faultlines 0.9.1

Map features in any codebase from git history alone. No Jira required.

faultlines

Map features in any codebase from git history alone. No Jira required.

Point faultlines at a git repo and get back a feature map — which parts of your product accumulate the most bug fixes, who owns what, and where the risk is hiding. Works on any language, any size, any repo.

Try it in 30 seconds

pip install faultlines
export ANTHROPIC_API_KEY=sk-ant-...   # or use Ollama for free local analysis
faultlines analyze .

That's it. You'll see something like:

╭──────────────────────────── FeatureMap Analysis ─────────────────────────────╮
│ Repository: /home/you/your-project                                           │
│ Features found: 22     Bug fix commits: 125     Health: 60.2/100             │
╰──────────────────────────────────────────────────────────────────────────────╯

                                Features by Risk
╭─────┬────────────────────────┬────────┬─────────┬───────────┬────────────────╮
│     │ Feature                │ Health │ Commits │ Bug Fixes │ Bug %          │
├─────┼────────────────────────┼────────┼─────────┼───────────┼────────────────┤
│ ✗   │ payments               │   23   │     112 │        38 │ 33.9%          │
│ ✗   │ booking-engine         │   29   │     487 │       151 │ 31.0%          │
│ !   │ auth                   │   54   │      48 │        12 │ 25.0%          │
│ ✓   │ dashboard              │   83   │      67 │         2 │  3.0%          │
╰─────┴────────────────────────┴────────┴─────────┴───────────┴────────────────╯

What it does

1. Reads your git history — commits, blame, file changes over the last year
2. Clusters files into features — using Claude Sonnet (or local Ollama) to group by business domain, not folder names
3. Scores each feature — by bug-fix density, churn, bus factor, and age-weighted trends
4. Detects user flows — with --flows, breaks features into end-to-end user journeys (checkout-flow, signup-flow, manage-team-flow)
5. Maps test coverage per feature — with --coverage, shows which features and flows are well-tested and which are exposed
6. Outputs JSON — ~/.faultlines/feature-map-*.json, ready for dashboards or CI

Without LLM vs with LLM

Without --llm:  "components", "views", "hooks"           ← technical folders
With --llm:     "payments", "booking-engine", "auth"     ← business features

The LLM reads your file tree and commit messages to produce names an engineering manager would actually use.

Tested on real OSS repos

Every number below is from a real faultlines analyze --llm --flows run. Reproduce any of them yourself.

Repo	Files	Features	Flows	Time	What it found
cal.com	10,463	282	725	23m	trpc/viewer, web/bookings, ee/billing, web/settings
plane	4,932	134	408	12m	web/issues, editor/extensions, web/workspace, web/pages
Ghost	6,898	101	281	14m	admin-x, ghost/members, ghost/email, stats
outline	2,390	22	188	6m	rich-text-editor, api-backend, dashboard, plugins
documenso	2,530	49	191	8m	trpc/envelope, remix/document-signing, ee/billing
formbricks	3,316	33	136	8m	web/survey, web/organization, web/auth
excalidraw	1,225	15	35	4m	excalidraw/shared-ui, excalidraw/data, renderer
trpc	1,573	14	37	1m	server/core, client/links, openapi, next-adapter
gin	130	22	—	15s	binding, render, context, recovery, logger
fastapi	2,981	14	—	80s	routing, dependencies, security, openapi, middleware

Libraries (gin, fastapi) are auto-detected and show modules instead of business features. Flows are suppressed for libraries since they don't have end-user journeys.

Installation

pip install faultlines

Requires Python 3.11+.

With Ollama (local, free, private)

pip install 'faultlines[ollama]'

Usage

Basic (heuristic, no API key needed)

faultlines analyze .
faultlines analyze ./path/to/repo
faultlines analyze . --days 90 --top 5

AI-powered (recommended)

# Claude (cloud) — best quality
export ANTHROPIC_API_KEY=sk-ant-...
faultlines analyze . --llm

# With flow detection
faultlines analyze . --llm --flows

# Focus on source directory
faultlines analyze . --llm --flows --src src/

# Ollama (local, free)
ollama pull llama3.1:8b
faultlines analyze . --llm --provider ollama

Get your API key at console.anthropic.com.

Monorepo support

Automatically detects and analyses workspace packages:

• pnpm (pnpm-workspace.yaml)
• npm/yarn (package.json workspaces)
• Turborepo, Nx, Lerna
• Cargo workspaces, Go workspaces

Large monorepos are scanned per-package in parallel (4 workers).

Flow detection

With --flows, each feature is broken into user-facing flows — named action sequences like checkout-flow or manage-team-flow.

