Pinecone Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.
Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
Install Pinecone
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install AlexAI-MCP/hermes-CCC pinecone --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pinecone .claude/skills/pinecone && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .claude/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pineconeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install AlexAI-MCP/hermes-CCC pinecone --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pinecone .agents/skills/pinecone && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .agents/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install AlexAI-MCP/hermes-CCC pinecone --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pinecone .cursor/skills/pinecone && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .cursor/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/AlexAI-MCP/hermes-CCC.git --path skills/pinecone--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install AlexAI-MCP/hermes-CCC pinecone --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pinecone .gemini/skills/pinecone && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .gemini/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install AlexAI-MCP/hermes-CCC pineconeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pinecone .github/skills/pinecone && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .github/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install AlexAI-MCP/hermes-CCC pinecone --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pinecone .opencode/skills/pinecone && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pinecone" agent skill from https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/pinecone into .opencode/skills/pinecone/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pinecone", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pinecone- GitHub stars
- 135
- Token cost
- ~954 tokens
- SKILL.md length
- 65 words
- Files
- 1
- Skills in repo
- 44
- Repo updated
- First seen
- Licence
- MIT
At a glance
Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
- Tasks that involve Vector databases
- SKILL.md covers Setup, Create Index, Upsert Vectors and Query, plus 5 more sections
- Calls pip; needs PINECONE_API_KEY
- Tasks that involve Retrieval-augmented generation
What it does
Pinecone is an agent skill from AlexAI-MCP/hermes-CCC. Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Databases, covering Vector databases, Retrieval-augmented generation and Serverless. It works with Pinecone. The repository describes itself as: Hermes Agent ported to Claude Code Channel — 46 native skills, no OAuth, no external process. The licence is MIT.
When your agent uses it
- Tasks that involve Vector databases
- Tasks that involve Retrieval-augmented generation
- Tasks that involve Serverless
Example prompts
- “/pinecone”
Requirements
- Python 3
- A credential in PINECONE_API_KEY
What it can do on your machine
Read from SKILL.md and the folder at commit 8107e89. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
PINECONE_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Pinecone loads about 954 tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 65 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from AlexAI-MCP/hermes-CCC at commit 8107e89, republished under its MIT licence (© AlexAI-MCP). 65 words, ~954 tokens.
Download SKILL.mdSave it as
.claude/skills/pinecone/SKILL.md (or your agent's skills folder).- name
- pinecone
- description
- Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
- version
- 1.0.0
- author
- hermes-CCC (ported from Hermes Agent by NousResearch)
- license
- MIT
Pinecone — Managed Vector Database
Fully managed vector database for production RAG. Serverless (pay-per-query) or pod-based (dedicated).
Setup
pip install pinecone-client sentence-transformers openaifrom pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key="your-api-key") # or os.environ["PINECONE_API_KEY"]Create Index
# Serverless (pay-per-query — cheapest to start)
pc.create_index(
name="my-index",
dimension=1536, # match your embedding model
metric="cosine", # cosine | euclidean | dotproduct
spec=ServerlessSpec(
cloud="aws",
region="us-east-1"
)
)
# Connect to index
index = pc.Index("my-index")Upsert Vectors
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2") # dim=384
documents = [
{"id": "doc1", "text": "Python async programming guide"},
{"id": "doc2", "text": "Machine learning with PyTorch"},
]
vectors = []
for doc in documents:
embedding = model.encode(doc["text"]).tolist()
vectors.append({
"id": doc["id"],
"values": embedding,
"metadata": {"text": doc["text"], "source": "manual"}
})
# Batch upsert (max 100 per call)
index.upsert(vectors=vectors, namespace="docs")Query
query_text = "how to write async Python?"
