Use when writing or reviewing Python code in this repo that calls Deepgram Text Intelligence / Read (`/v1/read`) for sentiment, summarization, topic detection, and intent recognition on text input. Covers `client.read.v1.text.analyze(...)` with body `text` or `url`. Use `deepgram-python-audio-intelligence` when the source is audio instead of text. Triggers include "read API", "text intelligence", "analyze text", "sentiment", "summarize text", "topics", "intents", "read.v1".
npx skills add https://github.com/deepgram/deepgram-python-sdk --skill deepgram-python-text-intelligence
Analyze plain text (or a hosted text URL) for sentiment, summarization, topics, and intents via /v1/read.
Use a different skill when:
deepgram-python-audio-intelligence (same analytics, applied at transcription time).from dotenv import load_dotenv
load_dotenv()
from deepgram import DeepgramClient
client = DeepgramClient()
Header: Authorization: Token <api_key>.
response = client.read.v1.text.analyze(
request={"text": "Hello, world! This is a sample text for analysis."},
language="en",
sentiment=True,
summarize=True, # /v1/read is boolean-only (see gotchas)
topics=True,
intents=True,
)
if response.results.sentiments:
print("sentiment avg:", response.results.sentiments.average)
if response.results.summary:
print("summary:", response.results.summary.text)
if response.results.topics:
print("topics:", response.results.topics.segments)
if response.results.intents:
print("intents:", response.results.intents.segments)
Pass request={"text": "..."} for raw text OR request={"url": "https://..."} for a hosted plain-text document.
from deepgram import AsyncDeepgramClient
client = AsyncDeepgramClient()
response = await client.read.v1.text.analyze(request={"text": "..."}, language="en", sentiment=True)
| Param | Type | Notes |
|---|---|---|
| request | {"text": str} or {"url": str} | One of these is required |
| language | str | Required for most analytics. English only today. |
| sentiment | bool | Per-segment + average sentiment |
| summarize | bool | /v1/read accepts boolean only. The SDK type alias TextAnalyzeRequestSummarize = typing.Union[typing.Literal["v2"], typing.Any] is shared with Listen and is broader than what Read actually supports — the analyze method docstring states: "For Read API, accepts boolean only." (Listen's summarize="v2" is a different product — see deepgram-python-audio-intelligence.) |
| topics | bool | Topic detection per segment |
| intents | bool | Intent recognition per segment |
| custom_topic / custom_topic_mode | list[str] / str | User-defined topics |
| custom_intent / custom_intent_mode | list[str] / str | User-defined intents |
| callback, callback_method, tag | | Async callback + metadata |
response.results.summary.text
response.results.sentiments.segments[]
response.results.sentiments.average
response.results.topics.segments[]
response.results.intents.segments[]
response.metadata
See reference.md → "Read V1 Text" for full shape. Request body model: ReadV1RequestParams.
reference.md — "Read V1 Text"./llmstxt/developers_deepgram_llms_txt.Token auth, not Bearer.summarize on /v1/read is boolean only. Pass True or False. Do not pass "v2" on /v1/read — that's a Listen-only option (see deepgram-python-audio-intelligence). The SDK type Union[Literal["v2"], Any] is shared with Listen and wider than Read actually accepts; the analyze docstring clarifies: "For Read API, accepts boolean only." The generated wire test passing summarize="v2" against a mock server is a Fern artifact and does not indicate real /v1/read support.language is required for the gated analytics features above.request=, not query parameters. Don't confuse with /v1/listen which takes audio as the body.custom_topic_mode="extended", "strict") or they are ignored.examples/40-text-intelligence.pytests/wire/test_read_v1_text.pyFor cross-language Deepgram product knowledge — the consolidated API reference, documentation finder, focused runnable recipes, third-party integration examples, and MCP setup — install the central skills:
npx skills add deepgram/skills
This SDK ships language-idiomatic code skills; deepgram/skills ships cross-language product knowledge (see api, docs, recipes, examples, starters, setup-mcp).
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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
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React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Take deepgram/deepgram-python-text-intelligence from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npx.
Without those the skill loads but fails at the first command.