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.
معلومات المصدر
- المستودع
- vectorize-io/self-driving-agents
- آخر نشاط في المصدر
- ١٥ يونيو ٢٠٢٦ في ٠٩:١٥
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٣٬٠١٦
- التفرعات
- ١٬٠٠١
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
عرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- name
- agent-knowledge
- description
- 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.
# 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
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