# Agent Knowledge

> Your long-term knowledge pages. Read them at session start. Create new pages when you learn something worth remembering across sessions. Pages auto-update from your conversations via Hindsight.

- Skill: `vectorize-io/agent-knowledge` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vectorize-io/agent-knowledge`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vectorize-io/agent-knowledge/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: vectorize-io (https://skillmd.com/u/vectorize-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vectorize-io/agent-knowledge

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# Agent Knowledge

You have knowledge pages that persist across sessions and auto-update from your conversations.

**How it works:** Your conversations are automatically retained into a Hindsight memory bank. The system extracts observations and uses them to keep your pages current. Each page has a "source query" — a question the system re-answers after every consolidation cycle to rebuild the page content. You create pages; the system maintains them.

## At session start

Call `agent_knowledge_list_pages` to see what pages exist, then `agent_knowledge_get_page` for each one you need.

## Reading

- `agent_knowledge_list_pages()` — list page IDs and names (no content)
- `agent_knowledge_get_page(page_id)` — read the full content of a page

## Creating pages

When you learn something durable — a user preference, a working procedure, performance data — create a page immediately.

`agent_knowledge_create_page(page_id, name, source_query)`

- `page_id`: lowercase with hyphens (`editorial-preferences`)
- `source_query`: a question that produces the page content from observations

Examples:

- `"What are the user's preferences for tone, length, and formatting?"`
- `"What content strategies have performed well or poorly? Include numbers."`
- `"What are the best practices for [topic], preferring our data over generic advice?"`

## Searching memories

`agent_knowledge_recall(query)` — search across all retained conversations and documents for specific facts.

Use when pages don't cover what you need.

## Ingesting documents

`agent_knowledge_ingest(title, content)` — upload raw content into memory. Never summarize before ingesting. Pass the full text inline.

`agent_knowledge_ingest_files(paths)` — ingest one or more files straight from disk. `paths` is a list of file paths or glob patterns (e.g. `["docs/**/*.md", "/abs/path/notes.txt"]`). Each file's content is read and stored under a document ID derived from its path. Prefer this over `agent_knowledge_ingest` when the content already lives in files — no need to read them first. Use absolute paths when in doubt; relative paths resolve against the working directory.

## Updating and deleting

- `agent_knowledge_update_page(page_id, name?, source_query?)` — change what a page tracks
- `agent_knowledge_delete_page(page_id)` — remove a page

## Important

- Pages update automatically — don't edit content directly
- State preferences clearly in your responses so the system captures them
- Create pages silently — don't announce it to the user
- Prefer fewer broad pages over many narrow ones