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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
Overview
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 tour of Pinecone's tools.
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Pinecone Quickstart
Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart, you will learn how to do a simple form of semantic search over some example data.
Prerequisites
Before starting either path, verify the API key works by calling list-indexes via the Pinecone MCP. If it succeeds, proceed. If it fails, ask the user to set their key:
- Terminal:
export PINECONE_API_KEY="your-key" - Or create a
.envfile in the project root:PINECONE_API_KEY=your-key
Then retry list-indexes to confirm.
Step 0: Choose Your Path
Ask the user which path they want:
- Database – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script.
- Assistant – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required.
Path A: Database Quickstart
For each step, explain to the user what will happen. An overview is here:
- Check if MCP is set
- Create an integrated index with MCP
- Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes)
- Run a semantic search query and explore further queries
- Optionally try reranking
- Offer the complete standalone script
Step 1 – Verify MCP is Available
The prerequisite check already called list-indexes. If it succeeded, the MCP is working — proceed to Step 2.
If it failed because MCP tools were unavailable (not an auth error):
- Tell the user the MCP server needs to be configured
- Point them to: https://docs.pinecone.io/reference/tools/mcp
Step 2 – Create an Integrated Index
Use the MCP create-index-for-model tool to create a serverless index with integrated embeddings:
name: quickstart-skills
cloud: aws
region: us-east-1
embed:
model: llama-text-embed-v2
fieldMap:
text: chunk_text
Explain to the user what's happening:
- An integrated index uses a built-in Pinecone embedding model (
llama-text-embed-v2) - This means you send plain text and Pinecone handles the embedding automatically
- The
field_maptells Pinecone which field in your records contains the text to embed
Wait for the index to become ready before proceeding. Waiting a few seconds is sufficient.
Step 3 – Upsert Sample Data
Run the bundled upsert script to seed the index with sample records.
If PINECONE_API_KEY is set in the environment:
uv run scripts/upsert.py --index quickstart-skills
If using a .env file:
uv run --env-file .env scripts/upsert.py --index quickstart-skills
Explain to the user what's happening:
- The script uploads 9 sample records across three themes: productivity (getting work done), health (feeling unwell), and nature (outdoors/wildlife)
- The dataset is intentionally varied so semantic search can show its value — the queries below use completely different words than the records, but the right ones still surface
- Each record has an
_id, achunk_textfield (the text that gets embedded), and acategoryfield - This is the same structure you'd use for your own data — just replace the records
Step 4 – Query with the MCP
Use the MCP search-records tool to run the first semantic search:
index: quickstart-skills
namespace: example-namespace
query:
topK: 3
inputs:
text: "getting things done efficiently"
Display the results in a clean table: ID, score, and chunk_text.
Explain to the user what's happening:
- Notice the query shares no keywords with the records — but it surfaces the productivity sentences
- That's semantic search: it finds meaning, not just matching words
- You sent plain text — Pinecone embedded the query using the same model as the index
Offer to explore further: Ask the user if they'd like to try another query to see the effect more clearly:
- Option A:
"feeling under the weather"— should surface the health records - Option B:
"wildlife spotting outside"— should surface the nature records - Option C: No thanks, move on
Run whichever query they choose and display the results the same way. If they want to try both, do both. After each result, point out which theme surfaced and why.
If they decline or are done exploring, proceed to Step 5 or offer to skip ahead to the complete script.
Step 5 – Try Reranking (Optional)
Ask the user if they want to try reranking.
If yes, use search-records again with reranking enabled:
rerank:
model: bge-reranker-v2-m3
rankFields: [chunk_text]
topN: 3
Explain: Reranking runs a second-pass model over the results to improve relevance ordering.
Step 6 – Wrap Up
Congratulate the user on completing the quickstart. Ask if they'd like a standalone Python script that does everything in one go — create index, upsert, query, and rerank.
If yes, copy it to their working directory:
cp scripts/quickstart_complete.py ./pinecone_quickstart.py
Tell the user:
- The script is at
./pinecone_quickstart.py - Run it with:
uv run pinecone_quickstart.py - It uses
uvinline dependencies — no separate install needed - They can swap in their own
recordslist to build something real
Path B: Assistant Quickstart
Guide the user through the Pinecone Assistant workflow using the existing assistant skills:
Step 1 – Check for Documents
Before anything else, ask the user if they have files to upload. Pinecone Assistant accepts .pdf, .md, .txt, and .docx files — a single file or a folder of files both work.
