ZtotheZ Design Engineering: An MCP-backed closed-loop architecture for product design, interface verification, automated repair, and strict UI quality gates.

Quality engineering for AI coding agents: a method with steps and human gates, measured by deterministic engines. Runs locally and sends no source code.

Automated Context Engineering MCP Server for creating high-quality Product Requirements Prompts (PRPs) through AI-assisted workflows

🚀 Kill the Junior AI Era. 🤖 Level up your AI code to Principal standards. No more sloppy lines or junior mistakes. Automated ESLint ✨ TypeScript 🔧 Prettier 🎨 SonarQube 🛡️ Security 🔒 Complexity 📊 in seconds. High-stakes quality, forced to ship only the best. ⚡🏆 Goodbye, bad code. Hello, Principal Engineer. 🚀✨

Agent-first SEO quality, intent and opportunity engine with CLI and optional MCP server.

PCP — the prompt quality engine. Deterministic scoring, compilation, cost estimation & governance for AI prompts. Zero LLM calls.

AI-Powered Full-Pipeline Music Composition Engine — Multi-Agent Lyric Writing → Sheet Music (PDF/MIDI/MusicXML) → Demo Audio (MP3) → Quality Eval → Publish. Zero GPU required. AI coding agent native. / AI 驱动全链路音乐创作引擎 — 多Agent作词→乐谱→Demo→评估→发布。零GPU。AI智能体原生。

MCP quality scanner and tool discovery for autonomous agents. Search 27,843+ indexed tools, check AEO (answer-engine-optimization) readiness scores, validate MCP server quality, find alternatives — the standard tool-validation layer before any agent calls

Bio-inspired semantic filtering engine. Ecosystem simulation for knowledge quality assessment.

Glyphic is an open source diagram engine that lets AI agents generate production-quality diagrams from structured JSON, without SVG, Mermaid, or a browser.

CLI and MCP servers for Cutline, including SlopBurn: a product quality engineering roguelike RPG for vibecoding workflows.

Deterministic SaaS accounting, SEC XBRL revenue quality and point-in-time FP&A backtesting: billing adapters, DuckDB engine, audited Excel models, Streamlit workbench and MCP server.

CLI and MCP servers for Cutline, including SlopBurn: a product quality engineering roguelike RPG for vibecoding workflows.

CLI and MCP servers for Cutline, including SlopBurn: a product quality engineering roguelike RPG for vibecoding workflows.

Provide specialized prompt engineering suggestions to enhance LLM-generated content for development and design tasks. Support backend, frontend, UI design, and general scenarios with tailored prompt completions. Easily integrate with MCP-compatible clients to improve prompt quality and relevance.

OptiForge (光匠) — AI-native optical design & lens engineering platform over the Zemax/OpticStudio ZOS-API. MCP server: 5-stage design workflow, executable quality gates, aberration & tolerance analysis, CAD/ISO 10110 deliverables.

Claude Code context manager + memory engine. Primary install is as a Claude Code plugin (see README). Also usable standalone as a zero-dep CLI: token monitoring against a model-aware quality ceiling, tailored /compact prompt + auto-clipboard, JSONL gzip b

Code-intelligence and evidence engine for coding agents: indexes symbols, call edges and config references, serves them over MCP and harness hooks, and proves plan claims with a verify gate. Extraction quality is measured against independent oracles. Local, pure Go.

Graph-Augmented Code Intelligence RAG engine pairing deterministic AST call graphs with dense vector search to boost direct-callee Recall@8 from 0.0% to 53.3% (+53.3 pp) under a 5.8ms p95 latency bound. Powered by a QLoRA fine-tuned Qwen2.5 adapter (70.02 CodeBLEU), quality verification harness, and production Model Context Protocol (MCP) server.

107 production-grade QA engineering skills for AI agents — frontend, API, performance, mobile, security, CI, and AI/LLM testing. MCP-ready: works with Claude Code, Claude Desktop, Cursor, Zed, Windsurf, and any MCP host.

Rules-pack-agnostic engineering practice audit tool: MCP server, inline trigger fragments, HTML audit reports

MCP server that generates quality control plans and inspection reports from technical-drawing PDFs — automatic extraction of tolerances, fits, threads, GD&T and surface finishes, balloon numbering, acceptance limits, ballooned drawing PDF and Excel measur

MCP server for runtime quality validation of AI agent outputs — hallucination detection, scope compliance, and output quality scoring

MCP server for auditing Git repositories against software engineering standards

Zero-setup data-quality checks on any file or warehouse via DuckDB

Read-only MCP server + Claude Code plugin for investigating data quality issues: metric drops, duplicates, joins, freshness, dbt test failures.

