inmemoria

Original：🇺🇸 English

Translated

Use when building persistent codebase intelligence for AI agents or integrating knowledge systems via MCP

2installs

Sourcezenobi-us/dotfiles

Added on2026-02-13

NPX Install

npx skill4agent add zenobi-us/dotfiles inmemoria

SKILL.md Content

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In Memoria: Persistent Codebase Intelligence

In Memoria is an MCP server that learns your codebase patterns once, then exposes that intelligence to AI agents persistently. Instead of re-analyzing code on every interaction, it maintains a semantic understanding of your architecture, conventions, and decisions.

Core Concept

Setup → Learn → Verify → Serve. After that, AI agents query persistent intelligence without repeated parsing.

Quick Start (5 minutes)

bash

# 1. Configure for your project
npx in-memoria setup --interactive

# 2. Build intelligence database
npx in-memoria learn ./src

# 3. Verify it worked
npx in-memoria check ./src --verbose

# 4. Keep it fresh (optional but recommended)
npx in-memoria watch ./src

# 5. Expose to agents via MCP
npx in-memoria server

When to Use

✅ Use In Memoria:

Building long-lived AI agent partnerships (Claude, Copilot, etc.)
Projects where consistency across sessions matters
Teams wanting shared codebase intelligence

❌ Skip it:

One-off analysis (use
```
npx in-memoria analyze [path]
```
directly)
Simple projects agents can read directly

The 5 Core Commands

Command	Purpose	When
`setup --interactive`	Configure exclusions, paths, preferences	First time only
`learn [path]`	Build/rebuild intelligence database	After setup, major refactors
`check [path]`	Validate intelligence layer	After learn, before server
`watch [path]`	Auto-update intelligence on code changes	During development (optional)
`server`	Start MCP server for agent queries	After check passes

Key difference:

learn

builds persistent knowledge.

analyze

is one-time reporting only.

What Agents See

When connected, agents can query:

Project structure - Tech stack, entry points, architecture
Code patterns - Your naming conventions, error handling, patterns used
Smart routing - "Add password reset" → suggests
```
src/auth/password-reset.ts
```
Semantic search - Find code by meaning, not keywords
Work context - Track decisions, tasks, approach consistency

Troubleshooting

Issue	Fix
Learn fails	Verify path is correct; check file permissions
Check reports missing intelligence	Run `learn [path]` again
Agent doesn't see new code	Is `watch` running? Start it: `npx in-memoria watch ./src`
Server won't start	Run `check --verbose` first; if issues, rebuild: `rm .in-memoria/*.db && npx in-memoria learn ./src`
Multiple projects conflict	Use `server --port 3001` (or different port per project)

Performance Notes

Small projects (<1K files): 5-15s to learn
Medium (1K-10K files): 30-60s
Large (10K+ files): 2-5min

If learning stalls (>10min), verify you're not indexing

node_modules/

,

dist/

, or build artifacts—use setup's exclusion patterns.

Key Principles

Local-first - Everything stays on your machine; no telemetry
Persistent - One learning pass; intelligence updates incrementally with
```
watch
```
Agent-native - Designed for MCP; works with Claude, Copilot, and any MCP-compatible tool
Pattern-based - Learns from your actual code, not rules you define

Deployment Pattern (3 terminals)