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@rigour-labs/cli
Advanced tools
AI-native quality gates with local LLM analysis. Forces AI agents (Claude, Cursor, Copilot, Cline, Windsurf) to meet engineering standards. Bayesian Brain learns your codebase. Zero config: npx rigour-scan.
AI Agent Governance CLI — quality gates, DLP, drift detection, and deep analysis. Rigour governs what goes IN (DLP), what comes OUT (quality gates), and what gets PERSISTED (memory governance).
Core gates run locally. Deep analysis can run local or cloud provider mode.
npx @rigour-labs/cli scan # Zero-config scan (auto-detect stack)
npx @rigour-labs/cli init # Initialize config, hooks, DLP, governance
npx @rigour-labs/cli check # Verify code quality (27+ gates)
npx @rigour-labs/cli run -- claude "Build feature X" # Agent loop
brew tap rigour-labs/tap
brew install rigour
AI agents are powerful but ungoverned. They claim success based on narrative, not execution. Credentials get cached in agent memory. Imports get hallucinated. Code quality drifts across sessions.
Rigour breaks this cycle with deterministic PASS/FAIL gates, credential interception, and memory governance — all local-first.
Agent writes code → Rigour checks → FAIL? → Fix Packet → Agent retries → PASS ✓
DLP: User input → Credential scan → BLOCK before agent sees it
Memory: Agent writes CLAUDE.md → Rigour intercepts → Forces rigour_remember (DLP-scanned)
| Gate | Description |
|---|---|
| File Size | Max lines per file (default: 300-500) |
| Content Hygiene | No TODO/FIXME comments allowed |
| AST Analysis | Cyclomatic complexity, method count, nesting depth, function length |
| Required Docs | SPEC.md, ARCH.md, README must exist |
| File Guard | Protected paths, max files changed |
| Security Patterns | XSS, SQL injection, hardcoded secrets, command injection, path traversal |
| Frontend Secret Exposure | API keys in client-side bundles |
| Deprecated APIs | Node, Python, Web, Go, C#, Java deprecated usage |
| Test Quality | Empty tests, tautological assertions, mock-heavy, snapshot abuse |
| Side-Effect Safety | Unbounded timers, recursive depth, resource lifecycle, retry loops |
| Gate | Description |
|---|---|
| Hallucinated Imports | Imports referencing non-existent modules (JS/TS, Python, Go, Ruby, C#, Rust, Java, Kotlin) |
| Phantom APIs | Non-existent stdlib/framework methods the LLM invented |
| Promise Safety | Unhandled async, unsafe JSON.parse, floating fetch across 6 languages |
| Duplication Drift | Three-pass: MD5 exact → AST Jaccard (tree-sitter) → semantic embedding (384D cosine) |
| Style Drift | Naming conventions, error handling, import style fingerprinted against project baseline |
| Logic Drift | Comparison operators (>= → >), branch counts, return statements tracked per function |
| Context Window Artifacts | Quality degradation within long files — clean top, messy bottom |
| Inconsistent Error Handling | Same error type handled differently across sessions |
| Dependency Bloat | Unused deps, heavy alternatives (moment→dayjs), duplicate purpose packages |
| Gate | Description |
|---|---|
| Memory Governance | Blocks agent writes to CLAUDE.md, .clinerules, .windsurf/memories/ |
| Skills Governance | Blocks agent writes to .claude/skills/, .cursor/rules/ |
| Governance DLP | Scans content written to any governed file for credentials |
Every failure carries a provenance tag (ai-drift, traditional, security, governance) and contributes to two sub-scores: AI Health Score (0–100) and Structural Score (0–100).
Real-time credential interception via PreToolUse hooks — blocks credentials before agents see them.
Two-tier supervision: inline hooks (<200ms per file write) + checkpoint suite (full gates).
rigour hooks init # auto-detect tool, install hooks + DLP
rigour hooks init --tool all # all tools at once
rigour hooks init --block # exit code 2 on failures (strict mode)
rigour hooks init --no-dlp # skip DLP hooks
rigour hooks check --files src/a.ts # manual fast check
Supported tools: Claude Code, Cursor, Cline, Windsurf — each with quality (post-write) and DLP (pre-write) hooks.
Five-signal extraction → LLM interpretation → deterministic verification pipeline.
rigour check --deep # Local sidecar (Qwen2.5-Coder-0.5B, any CPU)
rigour check --deep --pro # Full model (Qwen2.5-Coder-1.5B)
rigour check --deep --provider claude -k sk-ant-xxx # Cloud BYOK
Hallucinated import detection with stdlib whitelists and dependency manifest parsing:
JS/TS (Node.js builtins, package.json) · Python (160+ stdlib, local modules) · Go (150+ stdlib, go.mod) · Ruby (80+ stdlib, Gemfile) · C#/.NET (.NET 8 namespaces, .csproj) · Rust (std/core/alloc, Cargo.toml) · Java (java/javax/jakarta, build.gradle/pom.xml) · Kotlin (kotlin/kotlinx + Java interop, build.gradle.kts)
| Command | Purpose |
|---|---|
rigour scan | Zero-config stack-aware scan (auto-detect) |
rigour scan --deep | Zero-config + local LLM deep analysis |
rigour init | Setup config, hooks, DLP, governance |
rigour check | Full repository quality gates |
rigour check --ci | CI mode with minimal output |
rigour check --deep | + local LLM analysis |
rigour hooks init | Install real-time hooks for detected tools |
rigour hooks check --files ... | Fast hook gates on specific files |
rigour explain | Detailed explanation of failures |
rigour run | Supervisor loop for agent refinement |
rigour run --supervised | Full supervisor mode (iterative command + gate loop) |
rigour studio | Dashboard for monitoring |
rigour brain | Local memory status (SQLite) |
rigour brain --compact | Prune old findings, reclaim disk |
rigour doctor | Diagnose install + deep readiness |
rigour export-audit | Export compliance audit report (JSON/Markdown) |
rigour demo | Live demo on synthetic or real repos |
rigour settings | Manage API keys and provider config |
rigour run -- claude "..." + real-time hooks.cursor/hooks.json.clinerules/hooks/ scripts.windsurf/hooks.jsonrigour_check, rigour_explain)npx @rigour-labs/cli check --ci| Quick Links | |
|---|---|
| Getting Started | Install and run in 60 seconds |
| CLI Reference | All commands and options |
| Configuration | Customize quality gates |
| MCP Integration | AI agent setup |
- run: npx @rigour-labs/cli check --ci
MIT © Rigour Labs
FAQs
AI-native quality gates with local LLM analysis. Forces AI agents (Claude, Cursor, Copilot, Cline, Windsurf) to meet engineering standards. Bayesian Brain learns your codebase. Zero config: npx rigour-scan.
The npm package @rigour-labs/cli receives a total of 480 weekly downloads. As such, @rigour-labs/cli popularity was classified as not popular.
We found that @rigour-labs/cli demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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