langextract-llm-powered-structured-text-extraction

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Author repo skills-registry

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: LangExtract by Google is a Python library for extracting structured information from unstructured text using LLMs with precise s…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Installation”, “Source”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name langextract-llm-powered-structured-text-extraction directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Installation / Source” before expanding.

Boundaries And Review

Dependencies: Prepare Vendor-specific API keys before running a full task.
Permissions: Declared permissions include read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Data
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @tomevault-io · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: Required · Vendor-specific
Detected OS requirements: Unspecified
Runtime requirements: Python
Detected file/system behavior: Read-only
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview langextract-llm-powered-structured-text-extraction.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# LangExtract LLM-Powered Structured Text Extraction

LangExtract by Google is a Python library for extracting structured information from unstructured text using LLMs with precise source grounding. With 35,000+ GitHub stars, it handles everything from clinical notes to literary analysis, producing verified extraction results with exact source text mappings and interactive visualizations.

## Installation

Use the upstream install or setup path that matches your environment:
- pip install langextract
- git clone https://github.com/google/langextract.git
- pip install -e .
- pip install -e ".[dev]"

Requirements and caveats from upstream:
- LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifyi...
- **Note:** Using cloud-hosted models like Gemini requires an API key. See the [API Key Setup](#api-key-setup-for-cloud-models) section for instructions on how to get and configure your key.
- python

Basic usage or getting-started notes:
- [Quick Start](#quick-start)
- Extract structured information with just a few lines of code.
- ### 1. Define Your Extraction Task

- Source: https://github.com/google/langextract
- Extracted from upstream docs: https://raw.githubusercontent.com/google/langextract/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/langextract-llm-structured-text-extraction/)

---
> Source: [agentskillexchange/skills](https://github.com/agentskillexchange/skills) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-05-22 -->

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Installation → Source
terms    -> Note · Basic usage or getting-started notes: - [Quick Start](quick-start) - Extract structured information with just a few lines of code. · --- > Source: [agentskillexchange/skills](https://github.com/agentskillexchange/skills) — distributed by [TomeVault](https://tomevault.io).
files/cmd -> github.com/google/langextract.git · github.com/google/langextract · raw.githubusercontent.com/google/langextract/HEAD/README.md · agentskillexchange.com/skills/langextract-llm-structured-text-extraction · agentskillexchange/skills · github.com/agentskillexchange/skills
body sha256 -> 08f34147981e

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

LangExtract LLM-Powered Structured Text Extraction

Installation

1. Define Your Extraction Task

Source

Discussion