# Build Cohere v2 Entity Extraction

Teams need reliable structured data from unstructured text. This skill shows how to use Cohere v2 Python and JSON Schema mode for validated extraction.

## Install

```bash
npx skillstore add activeinferenceinstitute/cohere-v2-python
```

## Metadata

- Status: approved
- Slug: activeinferenceinstitute-cohere-v2-python
- Skillstore revision: r1
- Version status: missing
- Tree hash: 41f830595ad7aade4548367a1406ddaf47e8986d43e50839c455f4d3c1352889
- Author: ActiveInferenceInstitute
- GitHub username: ActiveInferenceInstitute
- License: MIT
- Repository: https://github.com/ActiveInferenceInstitute/Journal-Utilities/tree/main/.claude/skills/cohere-v2-python
- Ref: d46e62089a6dfc9e14ea02eebd9b8e2ad2dfe1f4
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: env\_access, external\_commands, network
- Quality score: 67
- Public page: https://skillstore.pages.dev/skills/activeinferenceinstitute-cohere-v2-python
- Manifest: https://skillstore.pages.dev/api/skills/activeinferenceinstitute-cohere-v2-python/manifest

## Capabilities

- Initialize Cohere ClientV2 and send basic chat requests.
- Define JSON Schema response formats for validated model outputs.
- Extract single entities, entity arrays, resumes, invoices, and structured records.
- Use enums, nested objects, references, formats, and required fields in schemas.
- Design batch extraction workflows with parsing and storage patterns.
- Troubleshoot schema limits, latency, token usage, and API error handling.

## Use Cases

- Create Document Extraction Pipelines: Extract validated fields from documents for databases, search indexes, and internal systems.
- Add Structured AI Features: Build product workflows that classify text and return predictable fields for application logic.
- Analyze Research Corpora: Identify people, organizations, locations, dates, and events across large text collections.

## Prompt Templates

### Basic Chat Request

```
Use this skill to show a minimal Cohere v2 Python chat request. Include setup, model selection, and response handling.
```

### Single Entity Schema

```
Use this skill to design a JSON Schema mode request that extracts one organization, person, date, and location from text.
```

### Batch Document Extraction

```
Use this skill to plan a Python pipeline that extracts entities from many documents, validates results, and logs failures.
```

### Production Extraction Review

```
Use this skill to review my Cohere v2 extraction design for schema quality, error handling, privacy, latency, and cost controls.
```

## Limitations

- Requires a Cohere API key and network access to Cohere services.
- Examples are documentation patterns, not a packaged runtime or SDK wrapper.
- JSON Schema mode does not support every JSON Schema constraint.
- Users must handle privacy, consent, and retention for sensitive source text.

## Best Practices

- Use environment variables or secret managers for Cohere API keys.
- Keep schemas small at first, then add optional fields after validation succeeds.
- Redact sensitive text before sending documents to external model APIs.

## Anti Patterns

- Hardcoding API keys, database passwords, or production endpoints in examples or applications.
- Relying on free-form JSON mode when a strict schema is required.
- Sending medical, financial, or personal data without approval and retention controls.

## Security Audit

- Audited at: 2026-07-07T17:54:33.603\+00:00
- Summary: Most static findings are false positives caused by Markdown code fences, placeholder API-key examples, and reference URLs. No prompt-injection text or malicious exfiltration behavior was found, but one insecure database credential example should be corrected.

## Stats

- Views: 421
- Downloads: 12
- Favorites: 0
- Popularity score: 0