Know which code would hurt the most if it broke.

HighStakes combines static analysis with LLM blast radius assessment to score every file in your repo. It tells you where human review is needed and where AI can handle the rest.

Quick start

go install github.com/zanetworker/highstakes/cmd/highstakes@latest
export OPENROUTER_API_KEY="sk-or-..."

cd /path/to/repo
highstakes init && highstakes analyze
highstakes dashboard

How it works

1. Static analysis scans complexity, dependency graph, and import structure
2. Git analysis measures change frequency and contributor patterns
3. LLM assessment reads each file and asks “if this breaks, what’s the damage?”
4. Scoring combines all signals into a 0-100 heat score per file
5. Tiering maps scores to concrete review requirements

Tier	Score	Review
CRITICAL	86-100	2 senior reviewers + security scan
HIGH	61-85	2 reviewers + integration tests
MEDIUM	31-60	1 reviewer
LOW	0-30	Auto-merge safe

Three ways to see it

Dashboard with treemap and file explorer:

highstakes dashboard

Terminal TUI with keyboard navigation:

CLI with JSON output for CI:

highstakes list --tier high --json
highstakes get src/auth/oidc.rs
highstakes pr check --base main --json

Works with any model

Any OpenAI-compatible endpoint. No vendor lock-in.

Setup	Cost	Data stays
OpenRouter	$0.15/repo	Cloud
Self-hosted vLLM on OpenShift AI	Free	On-prem
Local Ollama	Free	Your laptop

# Cloud
export OPENROUTER_API_KEY="sk-or-..."

# Self-hosted
export HIGHSTAKES_API_KEY="$(oc whoami -t)"
export HIGHSTAKES_API_URL="https://your-model.apps.cluster.dev/v1/chat/completions"

# Local
export HIGHSTAKES_API_KEY="ollama"
export HIGHSTAKES_API_URL="http://localhost:11434/v1/chat/completions"

Documentation

• Getting started
• Configuration
• CI integration
• CLI reference
• How it works

Links

• GitHub repo
• Blog post