Knowtation — Standalone Product & Architecture Plan (March 2026)

This document defines Knowtation (know + notation) as a standalone, general-purpose tool: CLI + Skill Manifest first, optional MCP, with memory and intent attestation (AIR) for full multimedia launch scenarios. It is the product and architecture spec for this repository.

1. Knowtation as a Standalone Product

• Knowtation is its own repository and its own tool. It is a general-purpose personal knowledge and content system that anyone can use.
• Value proposition: One place to capture, transcribe, index, and search notes and media; one CLI (and optional MCP) so many AI agents can use it without tool-definition context bloat; optional memory and AIR for traceable, authorized workflows.
• Users: Individuals and teams who want to own their knowledge base and run multimedia workflows (blogs, podcasts, reels, books, marketing, analysis) with clear provenance and governance.

2. CLI + Skill Manifest First, MCP When Needed

• Primary interface: One CLI, knowtation, with subcommands (search, get-note, list-notes, index, and optionally write, export). Agents discover usage via SKILL.md and knowtation --help; no large MCP schema in context.
• MCP optional: Offer an MCP server that wraps the same backend when a client only speaks MCP or you need stateful sessions / OAuth.
• Orchestration: The agent runtime (Cursor, Claude, etc.) discovers the skill, reads SKILL.md when the task matches, and invokes the CLI; no separate orchestration service.

3. Memory and AIR

• Memory: One supported layer (e.g. Mem0 or SAME) for decisions, provenance (“which notes fed this export”), and cross-session context. Expose via CLI (and MCP if present).
• AIR: Pre-execution intent attestation (e.g. Null Lens) recommended before write (except inbox), export, publish, and analysis. Log AIR id with the action.

See the full scenario coverage (capture → index → search → content → marketing → analysis → governance) and tool options in the sections below and in the repo docs.

4. Scenario Coverage (Summary)

• Capture: Inbox and transcription; optional memory for rules.
• Index & search: CLI returns ranked notes/chunks; memory for last index.
• Content creation: Export to blog/podcast/reel/book; provenance (source_notes); AIR before export.
• Marketing: Agents pull copy/assets from Knowledger; memory for campaigns; AIR before approve/schedule.
• Analysis: Agents query Knowledger; memory for last run; AIR before analysis.
• Governance: Logging, agent-generated tags, provenance chain.

5. Next Steps in This Repo

1. Implement CLI subcommands (wire to vault and vector store).
2. Add indexer (vault → chunk → embed → Qdrant or sqlite-vec).
3. Add transcription and capture pipelines.
4. Integrate memory and AIR as in this plan.
5. Optionally add MCP server that wraps the same backend.

Full detailed plan (tables, comparisons, novel AIR uses) is kept in the originating doc; this file is the in-repo summary and reference.