Skills cohere-v2-python

📦

cohere-v2-python

Name: cohere-v2-python
Author: ActiveInferenceInstitute

Content revision r1 Medium Risk 🔑 Env variables⚙️ External commands🌐 Network access

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.

Supports: Claude Codex Code(CC)

📊 67 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "cohere-v2-python" from https://skillstore.io/skills/activeinferenceinstitute-cohere-v2-python.md and its manifest at https://skillstore.io/api/skills/activeinferenceinstitute-cohere-v2-python/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/activeinferenceinstitute-cohere-v2-python.md Signed manifest GET /api/skills/activeinferenceinstitute-cohere-v2-python/manifest Signed lockfile GET /api/skills/activeinferenceinstitute-cohere-v2-python/lockfile

Test it

Using "cohere-v2-python". Extract contact details from a business card.

Expected outcome:

A schema plan with required contact fields, optional company details, and validation notes for email and phone values.

Using "cohere-v2-python". Classify customer feedback by sentiment and product aspect.

Expected outcome:

A constrained classification design with allowed sentiment values and clear aspect-level extraction guidance.

Using "cohere-v2-python". Review an entity extraction workflow for production readiness.

Expected outcome:

A checklist covering schema design, retries, privacy handling, token monitoring, and downstream validation.

Security Audit

Medium Risk

v9 • 7/7/2026 Open versioned report

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.

Files scanned

1,564

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Medium

SKILL.md:499-504

Insecure Database Credential Example

SKILL.md shows a SurrealDB storage example using root/root credentials while persisting extracted entities. Users may copy this pattern into real workflows and expose writable data stores.

The example directly hardcodes root/root credentials and opens a database connection. It is not malicious, but it encourages an unsafe default credential pattern.

Risk Factors

🔑 Env variables (6)

references/chat_api.md:19 references/chat_api.md:21 references/chat_api.md:45 references/structured_outputs.md:242 references/structured_outputs.md:589 SKILL.md:32

⚙️ External commands (36)

🌐 Network access (5)

SKILL.md:566 SKILL.md:567 SKILL.md:568 SKILL.md:569 SKILL.md:570

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

ActiveInferenceInstitute. (2026). cohere-v2-python security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/activeinferenceinstitute-cohere-v2-python/audits/9

BibTeX citation

@techreport{activeinferenceinstitute-activeinferenceinstitute-cohere-v2-python-2026,
  author = {ActiveInferenceInstitute},
  title = {cohere-v2-python security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/activeinferenceinstitute-cohere-v2-python/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "cohere-v2-python security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "ActiveInferenceInstitute"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/activeinferenceinstitute-cohere-v2-python/audits/9"
identifiers:
  - type: other
    value: "skillstore:activeinferenceinstitute-cohere-v2-python:audit:9"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

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.

Try These Prompts

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.

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.

Avoid

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.