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.
git clone --depth 1 https://github.com/pinecone-io/pinecone-cursor-pluginnpx agentmods add skills/pinecone-io/pinecone-cursor-plugin/pinecone-quickstartWrote 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.
[](https://agentmods.dev/skills/pinecone-io/pinecone-cursor-plugin/pinecone-quickstart)<a href="https://agentmods.dev/skills/pinecone-io/pinecone-cursor-plugin/pinecone-quickstart"><img src="https://agentmods.dev/badge/skills/pinecone-io/pinecone-cursor-plugin/pinecone-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.
<a href="https://agentmods.dev/skills/pinecone-io/pinecone-cursor-plugin/pinecone-quickstart"><img src="https://agentmods.dev/badge/skills/pinecone-io/pinecone-cursor-plugin/pinecone-quickstart.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.02246 |
| Opus 5.5 | $0.00032 | $0.00898 |
| Sonnet 5.5 | $0.00016 | $0.00449 |
| Haiku 4.5 | $0.00008 | $0.00225 |
Grade A, and why
pinecone-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.
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.
This is a copy
91% identical to pinecone:quickstart — 37 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.
How it starts
The opening of the file, as written. The whole thing — 232 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.
Whenever this skill asks the user to choose between options, confirm a destructive step, or pick from a list, ask in plain prose, list the options, and wait for their answer before continuing.
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:
- Add
PINECONE_API_KEY=your-keyto a.envfile at your workspace root. The bundled MCP config reads it through Cursor'senvFilefield. - For scripts, either
export PINECONE_API_KEY="your-key"in your terminal or run them withuv run --env-file .env scripts/....
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
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.
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 · 232 lines · 79 tokens per session scan A a7c6f6579baf
pinecone-quickstart is a skill published in the GitHub repository pinecone-io/pinecone-cursor-plugin (2 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 2,246 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to pinecone:quickstart, differing in 37 lines, and is treated as a copy.
Other skills, from other repositories
pinecone:full-text-search
Create, ingest into, and query a Pinecone full-text-search (FTS) document index using the graduated document-schema API (Python SDK 10.0.0, API version 2026-07). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct scoreby clauses…
pinecone:quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided…
pinecone:cli
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation…
pinecone:mcp
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them…
pinecone:help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.