Faultlines turns raw git history into a living map of every feature and user flow in your repo — with bug hotspots, test coverage, and the precise context your AI coding agent actually needs.
No Jira. No annotations. No manual tagging. Just git log and your code.
Every engineer rediscovers the same thing on a new codebase:
"Which files actually implement checkout?" "What breaks if I change this?" "Who owns the billing flow?" "Where are the bugs hiding?"
Your issue tracker doesn't know — it's full of aspirational tickets.
Static analysis sees imports, not intent.
And your AI agent just greps 15 files and burns your context window guessing.
The answers were in your git history the whole time. Faultlines reads them.
$ faultlines scan-v2 ./my-app --llm --flows --symbols
✓ 472 commits analysed · 1,284 files mapped
FEATURE HEALTH COVERAGE HOTSPOTS FLOWS
─────────────────────────────────────────────────────────
Billing & Subscriptions 62 48% 3 7
Authentication 88 91% 0 4
Document E-Sign 41 ⚠ 22% 5 9
Team & Permissions 79 66% 1 5
…
→ Highest blast radius: src/payments/charge.ts (touches 4 features)
→ Riskiest flow: e-sign/finalize (low coverage · 5 recent bug-fixes)
A two-layer feature map:
- Developer features — code-grounded, the units your engineers actually work in.
- Product features — the customer-facing capabilities those roll up into.
…each broken into flows (real user journeys), scored, attributed down to the function and line range, and served to humans and AI agents.
- 🧭 Feature & flow detection — from git history + code structure, on any stack (Next.js, Rails, Django, FastAPI, Express, Spring, Laravel, Phoenix, and more).
- 🔥 Bug hotspots & health scores — find what's rotting before it pages you.
- 🎯 Behavioral test coverage — coverage per user flow, inferred from history even when there's no
lcov. - 💥 Change-impact / blast radius — "if I touch these files, here's what breaks and who to add as reviewer."
- 🔬 Symbol-level attribution — functions, classes and line ranges per flow, not just file lists.
- 👥 Ownership & bus-factor — who maintains each feature, and where the knowledge is dangerously concentrated.
- 🤖 MCP server for AI agents — 13 typed tools your coding agent calls instead of grepping.
- 📡 Runtime overlays — map Sentry errors and PostHog usage onto features (which features actually fail and get used).
- 🔒 Local-first & private — runs on your machine; your source code never has to leave it.
pip install faultlines
# Scan a repo — deterministic by default, add --llm for richer naming
faultlines scan-v2 /path/to/your/repo --llm --flows --symbolsThat writes a versioned feature-map JSON to ~/.faultline/. Explore it, diff it across runs, ship it to CI, or hand it to your AI agent (below).
This is the wedge. Install the companion MCP server and your agent stops guessing:
pip install faultlines-mcpNow Cursor / Claude Code / Cline / Windsurf can call 13 tools:
| Tools | |
|---|---|
| 🔎 Discover | list_features · find_feature · get_repo_summary |
| 📁 Files & symbols | get_feature_files · get_flow_files · find_symbols_in_flow · find_symbols_for_feature |
get_hotspots · get_feature_owners · analyze_change_impact · get_regression_risk |
|
| 📡 Runtime | get_feature_errors (Sentry) · get_feature_pageviews (PostHog) |
Typical result: ~90% fewer tokens per query than a naive grep-and-read loop — your agent reads the right functions, with line ranges, on the first try.
| Metric | What it tells you | Why you care |
|---|---|---|
| Health score | Composite of churn, bug-fixes, coverage & ownership | One number to triage what to refactor next |
| Bug-fix ratio | Share of commits that fix bugs | High = fragile, defect-prone code |
| Churn | How often a feature changes | Hotspot detection; instability signal |
| Impact score | Structural blast radius − coverage | What a change here actually endangers |
| Coverage | Behavioral test coverage per flow | Find untested user journeys, not just untested lines |
| Ownership / bus factor | Who holds the knowledge | Spot single-points-of-failure before they leave |
git history ─┐
├─▶ deterministic extractors ─┐
code/config ─┘ (routes · MVC · schema · │
package · stack patterns) ├─▶ feature & flow map
│ + metrics + symbols
optional LLM pass (naming · flows) ────┘ │
▼
feature-map JSON ──▶ CLI · CI · dashboard · MCP
Deterministic-first. The structure comes from your routing conventions, configs, schemas and git co-change patterns. An optional LLM pass adds human-readable names and flow detection. The output is a single versioned JSON — the stable contract every consumer reads.
- GitHub — PR comments with risk, coverage gaps and runtime signal on the exact features a diff touches.
- Sentry — production errors mapped to features.
- PostHog — real usage & traffic per feature.
- Slack — weekly digest of top risks, coverage gaps and hotspots.
- …grep / read the files? Burns context and misses cross-boundary, runtime and historical coupling that static analysis can't see.
- …SonarQube / linters? Great for line-level issues; blind to features, flows and blast radius.
- …your issue tracker? Describes intent, not reality. Faultlines is grounded in what the code and history actually say.
Faultlines is the only layer that joins structure + git history + runtime into one map — and serves it to your AI agent.
- Two-layer feature/flow map on any stack
- Behavioral test coverage & health scoring
- Symbol-level attribution
- MCP server (13 tools) — Local · Hosted · VPC
- Sentry + PostHog runtime overlays
- Incremental, sub-second re-scans on every commit
- Native plugins for more agents & IDEs
Issues, ideas and PRs are welcome. Faultlines is built to map any codebase — if it mis-reads your stack, that's a bug we want to hear about.
MIT — see LICENSE.
⭐ Star this repo if Faultlines helps you (or your agent) understand a codebase faster.
Made for engineers and the AI agents that work alongside them · faultlines.dev