If they have files: ask for the path and proceed to Step 2.
If they don't have files: offer two options:
- Generate sample docs — create a few short markdown files in
./sample-docs/so they can complete the quickstart right now. Ask what topics they'd like (or default to: a product FAQ, a short how-to guide, and a brief company overview). Write 3 files, each 150–250 words. - Come back later — let them know they can return once they have documents and pick up from Step 2.
Step 2 – Create an Assistant
Invoke assistant or run (add --env-file .env if using a .env file):
uv run ../assistant/scripts/create.py --name my-assistant
Explain: The assistant is a fully managed RAG service — upload documents, ask questions, get cited answers.
Step 3 – Upload Documents
Invoke assistant or run (add --env-file .env if using a .env file):
uv run ../assistant/scripts/upload.py --assistant my-assistant --source ./your-docs
Explain: Pinecone handles chunking, embedding, and indexing automatically — no configuration needed.
Step 4 – Chat with the Assistant
Invoke assistant or run (add --env-file .env if using a .env file):
uv run ../assistant/scripts/chat.py --assistant my-assistant --message "What are the main topics in these documents?"
Explain: Responses include citations with source file and page number.
Next Steps for Assistant
- Invoke
assistantto keep the assistant up to date as documents change - Use the assistant skill to retrieve raw context snippets for custom workflows
- Every assistant is also an MCP server — see https://docs.pinecone.io/guides/assistant/mcp-server
Troubleshooting
PINECONE_API_KEY not set
Terminal environments:
export PINECONE_API_KEY="your-key"
IDEs that don't inherit shell variables: create a .env file in the project root:
PINECONE_API_KEY=your-key
Then use uv run --env-file .env when running scripts. Restart your IDE/agent session after setting.
MCP tools not available
- Verify the Pinecone MCP server is configured in your IDE's MCP settings
- Check that
PINECONE_API_KEYis set before the MCP server starts
Index already exists
- The upsert script is safe to re-run — it will upsert over existing records
- Or delete and recreate: use
pc index delete -n quickstart-skillsvia the CLI
uv not installed
See the uv installation guide.
Further Reading
- Quickstart docs: https://docs.pinecone.io/guides/get-started/quickstart
- Integrated indexes: https://docs.pinecone.io/guides/index-data/create-an-index
- Python SDK: https://docs.pinecone.io/guides/get-started/python-sdk
- MCP server: https://docs.pinecone.io/reference/tools/mcp
File metadata
name: quickstart description: 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 tour of Pinecone's tools.
View original text
---
name: quickstart
description: 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 tour of Pinecone's tools.
---
# Pinecone Quickstart
Welcome! This skill walks you through your first Pinecone experience using the tools available to you. In this quickstart,
you will learn how to do a simple form of semantic search over some example data.
## Prerequisites
Before starting either path, verify the API key works by calling `list-indexes` via the Pinecone MCP. If it succeeds, proceed. If it fails, ask the user to set their key:
- Terminal: `export PINECONE_API_KEY="your-key"`
- Or create a `.env` file in the project root: `PINECONE_API_KEY=your-key`
Then retry `list-indexes` to confirm.
## Step 0: Choose Your Path
Ask the user which path they want:
- **Database** – Build a vector search index. Best for developers who want to store and search embeddings. Uses the Pinecone MCP + a Python upsert script.
- **Assistant** – Build a document Q&A assistant. Best for users who want to upload files and ask questions with cited answers. No code required.
---
## Path A: Database Quickstart
For each step, explain to the user what will happen. An overview is here:
1. Check if MCP is set
2. Create an integrated index with MCP
3. Upsert sample data using the bundled script (9 sentences across productivity, health, and nature themes)
4. Run a semantic search query and explore further queries
5. Optionally try reranking
6. Offer the complete standalone script
### Step 1 – Verify MCP is Available
The prerequisite check already called `list-indexes`. If it succeeded, the MCP is working — proceed to Step 2.
