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supacortexsupacortex 文档

Agent Skill

supacortex 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

710

周安装

29

GitHub Stars

公开资料未说明

下载量

227
CodexClaudeCursorGemini CLI

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:supacortex(supacortex 文档)
来源仓库:https://github.com/monorepo-labs/skills
仓库路径:skills/supacortex
安装命令:
npx skills add https://github.com/monorepo-labs/skills --skill supacortex
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/monorepo-labs/skills --skill supacortex

简介

用于保存与检索 Agent 对话历史至内存知识库。

  • 支持简要、总结与详细三种层级的结构化记录。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 适合跟踪架构决策、研究成果与关键讨论要点。
  • 数据存储依赖本地文件系统,需定期备份以防丢失。
  • supacortex 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Supacortex — CLI Skill

Supacortex is a personal memory layer. Two CLI commands: bookmarks and conversations.

Setup

npm i -g @supacortex/cli
scx login

1. Bookmarks (scx bookmarks)

Save and search bookmarks (links, YouTube videos).

Commands

List bookmarks

scx bookmarks list [--search "<query>"] [--type <tweet|link|youtube>] [--limit <n>] [--offset <n>] [--pretty]

Add a bookmark

scx bookmarks add <url> [--pretty]

Get a bookmark by ID

scx bookmarks get <id> [--pretty]

Delete a bookmark

scx bookmarks delete <id> [--pretty]

When to use bookmarks

  • User asks to save a link or YouTube video
  • User wants to search their saved content
  • When you need to reference previously saved URLs

2. Conversations (scx conversation)

Save summaries of AI chat sessions. Every conversation has a tier that determines its depth.

Tiers

TierWhen to useContent format
briefThrowaway queries, quick lookupsSingle sentence: "Asked about JSON parsing in Bun"
summaryMost working sessionsMarkdown — 3-8 bullet points covering what was discussed, decided, and found
detailedDeep sessions with architectural decisions, research findingsMarkdown — full structured document with headings, reasoning, code snippets, follow-ups

Commands

Save a conversation

scx conversation add "<content>" --tier <brief|summary|detailed> [--title "<title>"] [--metadata '<json>'] [--pretty]

The --tier flag is required. It maps to memory types: conversation_brief, conversation_summary, conversation_detailed.

Examples:

# Brief — one sentence
scx conversation add "Helped debug CORS issue in Hono API" --tier brief

# Summary — bullet points
scx conversation add "- Set up memory table with tsvector search
- Added triggers for auto search vector generation
- Created API routes for CRUD
- Decided on hybrid schema approach" \
  --tier summary \
  --title "Memory system setup" \
  --metadata '{"source": "claude-code"}'

# Detailed — full document
scx conversation add "## Memory Architecture Decision..." --tier detailed --title "Memory layer brainstorm"

List conversations

scx conversation list [--search "<query>"] [--tier <brief|summary|detailed>] [--limit <n>] [--offset <n>] [--pretty]

Get a conversation by ID

scx conversation get <id> [--pretty]

Update a conversation

scx conversation update <id> [--title "<title>"] [--content "<content>"] [--tier <tier>] [--metadata '<json>'] [--pretty]

Delete a conversation

scx conversation delete <id> [--pretty]

When to save conversations

Save when the user says:

  • "save to cortex" / "save to supacortex"
  • "save this session" / "remember this"
  • "log this conversation"

When to recall conversations

Pull past conversations when the user says:

  • "check my conversation about X"
  • "pull the X conversation"
  • "get the summary for X"
  • "what did we work on last time?"

Metadata

Metadata is freeform JSON passed via --metadata. The AI decides what to store. Common fields:

  • source — where this was captured ("claude-code", "chatgpt", "opencode")
  • tags — array of topic tags
  • project — which project the conversation was about (e.g. "supacortex", "supalytics")
  • category — topic area: "project", "life", "general", "learning", "work"

Consistent verbs across all commands

Both commands (bookmarks, conversation) support: list, add, get, delete.

conversation also supports: update.

All commands output JSON by default (optimized for AI agents). Use --pretty for human-readable output.

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

35.68%
按下载量换算81

Claude

29.56%
按下载量换算67

Cursor

19.34%
按下载量换算44

Gemini CLI

9.59%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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