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/gemini-cli-extension --skill querygit clone --depth 1 https://github.com/pinecone-io/gemini-cli-extensionWrote 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/gemini-cli-extension/query)<a href="https://agentmods.dev/skills/pinecone-io/gemini-cli-extension/query"><img src="https://agentmods.dev/badge/skills/pinecone-io/gemini-cli-extension/query/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/gemini-cli-extension/query"><img src="https://agentmods.dev/badge/skills/pinecone-io/gemini-cli-extension/query.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 | $0.00068 | $0.01027 |
| Opus 5.5 | $0.00027 | $0.00411 |
| Sonnet 5.5 | $0.00014 | $0.00205 |
| Haiku 4.5 | $0.00007 | $0.00103 |
Grade A, and why
query 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:query — 21 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pinecone Query Skill
Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.
What is this skill for?
This skill provides a simple way to query integrated indexes (indexes with built-in Pinecone embedding models) using text queries. The MCP server automatically converts your text into embeddings and searches the index.
Prerequisites
Required:
- ✅ Pinecone MCP server must be configured - Check if MCP tools are available
- ✅ PINECONE_API_KEY environment variable must be set - Get a free API key at https://app.pinecone.io/?sessionType=signup
- ✅ Index must be an integrated index - Uses Pinecone embedding models (e.g., multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0)
When NOT to use this skill
Use the CLI skill instead if:
- ❌ Your index is a standard index (no integrated embedding model)
- ❌ You need to query with custom vector values (not text)
- ❌ You need advanced vector operations (fetch by ID, list vectors, bulk operations)
- ❌ Your index uses third-party embedding models (OpenAI, HuggingFace, Cohere)
MCP Limitation: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.
How it works
Utilize Pinecone MCP's search-records tool to search for records within a specified Pinecone integrated index using a text query.
Workflow
IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available. If MCP tools are not accessible:
- Inform the user that the Pinecone MCP server needs to be configured
- Check if
PINECONE_API_KEYenvironment variable is set - Direct them to the MCP setup documentation or the
helpskill
- Parse the user's input for:
query(required): The text to search for.index(required): The name of the Pinecone index to search.namespace(optional): The namespace within the index.reranker(optional): The reranking model to use for improved relevance.
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 · 84 lines · 68 tokens per session scan A 988b7b24c820
query is a skill published in the GitHub repository pinecone-io/gemini-cli-extension (23 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 1,027 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to pinecone:query, differing in 21 lines, and is treated as a copy.
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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.
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…