If it failed because MCP tools were unavailable (not an auth error):
- Tell the user the MCP server needs to be configured
- Point them to: https://docs.pinecone.io/reference/tools/mcp
### Step 2 – Create an Integrated Index
Use the MCP `create-index-for-model` tool to create a serverless index with integrated embeddings:
```
name: quickstart-skills
cloud: aws
region: us-east-1
embed:
model: llama-text-embed-v2
fieldMap:
text: chunk_text
```
**Explain to the user what's happening:**
- An *integrated index* uses a built-in Pinecone embedding model (`llama-text-embed-v2`)
- This means you send plain text and Pinecone handles the embedding automatically
- The `field_map` tells Pinecone which field in your records contains the text to embed
Wait for the index to become ready before proceeding. Waiting a few seconds is sufficient.
### Step 3 – Upsert Sample Data
Run the bundled upsert script to seed the index with sample records.
If `PINECONE_API_KEY` is set in the environment:
```bash
uv run scripts/upsert.py --index quickstart-skills
```
If using a `.env` file:
```bash
uv run --env-file .env scripts/upsert.py --index quickstart-skills
```
**Explain to the user what's happening:**
- The script uploads 9 sample records across three themes: **productivity** (getting work done), **health** (feeling unwell), and **nature** (outdoors/wildlife)
- The dataset is intentionally varied so semantic search can show its value — the queries below use completely different words than the records, but the right ones still surface
- Each record has an `_id`, a `chunk_text` field (the text that gets embedded), and a `category` field
- This is the same structure you'd use for your own data — just replace the records
### Step 4 – Query with the MCP
Use the MCP `search-records` tool to run the first semantic search:
```
index: quickstart-skills
namespace: example-namespace
query:
topK: 3
inputs:
text: "getting things done efficiently"
```
Display the results in a clean table: ID, score, and `chunk_text`.
**Explain to the user what's happening:**
- Notice the query shares no keywords with the records — but it surfaces the productivity sentences
- That's semantic search: it finds meaning, not just matching words
- You sent plain text — Pinecone embedded the query using the same model as the index
**Offer to explore further:** Ask the user if they'd like to try another query to see the effect more clearly:
- Option A: `"feeling under the weather"` — should surface the health records
- Option B: `"wildlife spotting outside"` — should surface the nature records
- Option C: No thanks, move on
Run whichever query they choose and display the results the same way. If they want to try both, do both. After each result, point out which theme surfaced and why.
If they decline or are done exploring, proceed to Step 5 or offer to skip ahead to the complete script.
### Step 5 – Try Reranking (Optional)
Ask the user if they want to try reranking.
If yes, use `search-records` again with reranking enabled:
```
rerank:
model: bge-reranker-v2-m3
rankFields: [chunk_text]
topN: 3
```
**Explain**: Reranking runs a second-pass model over the results to improve relevance ordering.
### Step 6 – Wrap Up
Congratulate the user on completing the quickstart. Ask if they'd like a standalone Python script that does everything in one go — create index, upsert, query, and rerank.
If yes, copy it to their working directory:
```bash
cp scripts/quickstart_complete.py ./pinecone_quickstart.py
```
Tell the user:
- The script is at `./pinecone_quickstart.py`
- Run it with: `uv run pinecone_quickstart.py`
- It uses `uv` inline dependencies — no separate install needed
- They can swap in their own `records` list to build something real
---
## Path B: Assistant Quickstart
Guide the user through the Pinecone Assistant workflow using the existing assistant skills:
### Step 1 – Check for Documents
Before anything else, ask the user if they have files to upload. Pinecone Assistant accepts `.pdf`, `.md`, `.txt`, and `.docx` files — a single file or a folder of files both work.
**If they have files:** ask for the path and proceed to Step 2.
**If they don't have files:** offer two options:
- **Generate sample docs** — create a few short markdown files in `./sample-docs/` so they can complete the quickstart right now. Ask what topics they'd like (or default to: a product FAQ, a short how-to guide, and a brief company overview). Write 3 files, each 150–250 words.
- **Come back later** — let them know they can return once they have documents and pick up from Step 2.
### Step 2 – Create an Assistant
Invoke `assistant` or run (add `--env-file .env` if using a `.env` file):
```bash
uv run ../assistant/scripts/create.py --name my-assistant
```
Explain: The assistant is a fully managed RAG service — upload documents, ask questions, get cited answers.
### Step 3 – Upload Documents
Invoke `assistant` or run (add `--env-file .env` if using a `.env` file):
```bash
uv run ../assistant/scripts/upload.py --assistant my-assistant --source ./your-docs
```
Explain: Pinecone handles chunking, embedding, and indexing automatically — no configuration needed.
