supacortex

Personal memory layer — save bookmarks and conversation summaries using the Supacortex CLI. Use when the user says "save to cortex", "save to supacortex", "save this session", or asks to recall past conversations.

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Install skill "supacortex" with this command: npx skills add monorepo-labs/skills/monorepo-labs-skills-supacortex

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.

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