query_vector = model.encode(query_text).tolist()
results = index.query(
vector=query_vector,
top_k=5,
namespace="docs",
include_metadata=True,
)
for match in results["matches"]:
print(f"Score: {match['score']:.3f} | {match['metadata']['text']}")Metadata Filtering
results = index.query(
vector=query_vector,
top_k=5,
filter={"source": {"$eq": "manual"}},
include_metadata=True,
)
# Operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $and, $or
results = index.query(
vector=query_vector,
top_k=5,
filter={
"$and": [
{"category": {"$in": ["tech", "science"]}},
{"year": {"$gte": 2023}},
]
},
include_metadata=True,
)Namespaces
# Different namespaces = separate vector spaces (free, no extra cost)
index.upsert(vectors=vectors, namespace="user-123")
index.upsert(vectors=vectors, namespace="user-456")
# Query specific namespace
results = index.query(vector=query_vector, top_k=5, namespace="user-123")
# Delete namespace
index.delete(delete_all=True, namespace="user-123")Fetch / Delete / Update
# Fetch specific vectors
fetched = index.fetch(ids=["doc1", "doc2"], namespace="docs")
# Delete vectors
index.delete(ids=["doc1"], namespace="docs")
# Update metadata (re-upsert with same id)
index.upsert(vectors=[{"id": "doc1", "values": embedding, "metadata": {"updated": True}}])Index Stats
stats = index.describe_index_stats()
print(f"Total vectors: {stats['total_vector_count']}")
print(f"Namespaces: {stats['namespaces']}")When to Use Pinecone vs Alternatives
| Pinecone | Qdrant | Chroma | |
|---|---|---|---|
| Hosting | Managed cloud | Self/cloud | Self/cloud |
| Cost | Pay-per-use | Self-hosted free | Free |
| Scale | Billions | Millions+ | Millions |
| Setup | Minutes | Minutes | Seconds |
| Best for | Production SaaS | Production self-hosted | Local dev/RAG |
© AlexAI-MCP, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/pinecone of AlexAI-MCP/hermes-CCC.
Open the folder on GitHubat commit 8107e89
Compare with similar skills
Pinecone next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pinecone this skillAlexAI-MCP/hermes-CCC | 135 | — | ~954 | Automated safety check: Pass | MIT | |
| Pinecone Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~2k | Automated safety check: Pass | MIT | |
| Using Vector Databasesancoleman/ai-design-components | 526 | — | ~3.5k | Automated safety check: Pass | MIT | |
| PineconeLuciole-Studio/Misaka-Agent | 158 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| RAG Implementationwshobson/agents | 40k | 9 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Hunt RAG Vectorelementalsouls/Claude-BugHunter | 4.8k | — | ~2.6k | Automated safety check: Pass | MIT |
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Using Vector Databases
ancoleman/ai-design-components
Vector database implementation for AI/ML applications, semantic search, and RAG systems.
Pinecone
Luciole-Studio/Misaka-Agent
Managed vector DB for production RAG and search. An agent skill from Luciole-Studio/Misaka-Agent.
RAG Implementation
wshobson/agents
Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.
Hunt RAG Vector
elementalsouls/Claude-BugHunter
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from…
- Official
Pinecone RAG
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Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.
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Related
Works with
Categories
Questions about Pinecone
What does Pinecone do?
Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering. Pinecone is an agent skill from AlexAI-MCP/hermes-CCC. Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
When should I use Pinecone?
Pinecone fits situations like: tasks that involve Vector databases; tasks that involve Retrieval-augmented generation; tasks that involve Serverless.
How do I install Pinecone in Claude Code?
Run `npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a claude-code`. Or copy the skill folder (skills/pinecone in AlexAI-MCP/hermes-CCC) into .claude/skills/pinecone in your project. Claude Code loads it when a task matches its description.
How do I install Pinecone in Codex?
Run `npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a codex`. Or copy the skill folder (skills/pinecone in AlexAI-MCP/hermes-CCC) into .agents/skills/pinecone in your project. Codex loads it when a task matches its description.
Can I use Pinecone in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AlexAI-MCP/hermes-CCC --skill pinecone -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pinecone, .gemini/skills/pinecone, .github/skills/pinecone and .opencode/skills/pinecone in your project.
What does Pinecone need to run?
Going by SKILL.md and its folder, Pinecone needs the command-line tools its instructions call (pip) and credentials named PINECONE_API_KEY. Our summary lists: Python 3; A credential in PINECONE_API_KEY.
Does Pinecone access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Pinecone safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Pinecone use?
Pinecone is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pinecone use?
About 954 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Pinecone?
Skills that share tags, products or a category with Pinecone: Pinecone Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Using Vector Databases (ancoleman/ai-design-components, 526 stars), Pinecone (Luciole-Studio/Misaka-Agent, 158 stars) and RAG Implementation (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pinecone?
AlexAI-MCP (a GitHub user) maintains it in AlexAI-MCP/hermes-CCC, which has 135 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on April 8, 2026.
Source: AlexAI-MCP/hermes-CCC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.