afterpaths

# Afterpaths

**Smarter with every session, automatically.**

Extract rules from what worked. Track what didn't. Find the best models for your stack.

You're running Claude Code, Cursor and Codex, but which model actually works best for your stack? What approaches lead to breakthroughs vs. expensive dead ends? How do you stop your agents from making the same mistakes?

Afterpaths gives you a single view across all your AI coding tools: compare what's working, capture discoveries as rules, and guide your agent team away from costly diversions.

![Afterpaths Demo](demo.gif)

**What you're seeing:**
1. **`ap audit`** — Overview of your sessions, models used, and rule status
2. **`ap show 5 --raw`** — Raw session transcript (the messy JSONL data, made readable)
3. **`ap show 5`** — LLM-generated summary extracting discoveries, dead ends, and decisions
4. **`head .cursor/rules/gotchas.md`** — Rules automatically extracted and ready for Cursor's next session

## The Problem

You're managing multiple agents - retaining critical context and learning from sessions is painful:

- **Repeated mistakes** — Your agents hit the same gotchas. Three weeks later, same dead end, same wasted tokens.
- **No cross-tool visibility** — Is Opus actually better than Sonnet for your codebase? Is Cursor outperforming Claude Code? You're guessing.
- **Rules are tedious** — After a costly diversion, the last thing you want is to write a CLAUDE.md rule. So you don't. And the knowledge evaporates.
- **Sessions vanish** — Session content is obscurely logged and hard to extract. Then it's often auto-deleted after 30 days. That breakthrough architecture decision? Context gone.

Afterpaths captures sessions across tools, surfaces what's working, and generates rules automatically—so your agents learn from every session, and you retain all your rich session context.

## How It Works

```
Your Sessions                      Afterpaths
───────────────                    ────────────────────────────────────