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Agent skill

Pinecone Research

by Luciole-Studio in Luciole-Studio/Misaka-Agent

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent.

MITAuto-check passedAI & LLM Engineering

Install Pinecone Research

skills CLI
$ npx skills add Luciole-Studio/Misaka-Agent --skill pinecone-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Luciole-Studio/Misaka-Agent pinecone-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Luciole-Studio/Misaka-Agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/misaka/core/skills/assets/optional/research/pinecone-research .claude/skills/pinecone-research && rm -rf skills-src

Use ~/.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/

Facts

Skill name
pinecone-research
GitHub stars
139
Used in
1 other repo
Token cost
~763 tokens
SKILL.md length
164 words
Files
3 (incl. scripts)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent.

  • Works in 5 steps: Namespace by session or user — isolate… → Batch upserts — 100–200 vectors per… → Metadata filtering — tag vectors with… → …
  • Tasks that involve Vector databases
  • SKILL.md covers When to use this skill, Quick start, Best practices and Resources
  • Runs Python scripts from its folder; calls pip; needs PINECONE_API_KEY

What it does

Pinecone Research is an agent skill from Luciole-Studio/Misaka-Agent. Agent RAG and long-term memory with Pinecone.

Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/memory_manager.py` and `scripts/rag_pipeline.py`).

It sits in AI & LLM Engineering, covering Vector databases, Agent memory and Retrieval-augmented generation. It works with Pinecone. The repository describes itself as: A multi-agent research system for the humanities and social sciences. The licence is MIT.

When your agent uses it

  • Tasks that involve Vector databases
  • Tasks that involve Agent memory
  • Tasks that involve Retrieval-augmented generation

Example prompts

  • “/pinecone-research”

Requirements

  • Python 3
  • A credential in PINECONE_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Namespace by session or user — isolate data for multi-tenant agents
  2. Batch upserts — 100–200 vectors per batch for efficiency
  3. Metadata filtering — tag vectors with session ID, timestamp, topic
  4. Prune old memory — delete stale namespaces to control costs
  5. Use serverless — auto-scaling, pay-per-use pricing

What it can do on your machine

Read from SKILL.md and the folder at commit b94464a. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.pinecone.io
    • python.langchain.com

    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 Research loads about 763 tokens when it runs. Until then it costs about 16 tokens; SKILL.md has 164 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~16
When it runs · the whole SKILL.md, loaded when a task matches
~763

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); the scripts in this folder are not scanned.

SKILL.md

The full file from Luciole-Studio/Misaka-Agent at commit b94464a, republished under its MIT licence (© Luciole-Studio). 164 words, ~763 tokens.

Download SKILL.mdSave it as .claude/skills/pinecone-research/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
pinecone-research
description
Agent RAG and long-term memory with Pinecone.
version
1.0.0
author
immuhammadfurqan
license
MIT
dependencies
pinecone-client, langchain-pinecone
platforms
linux, macos, windows

Pinecone Research — Agent RAG & Long-Term Memory

Use Pinecone as a retrieval-augmented generation (RAG) backend for agent conversations: persist embeddings, retrieve relevant context from past sessions, and build long-term memory.

When to use this skill

Use when:

  • Building agent RAG pipelines with Pinecone as the vector store
  • Need persistent long-term memory across agent sessions
  • Combining retrieval with agent tool use
  • Researching or prototyping semantic search workflows

Use the mlops/pinecone skill instead when:

  • Need a general Pinecone reference (index management, CRUD, hybrid search)
  • Working on production infrastructure without agent integration

Quick start

Setup
bash
pip install pinecone-client langchain-pinecone langchain-openai

Set your API key:

bash
export PINECONE_API_KEY="your-api-key"
Basic RAG pipeline
python
from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings

# Initialize Pinecone
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

# Create or connect to index
index_name = "agent-memory"
if index_name not in [i.name for i in pc.list_indexes()]:
    pc.create_index(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )

# Build vector store
vectorstore = PineconeVectorStore.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    index_name=index_name,
)

