale training data doesn't cause bugs. Trigger on: model selection, tool use, function calling, streaming, prompt caching, batch processing, SDK setup, or any AI API integration, planning, debugging, or code review.

- Skill: `dhegde11/live-docs-lookup` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add dhegde11/live-docs-lookup`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dhegde11/live-docs-lookup/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dhegde11 (https://skillmd.com/u/dhegde11)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dhegde11/live-docs-lookup

---


## Why this skill exists

AI platforms change constantly: model IDs get renamed, parameters get
deprecated, new features launch behind flags, entire APIs get replaced. Your
training data has a cutoff — this skill fetches live docs before you advise on
any AI SDK integration, catching the gap between what you remember and what's
actually current.

A 30-second doc check prevents hours of wasted implementation effort.

---

## Step 1: Detect which provider is in scope

Check imports, environment variables, model names, or the user's stated intent:

| Signal | Provider |
|--------|----------|
| `import anthropic`, `from anthropic import`, `ANTHROPIC_API_KEY`, model names like `claude-*`, `opus`, `sonnet`, `haiku` | **Anthropic** |
| `from openai import`, `import openai`, `OPENAI_API_KEY`, model names like `gpt-*`, `o1`, `o3`, `chatgpt-*` | **OpenAI** |
| `import google.generativeai`, `from google import genai`, `import vertexai`, `GOOGLE_API_KEY`, model names like `gemini-*` | **Google** |

If multiple providers are in scope (e.g. a multi-provider app), fetch docs for each.
If unclear, ask the user which SDK they're targeting before fetching.

---

## Step 2: Fetch the relevant live docs

Use your web fetching tool (WebFetch, web_search, browse, or equivalent).

**Always fetch the models page first** — model IDs are the most common source
of outdated guidance, and the user will copy-paste whatever you put in example
code. Stale IDs produce confusing "model not found" errors.

**If a URL fails or returns a redirect/404**, don't stop — search for it instead.
Restrict the search to the provider's official docs domain to avoid landing on
blog posts or unofficial mirrors:
- Anthropic: `site:platform.claude.com/docs`
- OpenAI: `site:developers.openai.com`
- Google: `site:ai.google.dev`

Example queries: `site:platform.claude.com/docs adaptive thinking` or
`site:developers.openai.com responses API`. Doc URLs move; the information
is always findable on the canonical domain.

Fetch only what's relevant to the task. Three parallel fetches is ideal; don't
flood context with docs the user doesn't need.

### Anthropic

| Feature | URL |
|---------|-----|
| **Model IDs** *(always fetch)* | `https://platform.claude.com/docs/en/about-claude/models/overview.md` |
| Messages API | `https://platform.claude.com/docs/en/api/messages` |
| Tool use / function calling | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview.md` |
| Streaming | `https://platform.claude.com/docs/en/build-with-claude/streaming.md` |
| Extended / adaptive thinking | `https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking.md` |
| Prompt caching | `https://platform.claude.com/docs/en/build-with-claude/prompt-caching.md` |
| Computer use | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/computer-use.md` |
| Batch processing | `https://platform.claude.com/docs/en/build-with-claude/batch-processing.md` |
| Files API | `https://platform.claude.com/docs/en/build-with-claude/files.md` |
| Code execution tool | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool.md` |
| Structured outputs | `https://platform.claude.com/docs/en/build-with-claude/structured-outputs.md` |
| SDK setup | `https://platform.claude.com/docs/en/api/client-sdks` |
| Rate limits | `https://platform.claude.com/docs/en/api/rate-limits.md` |

### OpenAI

| Feature | URL |
|---------|-----|
| **Model IDs** *(always fetch)* | `https://developers.openai.com/api/docs/models` |
| Chat completions | `https://developers.openai.com/api/reference/chat-completions/overview` |
| Responses API | `https://developers.openai.com/api/reference/responses/overview` |
| Function calling / tools | `https://developers.openai.com/api/docs/guides/function-calling` |
| Streaming | `https://developers.openai.com/api/docs/guides/streaming-responses` |
| Structured outputs | `https://developers.openai.com/api/docs/guides/structured-outputs` |
| Batch API | `https://developers.openai.com/api/docs/guides/batch` |
| Rate limits | `https://developers.openai.com/api/docs/guides/rate-limits` |

### Google / Gemini

| Feature | URL |
|---------|-----|
| **Model IDs** *(always fetch)* | `https://ai.google.dev/gemini-api/docs/models` |
| Text generation | `https://ai.google.dev/gemini-api/docs/text-generation` |
| Function calling | `https://ai.google.dev/gemini-api/docs/function-calling` |
| Streaming | `https://ai.google.dev/gemini-api/docs/text-generation#streaming` |
| Structured outputs | `https://ai.google.dev/gemini-api/docs/structured-output` |
| File API | `https://ai.google.dev/gemini-api/docs/files` |

---

## Step 3: Surface a brief summary

Present only what's relevant and potentially surprising given your training
data. Aim for 5–10 bullet points, not a wall of text:

**Current recommended models:** [list the relevant current model IDs]

**Key API details for [features in scope]:** [params, headers, syntax that matters]

**Watch out for:** [deprecations, gotchas, recently changed behavior — omit if nothing notable]

If the docs confirm what you'd expect:
*"Docs confirm current expected behavior. Recommended model: `claude-opus-4-6`."*

---

## Step 4: Proceed with the original task

Carry the grounded context forward into all code, tests, plans, and reviews.
The correct model IDs, parameter names, and feature knowledge should flow
through naturally — don't repeat the summary, just use it.

**If invoked before a superpowers skill:** proceed to invoke that skill next.
The live context is now loaded — carry it forward.

---

## Common things to verify

Things that change often and are most likely to be wrong in training data:

### Anthropic
- **`budget_tokens` / thinking params** — syntax and supported models have changed across versions; verify current API shape in docs
- **`output_format` / output config params** — naming has changed; verify current parameter structure from docs
- **Model ID suffixes** — never construct date-suffixed IDs from memory; copy exact aliases from the models page
- **Beta headers** (Files API, Compaction, etc.) — required headers and values change; verify current values from docs before using
- **Docs base URL** — has moved before; if a URL fails, re-find on `platform.claude.com/docs`

### OpenAI
- **API surface choice** — verify which API the docs currently recommend for the user's use case
- **Model IDs** — always copy from the live models page
- **SDK migration details** — confirm version-specific breaking changes before suggesting code
- **Output formatting features** — verify the current recommended pattern from the docs

### Google
- **Gemini API vs Vertex AI** — clarify which surface the user is targeting, then fetch the matching docs
- **SDK choice and imports** — verify the current recommended SDK/import path from live docs
- **Model IDs** — always verify from the live models page

---

## Superpowers integration

Live doc lookup is most valuable before any planning, implementation, testing,
debugging, or review of AI SDK work — catching stale assumptions before they
get baked into a plan or test suite.

| Superpowers skill | Why live docs matter |
|------------------|-----------------------|
| **brainstorming** | Architecture decisions bake in model choices and API patterns |
| **writing-plans** | Plans contain model IDs, API call patterns, parameter names |
| **test-driven-development** | Tests depend on exact response formats, param names, stop reasons |
| **systematic-debugging** | Debugging API errors needs current known behavior, not assumptions |
| **executing-plans** | Plans may not have had live doc grounding at write time |
| **subagent-driven-development** | Subagents write code independently; they need accurate API context upfront |
| **dispatching-parallel-agents** | Same — agents need correct params from the start |
| **requesting-code-review** | Reviewers need current docs to spot stale patterns |
| **verification-before-completion** | Verifying AI API code requires knowing current expected behavior |