Multi-persona engineering mentor with 64 specialized AI personas orchestrating collaborative guidance, session memory, and git-aware context

A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.

High-throughput agent context router and deterministic static analysis engine.

A deterministic development harness for Claude Code — MCP workflow engine, enforcement hooks, YAML workflows, and multi-agent consensus (Claude + Codex + Antigravity/Gemini)

Find, explain, and safely fix bad data. Local-first data quality for files, databases, CI pipelines, and AI agents.

Open-source data quality monitoring: checks across warehouses, lakehouses and object stores, triggered by ADF, Airflow and dbt, with alerting and an MCP server.

Embedding / vector-store drift and poisoning audit

Auto Dataset Cards / datasheets with Croissant + provenance

QualityPilot: 基于 MCP + RAG 的智能测试自动化平台 — 测试用例生成、API/UI 自动执行、失败分析、Bug 报告

Synthetic test-data generator with referential integrity

Schema-change detector and data-contract tests

Deterministic semantic verification for agent-generated SQL.

Column-level lineage extracted from SQL and dbt

Fast CLI for profiling and cleaning huge CSV / Parquet files

PII discovery across warehouses and lakes (data-side scanner)

Groundrule: your engineering standards, taught to every coding agent and checked on every PR. CLI, checks, packs, MCP server and hooks.

Your engineering standards, taught to every coding agent and checked on every PR. Open-source CLI, checks, packs, MCP server and hooks.

Readiness system that lets AI agents build your BI pipeline but never approve it -- evidence-gated source profiling, medallion mappings, metric contracts, and Power BI delivery. Offline demo in 60 seconds: uvx --from seshat-bi seshat demo init

Messy retail data turned into trusted metrics with dbt, plus an AI analyst that answers from them (40/40)

Action guardrails for AI coding agents — native Claude Code enforcement, MCP integrations, and Git/CI checks.

AI context that survives the chat window. Persistent repo memory, impact analysis, and instant style audits in one command.

The Data Control Plane for AI Agents — acquire, validate, and land data over MCP with scoped keys, approval gates, and an audit log. Self-hosted, open source.

An AI tech lead in server form—this intelligent MCP agent validates your coding agent's strategy, analyzes impact, and catches critical issues before any code is written. Like having a senior engineer review every approach, ensuring thoughtful architecture and fewer regressions.

Evidence-bound remediation agent for DataHub-governed data products. Turns one schema-drift incident into a proof-carrying, verified dbt change set — with an MCP server and a DataHub Skill so an agent can drive it, but never approve it.

Open-source AI agent that patrols your data, fixes only what it can prove against the source, and sends the rest to a person with the evidence. Works with Snowflake, dbt and DuckDB, runs as a GitHub Action, and speaks MCP.

AI Engineering Governance Platform — Session memory, feature registry, code intelligence, and rule enforcement for AI coding assistants.

Test on tiny data before you run on giant data. MCP server + CLI that gives AI agents a dress rehearsal for SQL: cost preflight, scale lint with auto-fix, rehearse on 1% → 10%, predict 100%, run only within budget. DuckDB, Postgres, BigQuery, Snowflake. Local-first, MIT.

Code Guardian Studio – AI-powered code refactor engine for large repositories, built on Claude Code + MCP.

Declarative synthetic data for Python. State the facts a dataset must hold (monthly revenue, channel shares, balanced ledgers, foreign keys, state machines) and get multi-table data that holds them exactly, with a DuckDB certificate anyone can rerun. From YAML, SQL DDL or a sentence. CLI and MCP server.

AI-powered code refactor engine for large repositories, built on Claude Code + MCP.

AI-driven quality & governance MCP Server for dbt projects. Audit coverage, profile data, detect schema drift, and auto-generate documentation.

Brutally honest senior-engineer code reviews for Claude Code, Cursor & Windsurf - and your terminal. Scores, evidence-backed issues, usable fixes.

Bulk decisions for AI agents. Jev classifies files and logs; your agent reviews exceptions. Reproducible cost and quality benchmarks.

The reproducible data-cleaning engine for Python. Profile messy CSVs, detect issues, generate a YAML recipe (with or without an LLM), and replay it with zero AI calls.