╭──────┬───────────────────────────┬────────┬─────────┬───────────╮
│      │ Feature / Flow            │ Health │ Commits │ Bug Fixes │
├──────┼───────────────────────────┼────────┼─────────┼───────────┤
│  ✗   │ web/settings              │   23   │     206 │       144 │
│      │   ├─ manage-org-roles     │   11   │      37 │        30 │
│      │   ├─ manage-oauth-clients │   15   │      13 │        10 │
│      │   └─ manage-billing       │   50   │      24 │        12 │
│  ✓   │ auth/server               │   87   │      18 │         6 │
│      │   └─ login-flow           │   87   │      18 │         6 │
╰──────┴───────────────────────────┴────────┴─────────┴───────────╯

Flow names and health scores are real — from actual Haiku detection against git commit history.

Test coverage per feature

With --coverage, faultlines reads your existing coverage report and maps it to features and flows:

# Python
coverage run -m pytest && coverage json
faultlines analyze . --llm --flows --coverage coverage.json

# JS/TS (Jest or Vitest)
npx jest --coverage
faultlines analyze . --llm --flows --coverage coverage/coverage-summary.json

# Auto-detect (looks for common coverage files)
faultlines analyze . --llm --flows --coverage

Supports: Python coverage.py (.coverage, coverage.json), Cobertura XML, Jest/NYC, LCOV.

╭───┬──────────────────┬────────┬───────┬─────────╮
│   │ Feature          │ Health │ Bug % │   Cov % │
├───┼──────────────────┼────────┼───────┼─────────┤
│ ✗ │ element/arrows   │   13   │  75%  │     50% │
│ ✗ │ font-management  │   16   │  67%  │     97% │
│ ! │ excalidraw/data  │   39   │  63%  │     60% │
│ ✓ │ math             │   69   │  50%  │     91% │
╰───┴──────────────────┴────────┴───────┴─────────╯

The insight: element/arrows has 75% bug ratio and only 50% coverage — the exact spot where adding tests would prevent the most regressions.

Output format

Results save to ~/.faultlines/feature-map-{repo}-{timestamp}.json:

{
  "repo_path": "/path/to/repo",
  "remote_url": "https://github.com/org/repo",
  "features": [
    {
      "name": "payments",
      "description": "Stripe payment processing and subscription billing",
      "health_score": 23.0,
      "bug_fix_ratio": 0.339,
      "total_commits": 112,
      "bug_fixes": 38,
      "authors": ["alice", "bob"],
      "paths": ["src/payments/stripe.py", "src/payments/webhooks.py"],
      "flows": [
        {
          "name": "checkout-flow",
          "health_score": 18.0,
          "total_commits": 67,
          "bug_fixes": 28,
          "bus_factor": 1,
          "hotspot_files": ["src/payments/charge.ts"]
        }
      ]
    }
  ]
}

Health score

The health score (0–100) uses an age-weighted sigmoid based on bug-fix ratio:

Bug fix %	Health	Status
0–20%	85–99	✓ Healthy — mostly feature work
20–40%	55–85	✓ Normal active development
40–55%	30–55	! Elevated — worth watching
55–75%	10–30	✗ High debt — maintenance-dominant
75%+	0–10	✗ Critical — almost all bug fixes

Recent bugs (< 30 days) weigh 2x more than older ones, so features that are actively getting worse score lower.

CLI reference

faultlines analyze [REPO_PATH]

Flag	Default	Description
--llm	off	AI-powered semantic feature detection
--flows	off	Detect user-facing flows (requires --llm)
--provider	anthropic	LLM provider: anthropic or ollama
--model	auto	Model override
--api-key	env	Anthropic API key
--src	—	Focus on subdirectory (e.g. src/)
--days	365	Days of git history
--top	3	Top risk zones to highlight
--output	~/.faultlines/	Output file path
--coverage	auto	Path to coverage report (lcov, jest, cobertura, coverage.py)
--no-save	—	Don't save JSON
--legacy	—	Use pre-rewrite 5-strategy pipeline
--ollama-url	localhost:11434	Custom Ollama URL

How it works

1. git log + git blame → commit history with file attribution
2. Heuristic candidates from directory structure and import graph
3. Claude Sonnet (or Ollama) merges/renames/splits candidates into business features via an operations-based prompt
4. Per-feature health scoring with sigmoid-weighted bug-fix ratio
5. Optional: Haiku detects user-facing flows per feature from signatures and commit patterns
6. JSON output + terminal report

For monorepos, step 3 runs per-package in parallel (ThreadPoolExecutor, 4 workers).

Cost

Repo size	Estimated LLM cost	Time
< 500 files	~$0.01–0.05	15–30s
500–2,000	~$0.10–0.30	1–5m
2,000–5,000	~$0.30–0.70	5–12m
5,000–10,000	~$0.70–2.00	10–25m

Ollama is free (runs locally). Heuristic mode (no --llm) is free and instant.

Who is this for

• Engineering managers — see where technical debt actually lives, not where you think it lives
• Tech leads — prioritise refactoring with data, not gut feeling
• New team members — understand which parts of the codebase need the most care
• Sprint planning — make "should we refactor X?" a data conversation

Contributing

Issues, PRs, and feedback welcome at github.com/PashaSchool/faultlines.

License

MIT