langextract-llm-powered-structured-text-extraction
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- Author repo skills-registry
- Domain
- Data
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Required · Vendor-specific
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
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。
---
name: langextract-llm-powered-structured-text-extraction
description: LangExtract by Google is a Python library for extracting structured information from unstructure…
category: data
runtime: Python
---
# langextract-llm-powered-structured-text-extraction output preview
## PART A: Task fit
- Use case: 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. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Installation / Source” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Installation / Source” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Installation / Source”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: langextract-llm-powered-structured-text-extraction
description: LangExtract by Google is a Python library for extracting structured information from unstructure…
category: data
source: tomevault-io/skills-registry
---
# langextract-llm-powered-structured-text-extraction
## When to use
- LangExtract by Google is a Python library for extracting structured information from unstructured text using LLMs with…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Installation / Source” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "langextract-llm-powered-structured-text-extraction" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Installation / Source
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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 section for instructions on how to get and configure your key.
- python
Basic usage or getting-started notes:
Extract structured information with just a few lines of code.
1. Define Your Extraction Task
Extracted from upstream docs: https://raw.githubusercontent.com/google/langextract/HEAD/README.md
Source
Source: agentskillexchange/skills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review