### Step 4 – Chat with the Assistant
Invoke `assistant` or run (add `--env-file .env` if using a `.env` file):
```bash
uv run ../assistant/scripts/chat.py --assistant my-assistant --message "What are the main topics in these documents?"
```
Explain: Responses include citations with source file and page number.
### Next Steps for Assistant
- Invoke `assistant` to keep the assistant up to date as documents change
- Use the assistant skill to retrieve raw context snippets for custom workflows
- Every assistant is also an MCP server — see https://docs.pinecone.io/guides/assistant/mcp-server
---
## Troubleshooting
**`PINECONE_API_KEY` not set**
Terminal environments:
```bash
export PINECONE_API_KEY="your-key"
```
IDEs that don't inherit shell variables: create a `.env` file in the project root:
```
PINECONE_API_KEY=your-key
```
Then use `uv run --env-file .env` when running scripts. Restart your IDE/agent session after setting.
**MCP tools not available**
- Verify the Pinecone MCP server is configured in your IDE's MCP settings
- Check that `PINECONE_API_KEY` is set before the MCP server starts
**Index already exists**
- The upsert script is safe to re-run — it will upsert over existing records
- Or delete and recreate: use `pc index delete -n quickstart-skills` via the CLI
**`uv` not installed**
See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/).
## Further Reading
- Quickstart docs: https://docs.pinecone.io/guides/get-started/quickstart
- Integrated indexes: https://docs.pinecone.io/guides/index-data/create-an-index
- Python SDK: https://docs.pinecone.io/guides/get-started/python-sdk
- MCP server: https://docs.pinecone.io/reference/tools/mcp
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- MIT
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Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- Financial research output is not financial advice; require human review before any live investment decision.
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 2 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedAI Reviewed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- pinecone-io/gemini-cli-extension
- License
- MIT
- Version
- Unknown
- Last GitHub push
- Aug 14, 2026
- Registry updated
- Sep 13, 2026
- Instruction path
- skills/quickstart/SKILL.md @ de6792954ae2
Version reported in registry metadata; check source releases before relying on it.
Quality
57/100
Promising
Trust
57/100
Do not auto-install
Audit
70/100
Risky
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- Financial research output is not financial advice; require human review before any live investment decision.
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 2 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
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More details
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"repoActivity": "23 stars, 2 forks",
"lastPushed": "2mo since push",
"license": "MIT",
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"install": "npx skills add pinecone-io/gemini-cli-extension --skill quickstart",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Strong README/SKILL.md context",
"agentOutcomes": "No agent outcome data yet"
},
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"last_outcome_at": null,
"label": "No agent outcome data yet"
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},
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"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Low GitHub adoption signal",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 23 GitHub stars",
"Stars/forks activity: 23 stars, 2 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access"
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"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
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"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
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"recentFailureRate": null,
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"setupRequired": 0,
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"productionOutcomes": 0,
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"uniqueAgents": 0,
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"signals": [],
"penalties": [
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]
},
"audit": {
"score": 70,
"risk_level": "risky",
"risk_label": "Risky",
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"Low GitHub adoption signal",
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
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"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
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"quality": {
"score": 57,
"label": "Promising"
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"supply": {
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"maintenance": "2mo since push",
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{
"slug": "pathwaycom-llm-app",
"name": "Llm App",
"url": "https://www.openagentskill.com/skills/pathwaycom-llm-app",
"stars": 59299,
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"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision"
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"agent_contract": {
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"install_policy": "block",
"minimum_review_before_use": [
"Trust: 65/100 Manual review",
"Audit: 70/100 Risky",
"Safety: 22/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
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"install_command": "npx skills add pinecone-io/gemini-cli-extension --skill quickstart",
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"expected_outcomes": [
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"skill_slug": "pinecone-io-quickstart",
"task": "Use quickstart in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
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"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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"endpoints": {
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"api": "https://www.openagentskill.com/api/agent/skills/pinecone-io-quickstart",
"audit": "https://www.openagentskill.com/skills/pinecone-io-quickstart/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=pinecone-io-quickstart&task=Use%20quickstart%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/pinecone-io-quickstart/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/pinecone-io-quickstart"
}
}For the creator
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