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quality gate engine with local Bayesian learning. AST analysis, drift detection, Fix Packet generation, and agent self-healing across TypeScript, JavaScript, Python, Go, Ruby, and C#.","maintainers":[{"name":"erashu212","email":"ashu.singh212@gmail.com"},{"name":"riju_01","email":"iamriju01@gmail.com"},{"name":"gprakashdev","email":"gaurav.prakashcool72@gmail.com"},{"name":"hamza.sial1911","email":"hamza.sial1911@gmail.com"}],"readme":"# @rigour-labs/core\n\n[![npm version](https://img.shields.io/npm/v/@rigour-labs/core?color=cyan)](https://www.npmjs.com/package/@rigour-labs/core)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n**AI Agent Governance Engine — deterministic quality gates, drift detection, and LLM-powered deep analysis.**\n\nThe core library powering [Rigour](https://rigour.run) — 27+ quality gates, five-signal deep analysis pipeline, temporal drift engine, and AI agent DLP across TypeScript, JavaScript, Python, Go, Ruby, and C#/.NET.\n\n> This package is the engine. For the CLI, use [`@rigour-labs/cli`](https://www.npmjs.com/package/@rigour-labs/cli). For MCP integration, use [`@rigour-labs/mcp`](https://www.npmjs.com/package/@rigour-labs/mcp).\n\n## What's Inside\n\n### 27+ Deterministic Quality Gates\n\n**Structural:** File size, cyclomatic complexity, method count, parameter count, nesting depth, required docs, content hygiene.\n\n**Security:** Hardcoded secrets, SQL injection, XSS, command injection, path traversal, frontend secret exposure.\n\n**AI Drift Detection:**\n- **Three-pass duplication drift** — MD5 exact → AST Jaccard (tree-sitter) → semantic embedding (all-MiniLM-L6-v2, 384D cosine). Catches `.find()` vs `.filter()[0]` — same intent, different implementation.\n- **Hallucinated imports** — language-aware resolution for relative + package imports.\n- **Phantom APIs** — non-existent stdlib/framework methods the LLM invented.\n- **Style drift** — fingerprints naming, error handling, import style, quote preferences against project baseline.\n- **Logic drift** — tracks comparison operators (>= → >), branch counts, return statements per function across scans.\n- **Dependency bloat** — unused deps, heavy alternatives (moment→dayjs), duplicate purpose packages.\n- **Context-window artifacts**, inconsistent error handling, promise safety, deprecated APIs.\n\n**Agent Governance:** Multi-agent scope isolation, EWMA-based checkpoint supervision, context drift, retry loop breaker, memory & skills governance with DLP scanning.\n\n### Real-Time Hook Engine\n\nSub-200ms per-file-write checker with 5 fast gates (governance, hallucinated imports, promise safety, security patterns, file size). Generates native hook configs for Claude Code, Cursor, Cline, and Windsurf.\n\n### AI Agent DLP (Data Loss Prevention)\n\n29 credential patterns with anti-evasion hardening (unicode normalization, zero-width char removal, bidi control stripping, Shannon entropy detection >4.5 bits). Compliance-mapped to SOC2-CC6.1, HIPAA-164.312, PCI-DSS-3.4/3.5/6.5, OWASP-A2, CWE-798.\n\n### Five-Signal Deep Analysis Pipeline\n\nRigour's deep analysis is not a wrapper around a generic LLM. The model operates within a cage of deterministic facts:\n\n1. **Extract** — five independent signal streams (AST facts, semantic embeddings, style fingerprints, logic baselines, dependency graphs) computed deterministically before the LLM sees anything.\n2. **Interpret** — the model receives structured facts (not raw source), focuses on SOLID, design patterns, language idioms, architecture. Constrained input prevents hallucination.\n3. **Verify** — every LLM finding is cross-referenced against all five signal streams. Wrong line numbers, phantom patterns, non-existent functions → discarded. Only verified findings with confidence scores reach the report.\n\nBoth model tiers (lite sidecar + pro code-specialized) are fine-tuned via the [DriftBench RLAIF pipeline](https://github.com/rigour-labs/driftbench) where the five signal streams serve as the teacher signal.\n\n### Temporal Drift Engine (v5.1)\n\nCross-session trend analysis powered by EWMA and Z-score anomaly detection. Tracks three independent provenance streams (AI drift, structural, security) with separate trend directions. Reads from the SQLite brain for month-over-month analysis.\n\nKey capabilities: per-provenance EWMA streams (alpha=0.3), Z-score anomaly detection (|Z| > 2.0), monthly/weekly rollups, semantic duplicate tracking, style + logic baseline evolution, human-readable narrative generation.\n\n### Multi-Language Support\n\nHallucinated import detection supports 8 languages with stdlib whitelists and dependency manifest parsing: TypeScript, JavaScript, Python, Go, Ruby, C#/.NET, Rust, Java, and Kotlin. Core structural gates support all languages via AST analysis.\n\n### Two-Score System\n\nEvery failure carries a **provenance tag** (`ai-drift`, `traditional`, `security`, `governance`) and contributes to two sub-scores:\n\n- **AI Health Score** (0–100) — AI-specific failures\n- **Structural Score** (0–100) — Traditional code quality\n\n### Fix Packets (v2)\n\nMachine-readable JSON diagnostics with severity, provenance, file, line number, and step-by-step remediation instructions that AI agents can consume directly.\n\n## Usage\n\n```typescript\nimport { GateRunner } from '@rigour-labs/core';\n\nconst runner = new GateRunner(config);\nconst report = await runner.run(projectRoot);\n\nconsole.log(report.status);    // 'PASS' or 'FAIL'\nconsole.log(report.stats.score);     // 0-100\nconsole.log(report.failures);  // Failure[]\n```\n\n### With Deep Analysis\n\n```typescript\nimport { GateRunner } from '@rigour-labs/core';\n\nconst runner = new GateRunner(config);\nconst report = await runner.run(projectRoot, undefined, {\n  enabled: true,\n  pro: false,        // true for full-power model\n  provider: 'local', // or 'claude', 'openai', etc.\n});\n```\n\n## Documentation\n\n**[Full docs at docs.rigour.run](https://docs.rigour.run)**\n\n## License\n\nMIT © [Rigour Labs](https://github.com/rigour-labs)\n","readmeFilename":"README.md"}