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.

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 .claude/rules/gotchas.md — Rules automatically extracted and ready for Claude'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
───────────────                    ────────────────────────────────────

Claude Code  ──► ap log      ──► Browse sessions across IDEs
Cursor           ap stats    ──► Analytics: tokens, activity, errors
Codex            ap summarize──► Session summaries (what happened)
                 ap rules    ──► Rule files (what to remember)
                 ap search   ──► Find past discussions & discoveries
                                    │
                                    ▼
                           .claude/rules/ · .cursor/rules/
                                    │
                                    ▼
                           Your next session is smarter

Quick Start

pip install afterpaths

# Navigate to your project (rules are project-specific)
cd ~/code/your-project

# Run audit to see what you have
ap audit

The audit shows your sessions across all tools, model performance, and whether you have rules set up. No API key needed.

From there, the recommended flow:

# 1. Browse sessions and find significant work
ap log

# 2. Summarize important sessions (requires API key)
export ANTHROPIC_API_KEY="sk-ant-..."
ap summarize 1

# 3. Extract rules from summaries → .claude/rules/
ap rules

# 4. Search across past sessions
ap search "database schema"
ap search "auth" --deep    # also search raw transcripts

# 5. Track ongoing performance
ap stats
ap stats --daily

Tip: ap is the short alias for afterpaths. Both work identically.

All commands support --json for structured output (e.g., ap log --json, ap show 1 --json, ap search "query" --json).

See docs/commands.md for the full command reference and recipes.

From Session to Rules

Real example: A bug causing 76 missing sessions became a rule that prevents the same mistake.

While building afterpaths, sessions for a project weren't showing up. The path (let's call it) /Users/Code/foo_bar was being decoded as /Users/Code/foo/bar. After investigation, we discovered Claude Code uses lossy path encoding—both / and _ become -.

The summary captured the discovery:

## Discoveries

- **Claude Code's path encoding is lossy**: Project paths in `~/.claude/projects/`
  are encoded by replacing `/` with `-`, but underscores are ALSO converted to
  hyphens. Three different paths encode identically:
  - `/Users/Code/foo_bar` → `-Users-Code-foo-bar`
  - `/Users/Code/foo-bar` → `-Users-Code-foo-bar`
  - `/Users/Code/foo/bar` → `-Users-Code-foo-bar`

ap rules extracted it into .claude/rules/gotchas.md:

- **Claude Code lossy path encoding**: Claude Code encodes project paths by
  replacing both `/` and `_` with `-`, making them indistinguishable when
  decoding. When decoding, try underscore variants alongside hyphen variants
  at each greedy step, preferring longer segments (single directories) over
  nested paths.
  _Source: 91b1ffbc_

Next time Claude works on path decoding in this codebase, it already knows about the lossy encoding—no need to rediscover it.

Why Afterpaths

What Gets Extracted

Each rule includes source session references so you can trace back to the original context.

Supported Tools

Known Limitations

Cursor sessions: the adapter reads several known chat/composer formats from state.vscdb (including workbench.panel.aichat.view.aichat.chatdata, composer.composerData, and allComposers), but Cursor changes its storage schema often and some sessions will return Entries: 0 even when the state.vscdb file is present. When this happens, ap show <ref> --raw and the afterpaths_show_session MCP tool emit a warning rather than silently returning empty. The raw file is untouched — if you need that session, opening state.vscdb directly with sqlite3 will still work.

Tool provenance features (ap show --artifacts, commit tracing, file activity) only cover sessions whose entries the adapter can read.

MCP Server

Afterpaths includes an MCP server that puts session history directly into your agent's tool list. Instead of relying on agents to discover the CLI, the MCP server makes session search, summaries, and rules available as native tools.

# Install with MCP support
pip install afterpaths[mcp]

# Add to Claude Code
claude mcp add afterpaths -- afterpaths-mcp

# Or run directly
python -m afterpaths.mcp_server

Tools exposed:

Once configured, agents can ask "have we seen this before?" or "what were the dead ends?" and get answers from your session history.

Privacy

• All local — Summaries and rules stay in your project
• Your API key — Uses your Anthropic/OpenAI key
• Read-only — Never modifies your source code
• Gitignored — .afterpaths/ excluded by default

Storage

your-project/
├── .afterpaths/           # Summaries (gitignored)
│   ├── summaries/
│   └── meta.json
├── .claude/
│   └── rules/             # Generated rules (commit these!)
│       ├── dead-ends.md
│       ├── gotchas.md
│       └── patterns.md
└── src/

Hand-edited notes in rule files

Each rule file is split into a manual section (preserved across ap rules runs) and an auto section (regenerated each run):

# Gotchas: Things to Watch For

<!-- afterpaths:manual:start -->
_Add project-specific notes here — preserved across `ap rules` runs._
<!-- afterpaths:manual:end -->

<!-- afterpaths:auto:start -->
> Auto-generated by `afterpaths distill` | Updated: 2026-04-21 12:00
> Sources: 3 session(s)

## Rules

- **Some extracted rule**: ...
  _Source: abc12345_
<!-- afterpaths:auto:end -->

Put hand-curated rules or project-specific reminders between the manual:start / manual:end markers — they'll survive every ap rules run. Content outside those markers (and outside the recognized boilerplate) blocks the run as a safety check. Pass --force to overwrite anyway, or wrap the content in manual markers to preserve it.

If you're upgrading from an earlier version and your rule files contain only auto-generated content, markers are inserted automatically on the next ap rules run with an empty manual section — no action required.

Roadmap

• Claude Code session parsing
• Cursor session support
• Session analytics (tokens, errors, daily trends)
• LLM summarization
• Automatic rule extraction
• Multi-target export (Claude, Cursor)
• Codex CLI support
• Cross-session search (ap search)
• JSON output (--json flag)
• MCP server for agent integration
• Semantic search across sessions
• Benchmarking and productivity insights

License

MIT

Manage your AI coding agents. Learn what works. Stop repeating mistakes.