An interactive introduction to Pinecone, a service for storing and searching data by meaning rather than exact words. It offers a database path and a document-question-answering assistant path.

Use it to create a vector search index, add sample data, query it with Python, or build a Pinecone Assistant for answering questions about uploaded documents.

It guides new developers through the first setup steps and checks that the Pinecone connection works before continuing.

quickstart — Skill for Claude Code and Codex by pinecone-io

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/pinecone-io/gemini-cli-extension

agentmods

npx agentmods add skills/pinecone-io/gemini-cli-extension/quickstart

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pinecone-io/gemini-cli-extension/quickstart/github.svg)](https://agentmods.dev/skills/pinecone-io/gemini-cli-extension/quickstart)

Your own site

<a href="https://agentmods.dev/skills/pinecone-io/gemini-cli-extension/quickstart"><img src="https://agentmods.dev/badge/skills/pinecone-io/gemini-cli-extension/quickstart/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/pinecone-io/gemini-cli-extension/quickstart"><img src="https://agentmods.dev/badge/skills/pinecone-io/gemini-cli-extension/quickstart.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,125 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 88% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00076	$0.02125
Opus 5.5	$0.00030	$0.00850
Sonnet 5.5	$0.00015	$0.00425
Haiku 4.5	$0.00008	$0.00213

Measured 1mo ago against content hash d3e1da07d767, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

quickstart scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/quickstart_complete.py, scripts/upsert.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to pinecone:quickstart — 36 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/quickstart/SKILL.md · 231 lines

How it starts

The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pinecone Quickstart

Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart, you will learn how to do a simple form of semantic search over some example data.

Prerequisites

Before starting either path, verify the API key works by calling list-indexes via the Pinecone MCP. If it succeeds, proceed. If it fails, ask the user to set their key:

Terminal: export PINECONE_API_KEY="your-key"
Or create a .env file in the project root: PINECONE_API_KEY=your-key

Then retry list-indexes to confirm.

Step 0: Choose Your Path

Ask the user which path they want:

Database – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script.
Assistant – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required.

Path A: Database Quickstart

For each step, explain to the user what will happen. An overview is here:

Check if MCP is set
Create an integrated index with MCP
Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes)
Run a semantic search query and explore further queries
Optionally try reranking
Offer the complete standalone script

Step 1 – Verify MCP is Available

The prerequisite check already called list-indexes. If it succeeded, the MCP is working — proceed to Step 2.

If it failed because MCP tools were unavailable (not an auth error):

Tell the user the MCP server needs to be configured
Point them to: https://docs.pinecone.io/reference/tools/mcp

Step 2 – Create an Integrated Index

Use the MCP create-index-for-model tool to create a serverless index with integrated embeddings:

name: quickstart-skills
cloud: aws
region: us-east-1
embed:
  model: llama-text-embed-v2
  fieldMap:
    text: chunk_text

Explain to the user what's happening:

An integrated index uses a built-in Pinecone embedding model (llama-text-embed-v2)
This means you send plain text and Pinecone handles the embedding automatically
The field_map tells Pinecone which field in your records contains the text to embed

Read the full file on GitHub · 231 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 231 lines · 76 tokens per session scan A d3e1da07d767

Subscribe to this mod's changes

quickstart is a skill published in the GitHub repository pinecone-io/gemini-cli-extension (23 stars, last pushed 2mo ago), licensed MIT. It adds 76 tokens to every session and 2,125 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to pinecone:quickstart, differing in 36 lines, and is treated as a copy.

Data and AI Databases RAG and vectors agent-skills gemini-cli gemini-cli-extension pinecone retrieval-augmented-generation semantic-search

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