# Retrieve relevant context
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
results = retriever.invoke("What did the agent discuss yesterday?")
Namespace-based session memory
python
# Store per-session memory
vectorstore = PineconeVectorStore(
    index=pc.Index(index_name),
    embedding=OpenAIEmbeddings(),
    namespace=f"session-{session_id}",
)

# Query across all sessions (no namespace filter)
all_memory = PineconeVectorStore(
    index=pc.Index(index_name),
    embedding=OpenAIEmbeddings(),
)
results = all_memory.similarity_search("relevant query", k=10)

Best practices

  1. Namespace by session or user — isolate data for multi-tenant agents
  2. Batch upserts — 100–200 vectors per batch for efficiency
  3. Metadata filtering — tag vectors with session ID, timestamp, topic
  4. Prune old memory — delete stale namespaces to control costs
  5. Use serverless — auto-scaling, pay-per-use pricing

Resources

© Luciole-Studio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in misaka/core/skills/assets/optional/research/pinecone-research of Luciole-Studio/Misaka-Agent.

  • SKILL.md
  • scripts/memory_manager.py
  • scripts/rag_pipeline.py

Open the folder on GitHubat commit b94464a

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Luciole-Studio/Misaka-Agent, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Pinecone Research 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.

Pinecone Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pinecone Research this skillLuciole-Studio/Misaka-Agent1391 repos~763Automated safety check: PassMIT
RAG Implementationwshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Hunt RAG Vectorelementalsouls/Claude-BugHunter4.8k—~2.6kAutomated safety check: PassMIT
Pinecone RAGgithub/awesome-copilot40k—~2.4kAutomated safety check: PassApache-2.0
Neo4j Graphrag Skillneo4j-contrib/neo4j-skills114—~4.2kAutomated safety check: NotesMIT
Pinecone Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k6 repos~2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Pinecone Research

What does Pinecone Research do?

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent. Pinecone Research is an agent skill from Luciole-Studio/Misaka-Agent. Agent RAG and long-term memory with Pinecone.

When should I use Pinecone Research?

Pinecone Research fits situations like: tasks that involve Vector databases; tasks that involve Agent memory; tasks that involve Retrieval-augmented generation.

How do I install Pinecone Research in Claude Code?

Run `npx skills add Luciole-Studio/Misaka-Agent --skill pinecone-research -a claude-code`. Or copy the skill folder (misaka/core/skills/assets/optional/research/pinecone-research in Luciole-Studio/Misaka-Agent) into .claude/skills/pinecone-research in your project. Claude Code loads it when a task matches its description.

How do I install Pinecone Research in Codex?

Run `npx skills add Luciole-Studio/Misaka-Agent --skill pinecone-research -a codex`. Or copy the skill folder (misaka/core/skills/assets/optional/research/pinecone-research in Luciole-Studio/Misaka-Agent) into .agents/skills/pinecone-research in your project. Codex loads it when a task matches its description.

Can I use Pinecone Research 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 Luciole-Studio/Misaka-Agent --skill pinecone-research -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-research, .gemini/skills/pinecone-research, .github/skills/pinecone-research and .opencode/skills/pinecone-research in your project.

What does Pinecone Research need to run?

Going by SKILL.md and its folder, Pinecone Research needs Python for the scripts in its folder, 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 Research access the network?

SKILL.md names 2 domains. As links in the text: docs.pinecone.io and python.langchain.com. This is read from the text; nothing was executed.

Is Pinecone Research 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Pinecone Research use?

Pinecone Research 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 Research use?

About 763 tokens (SKILL.md is roughly 3.1k 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 Research?

Skills that share tags, products or a category with Pinecone Research: RAG Implementation (wshobson/agents, 40k stars), Hunt RAG Vector (elementalsouls/Claude-BugHunter, 4.8k stars), Pinecone RAG (github/awesome-copilot, 40k stars) and Neo4j Graphrag Skill (neo4j-contrib/neo4j-skills, 114 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pinecone Research?

Luciole-Studio (a GitHub organization) maintains it in Luciole-Studio/Misaka-Agent, which has 139 GitHub stars. The repository holds 76 skills in this directory. The repository was last updated on October 8, 2026.

Source: Luciole-Studio/Misaka-Agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.