Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pinecone-io/pinecone-cursor-plugin --skill pinecone-helpgit clone --depth 1 https://github.com/pinecone-io/pinecone-cursor-pluginWrote 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-help)<a href="https://agentmods.dev/skills/pinecone-io/pinecone-cursor-plugin/pinecone-help"><img src="https://agentmods.dev/badge/skills/pinecone-io/pinecone-cursor-plugin/pinecone-help/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-help"><img src="https://agentmods.dev/badge/skills/pinecone-io/pinecone-cursor-plugin/pinecone-help.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 | $0.00052 | $0.00929 |
| Opus 5.5 | $0.00021 | $0.00372 |
| Sonnet 5.5 | $0.00010 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
pinecone-help 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
86% identical to pinecone-help — 7 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pinecone Skills — Help & Overview
Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. It's useful for building semantic search, retrieval augmented generation, recommendation systems, and agentic applications.
Here's everything you need to get started and a summary of all available skills.
Invoke any skill from Cursor Agent chat with /pinecone-<skill-name> — for example /pinecone-quickstart or /pinecone-n8n.
What You Need
Required
- Pinecone account — free to create at https://app.pinecone.io/?sessionType=signup
- API key — create one in the Pinecone console after signing up, then make it
available to this environment:
- Add
PINECONE_API_KEY=your-keyto a.envfile at your workspace root. The bundled MCP config reads it through Cursor'senvFilefield.
- Add
- For scripts, either
export PINECONE_API_KEY="your-key"in your terminal or run them withuv run --env-file .env scripts/....
Optional (unlock more capabilities)
| Tool | What it enables | Install |
|---|---|---|
| Pinecone MCP server | Use Pinecone directly inside your AI agent/IDE without writing code | Setup guide |
Pinecone CLI (pc) |
Manage all index types from the terminal, batch operations, backups, CI/CD | brew tap pinecone-io/tap && brew install pinecone-io/tap/pinecone |
| uv | Run the packaged Python scripts included in these skills | Install uv |
Available Skills
| Skill | What it does |
|---|---|
pinecone-quickstart |
Step-by-step onboarding — create an index, upload data, and run your first search |
pinecone-query |
Search integrated indexes using natural language text via the Pinecone MCP |
pinecone-cli |
Use the Pinecone CLI (pc) for terminal-based index and vector management |
pinecone-assistant |
Create, manage, and chat with Pinecone Assistants for document Q&A with citations |
pinecone-mcp |
Reference for all Pinecone MCP server tools and their parameters |
pinecone-full-text-search |
Build a full-text-search index — schema design, safe bulk ingestion, and query construction (text / query_string / dense / sparse scoring with text-match and metadata filters). Document-schema API (2026-07); requires pinecone Python SDK ≥ 10.0.0. |
pinecone-docs |
Curated links to official Pinecone documentation, organized by topic |
pinecone-n8n |
Build n8n workflows with the Pinecone Assistant node or Pinecone Vector Store node, including best practices and full workflow JSON generation |
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 Changed 1c8c3da38f7a
- 1mo ago First seen · 71 lines · 52 tokens per session scan A 2476d8de896b
pinecone-help is a skill published in the GitHub repository pinecone-io/pinecone-cursor-plugin (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 929 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to pinecone-help, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
rag
Use when building grounded Q&A over your own corpus — chunk, retrieve hybrid, rerank, ground, cite chunk ids, refuse when the sources fall short — or when the right document is retrieved but the answer is still wrong, invented, or unmeasured. NOT operating the store itself — collection schema, HNSW efsearch…
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
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:n8n
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
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…