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pinecone-clipinecone CLI 搜索

Agent Skill

pinecone-cli 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

931

周安装

40

GitHub Stars

9

下载量

326
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pinecone-cli(pinecone CLI 搜索)
来源仓库:https://github.com/pinecone-io/skills
仓库路径:skills/pinecone-cli
安装命令:
npx skills add https://github.com/pinecone-io/skills --skill pinecone-cli
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/pinecone-io/skills --skill pinecone-cli

简介

用于查找、检索和筛选相关信息。

  • 适合根据关键词、任务场景或来源线索快速定位候选结果。
  • 可结合来源仓库 README 核验具体用法,注意权限与维护状态。
  • 安装命令:npx skills add https://github.com/pinecone-io/skills --skill pinecone-cli
  • 建议确认是否会触发联网、命令执行或文件读写操作

SKILL.md

Pinecone CLI (pc)

Manage Pinecone from the terminal. The CLI is especially valuable for vector operations across all index types — something the MCP currently can't do.

CLI vs MCP

CLIMCP
Index typesAll (standard, integrated, sparse)Integrated only
Vector ops (upsert, query, fetch, update, delete)
Text search on integrated indexes
Backups, namespaces, org/project mgmt
CI/CD / scripting

Setup

Install (macOS)

brew tap pinecone-io/tap
brew install pinecone-io/tap/pinecone

Other platforms (Linux, Windows) — download from GitHub Releases.

Authenticate

# Interactive (recommended for local dev)
pc login
pc target -o "my-org" -p "my-project"

# Service account (recommended for CI/CD)
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"

# API key (quick testing)
pc config set-api-key $PINECONE_API_KEY

Check status: pc auth status · pc target --show

Note for agent sessions: If you need to run pc login inside an agent loop, the browser auth link may not surface correctly. It's best to authenticate before starting an agent session. Run pc login in your terminal directly, then invoke the agent once you're authenticated.

Authenticating the CLI does not set PINECONE_API_KEY

pc login authenticates the CLI tool itself — it does not set PINECONE_API_KEY in your environment. Python scripts, Node.js SDKs, and other tools that use the Pinecone SDK need PINECONE_API_KEY set separately.

Use the CLI to create a key and export it in one step:

KEY=$(pc api-key create --name agent-sdk-key --json | jq -r '.value')
export PINECONE_API_KEY="$KEY"

Without jq: run pc api-key create --name agent-sdk-key --json and copy the "value" field manually.


Common Commands

TaskCommand
List indexespc index list
Create serverless indexpc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
Index statspc index stats -n my-index
Upload vectors from filepc index vector upsert -n my-index --file./vectors.json
Query by vectorpc index vector query -n my-index --vector '[0.1,...]' -k 10 --include-metadata
Query by vector IDpc index vector query -n my-index --id "doc-123" -k 10
Fetch vectors by IDpc index vector fetch -n my-index --ids '["vec1","vec2"]'
List vector IDspc index vector list -n my-index
Delete vectors by filterpc index vector delete -n my-index --filter '{"genre":"classical"}'
List namespacespc index namespace list -n my-index
Create backuppc backup create -i my-index -n "my-backup"
JSON output (for scripting)Add -j to any command

Interesting Things You Can Do

Query with custom vectors (not just text)

Unlike the MCP, the CLI lets you query any index with raw vector values — useful when you generate embeddings externally (OpenAI, HuggingFace, etc.):

pc index vector query -n my-index \
  --vector '[0.1, 0.2, ..., 0.9]' \
  --filter '{"source":{"$eq":"docs"}}' \
  -k 20 --include-metadata

Pipe embeddings directly into queries

jq -c '.embedding' doc.json | pc index vector query -n my-index --vector - -k 10

Bulk metadata update with preview

# Preview first
pc index vector update -n my-index \
  --filter '{"env":{"$eq":"staging"}}' \
  --metadata '{"env":"production"}' \
  --dry-run

# Apply
pc index vector update -n my-index \
  --filter '{"env":{"$eq":"staging"}}' \
  --metadata '{"env":"production"}'

Backup and restore

# Snapshot before a migration
pc backup create -i my-index -n "pre-migration"

# Restore to a new index if something goes wrong
pc backup restore -i <backup-uuid> -n my-index-restored

Automate in CI/CD

export PINECONE_CLIENT_ID="..."
export PINECONE_CLIENT_SECRET="..."
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
pc index vector upsert -n my-index --file ./vectors.jsonl --batch-size 1000

Script against JSON output

# Get all index names as a list
pc index list -j | jq -r '.[] | .name'

# Check if an index exists before creating
if ! pc index describe -n my-index -j 2>/dev/null | jq -e '.name' > /dev/null; then
  pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
fi

Reference Files

Documentation

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.39%
按下载量换算115

Claude

34.02%
按下载量换算111

Cursor

17.57%
按下载量换算57

Gemini CLI

10.29%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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