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/cookiemonsterdev/agents-skills/pinecone-docsnpx skills add cookieMonsterDev/agents-skills --skill pinecone-docsgit clone --depth 1 https://github.com/cookieMonsterDev/agents-skillsWrote 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/cookiemonsterdev/agents-skills/pinecone-docs)<a href="https://agentmods.dev/skills/cookiemonsterdev/agents-skills/pinecone-docs"><img src="https://agentmods.dev/badge/skills/cookiemonsterdev/agents-skills/pinecone-docs.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 | $0.00080 | $0.01204 |
| Opus 5 | $0.00040 | $0.00602 |
| Sonnet 5 | $0.00016 | $0.00241 |
| Haiku 4.5 | $0.00008 | $0.00120 |
Grade A, and why
pinecone-docs scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Only provide SDK code or `curl` examples when the user asks for them or when the docs page makes an example necessary. How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pinecone questions are easy to answer from stale memory, especially since pod-based indexes were replaced by the serverless architecture. Use this skill to ground answers in the official Pinecone documentation and return the closest authoritative page instead of generic vector-search advice.
When to Use
Use this skill when the request is about:
- Pinecone concepts: organizations, projects, serverless indexes, namespaces, and records
- Creating and configuring serverless indexes (dense, sparse/BM25, or integrated-embedding indexes)
- Upserting, querying, updating, deleting, or fetching records
- Namespaces for multitenancy and data isolation
- Metadata filtering, hybrid search, and reranking search results
- Bulk import from object storage, backups, and restoring indexes
- Pinecone Assistant for retrieval-augmented generation
- SDKs (Python, Node.js, Java, Go) and the REST API reference
- Authentication, API keys, limits, quotas, and billing plans
- Performance: reducing latency, increasing throughput, and managing cost
Do not use this skill for:
- Qdrant-specific concepts (payload indexes, HNSW parameter tuning, on-premise clustering). Use
qdrant-docsinstead. - Weaviate-specific concepts (GraphQL API, modules, classes as collections). Use
weaviate-docsinstead. - MongoDB Atlas Vector Search or Redis vector sets. Use
mongodb-docsorredis-docsinstead. - General embedding model selection or RAG architecture unless the question is specifically about Pinecone configuration or behavior.
Workflow
1. Classify the request
Decide which bucket the question belongs to before searching:
- Getting started and core concepts
- Creating and managing indexes
- Upserting, querying, updating, and deleting records
- Namespaces, metadata filtering, and multitenancy
- Search quality: hybrid search and reranking
- Backups, restores, and bulk import
- Pinecone Assistant
- SDKs and API reference
- Limits, cost, and performance optimization
If you need a quick starting point, load references/topic-map.md and jump to the closest section.
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.
- 3d ago First seen · 103 lines · 80 tokens per session scan A 838c1102e1f3
pinecone-docs is a skill published in the GitHub repository cookieMonsterDev/agents-skills (4 stars, last pushed 14d ago), licensed MIT. It adds 80 tokens to every session and 1,204 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
scylladb-vector-search
Guides ScyllaDB Cloud users through implementing and optimizing Vector Search for semantic similarity, RAG, and similar use cases. Use this skill when users need to store and query embeddings, build vector indexes (HNSW), run approximate nearest neighbour (ANN) queries, apply filtering (global/local secondary…
memgraph-graph-rag
Language-agnostic blueprint for building GraphRAG systems with Memgraph and agent tooling. Covers end-to-end architecture, schema design, ingestion, hybrid retrieval, tool contracts, and evaluation. Use when designing GraphRAG platforms that must work across multiple programming languages.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
alloydb-basics
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
cloud-databases-onboarding
Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud.…
cloud-monitoring-metric-selection
Retrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.