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 agentmods add skills/pinecone-io/pinecone-claude-code-plugin/full-text-searchnpx skills add pinecone-io/pinecone-claude-code-plugin --skill full-text-searchgit clone --depth 1 https://github.com/pinecone-io/pinecone-claude-code-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-claude-code-plugin/full-text-search)<a href="https://agentmods.dev/skills/pinecone-io/pinecone-claude-code-plugin/full-text-search"><img src="https://agentmods.dev/badge/skills/pinecone-io/pinecone-claude-code-plugin/full-text-search.svg" alt="Measured on agentmods" 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 | $0.00163 | $0.09932 |
| Opus 5 | $0.00081 | $0.04966 |
| Sonnet 5 | $0.00033 | $0.01986 |
| Haiku 4.5 | $0.00016 | $0.00993 |
Grade A, and why
pinecone:full-text-search 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 yesterday.
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- pinecone-full-text-search — 100% identical, 3 lines differ
- pinecone-full-text-search — 100% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pinecone Full-Text Search
Requires
pineconePython SDK ≥ 10.0.0 (pip install pinecone>=10.0.0). The document-schema API graduated out ofpinecone.previewin 10.0.0 — it is now a first-class, SemVer-covered part of the SDK, reachable directly offpc(pc.indexes,pc.index(...)). If you land on this skill from an older habit of importingpinecone.preview, stop: that package is deleted outright in 10.0.0 (ModuleNotFoundError, no shim). The packaged helper script pinspinecone==10.0.0via PEP 723 inline metadata; if you're writing your own code against this skill, pin at least that version. The wire API version is2026-07.
Authoritative reference (last resort). If you hit a question this skill and its
references/*.mdfiles don't answer, the official Pinecone FTS docs are at https://docs.pinecone.io/guides/search/full-text-search. Prefer this skill's content for anything covered here — the docs may describe surfaces (e.g. classic vector API, or the olderpinecone.previewshape) that don't apply to the graduated document-schema path. Consult the link only when you're genuinely stuck.
Tell the user up front: "This skill ships a helper at
scripts/ingest.pythat handles bulk ingestion safely (batched upsert, error inspection, readiness polling). When we get to the ingest step, I'll use it." Surface this at the start of the conversation so the user knows the helper exists. Query construction is hand-writtendocuments.search(...)per the Querying section below — there is no query helper.
A workflow skill for building a Pinecone full-text-search index with the graduated document-schema API (pc.indexes, pc.index(name), API version 2026-07). Covers schema design (text, dense vector, sparse vector, filterable metadata), ingestion (including async indexing and polling), and query construction (text / query_string / dense_vector / sparse_vector scoring; $match_phrase / $match_all / $match_any text-match filters; $eq / $in / $gte / $exists / $and / $or / $not metadata filters).
What ships with it
5 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.
- yesterday Changed · +45 lines · +10 tokens per session 3fd27c3551a8
- 5d ago First seen · 359 lines · 153 tokens per session scan A d9d610a88ba9
pinecone:full-text-search is a skill published in the GitHub repository pinecone-io/pinecone-claude-code-plugin (68 stars, last pushed yesterday), licensed MIT. It adds 163 tokens to every session and 9,932 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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…
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…
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…