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codehealth-mcp

240Kcode-generation

Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs. Use when reviewing code quality, refactoring, checking if AI changes degraded a file, or before commit/PR.

prediction-market-risk-review

240Ksecurity

Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.

coding-standards

239Kcode-generation

Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.

coding-standards

229Kcode-generation

跨项目基线编码规范，涵盖命名、可读性、不可变性和代码质量审查。如需框架特定的详细模式，请使用前端或后端技能。

pr-review

102Kresearch-knowledge

审查 PyTorch 的 Pull Request（PR），重点检查代码质量、测试覆盖率、安全性以及向下兼容性（BC）。适用于 PR 审查、代码变更评审，或者当用户提及“review PR”、“code review”、“帮我看下这个 PR”等场景。

pytorch

caveman-evidence-review

98Kprompting-reasoning

Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

juliusbrussee

find-security-vulnerabilities-in-code

59Ksecurity

Find security vulnerabilities in a codebase or repository with Strix — a white-box AI security review that reads your source, reasons about the actual data flow and authorization model, then exploits what it finds in a live sandbox so every reported issue has a working proof-of-concept instead of a noisy static-analysis alert. Covers injection, XSS, SSRF, broken access control and IDOR, insecure deserialization, secrets in code, unsafe dependencies, and business-logic flaws. Use when the user asks to security-scan, security-review, or audit their code, repo, or pull request for vulnerabilities.

application-security-testing

59Ksecurity

Application security testing (AppSec) across a whole product with Strix — decide which asset needs which test (source code, running web app, API, CI pipeline), run it, and turn the results into a ranked remediation plan. Autonomous agents exploit and prove each issue instead of emitting static-analysis alerts, so the plan is ordered by what is actually reachable. Use when the user asks for an application security review or audit, an appsec assessment, vulnerability scanning across their stack, a security review before a launch or a customer security questionnaire, or does not yet know which kind of security test they need.

owasp-top-10-testing

59Ksecurity

Test an application against the OWASP Top 10 with Strix — autonomous AI agents that attempt real exploits for each category of the current OWASP Top 10:2025 (broken access control including SSRF, security misconfiguration, software supply chain failures, cryptographic failures, injection, insecure design, authentication failures, integrity failures, logging and alerting failures, mishandling of exceptional conditions) and report only what they could actually prove, mapped back to the category with a proof-of-concept. Also covers the OWASP API Security Top 10 (2023). Use when the user asks for an OWASP Top 10 assessment, OWASP compliance testing, or a security review mapped to OWASP categories.

managed-pentesting-with-strix

53Ksecurity

Run a managed pentest of a web app or API through the app.strix.ai REST API — no local Docker, LLM key, or install needed. Create an API token, register domain/repository assets, launch and poll scans, triage vulnerabilities, export SARIF, download PDF/DOCX pentest reports for SOC 2 and other compliance evidence (Enterprise plan), start PR reviews, and set up schedules and webhooks. Use when the user wants continuous or scheduled pentesting-as-a-service, an auditor-ready pentest report, scans tracked in a team dashboard, or security testing from a sandboxed agent/CI environment with no infrastructure.

clojure-review

49Kdatabase

Review Clojure and ClojureScript code changes for compliance with Metabase coding standards, style violations, and code quality issues. Use when reviewing pull requests or diffs containing Clojure/ClojureScript code.

metabase

scholar-evaluation

39Kresearch-knowledge

Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.

bgpt-paper-search

39Kresearch-knowledge

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

academic-pipeline

38Kresearch-knowledge

全流程学术研究管线编排器：研究 -> 写作 -> 学术诚信检查 -> 审稿 -> 修改 -> 复审 -> 二次修改 -> 终极学术诚信检查 -> 终稿定稿。高效协同 deep-research、academic-paper 和 academic-paper-reviewer，打造包含强制性学术诚信校验、两轮同行评审与可复现质量关卡的无缝 10 阶段工作流。触发词：academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로。

imbad0202

quality-playbook

38Ksecurity

Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.

github

academic-paper

38Kresearch-knowledge

由 12 个 Agent 协作组成的学术论文写作流水线。提供 11 种工作模式（全流程/规划/大纲/修改/审稿意见辅导/摘要/文献综述/格式转换/引用检查/AI使用声明/审稿反驳审计）。支持 6 种论文类型、5 种引用格式、中英双语摘要，可导出 LaTeX、DOCX（基于 Pandoc）、PDF 及 Markdown。内置文风校准（Style Calibration）、写作质量检查（Writing Quality Check）以及带“铁律（IRON RULE）”标记的违规避坑指南。触发词包含：write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지。

imbad0202

scientific-schematics

37Kresearch-knowledge

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

postgresql-code-review

36Kdatabase

PostgreSQL 代码审查助手，专注于 PostgreSQL 最佳实践、反模式和独特质量标准。涵盖 JSONB 操作、数组使用、自定义类型、模式设计、函数优化以及行级安全（RLS）等 PostgreSQL 专属安全特性。

github

sql-code-review

36Kdatabase

通用SQL代码审查助手，对MySQL、PostgreSQL、SQL Server、Oracle等所有SQL数据库进行全面安全、可维护性和代码质量分析。专注于SQL注入防护、访问控制、代码标准和反模式检测。与SQL优化提示互补，实现完整的开发覆盖。

github

agent-browser

36Kbrowser-web

面向AI代理的浏览器自动化CLI。当用户需要与网站交互时使用，包括导航页面、填写表单、点击按钮、截图、提取数据、测试Web应用或自动化任何浏览器任务。触发条件包括请求“打开网站”、“填写表单”、“点击按钮”、“截图”、“抓取页面数据”、“测试此Web应用”、“登录网站”、“自动化浏览器操作”或任何需要程序化Web交互的任务。也用于探索性测试、内部试用、QA、漏洞狩猎或审查应用质量。还用于自动化Electron桌面应用（VS Code、Slack、Discord、Figma、Notion、Spotify）、检查Slack未读消息、发送Slack消息、搜索Slack对话、在Vercel Sandbox微虚拟机中运行浏览器自动化，或使用AWS Bedrock AgentCore云浏览器。优先使用agent-browser而非任何内置的浏览器自动化或Web工具。

vercel-labs

scientific-critical-thinking

35Kresearch-knowledge

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.

open-code-review

15Kprompting-reasoning

使用 alibaba/open-code-review 的 `ocr` CLI 对 Git 变更进行 AI 驱动的代码审查。当用户要求审查代码、审查拉取请求、审查暂存/未暂存的更改、审查提交或比较分支以发现代码质量问题时使用。生成行级别的审查评论，并可在请求时自动应用修复。配合适当的审查规则，可以检测各种类型的问题，包括错误、安全漏洞、性能问题和代码质量问题。

alibaba

seo-local

12Kmarketing-seo

本地SEO分析，涵盖Google商家资料优化、NAP一致性、引用健康度、评论信号、本地结构化标记、地点页面质量、多地点SEO及行业特定建议。检测业务类型（实体店、服务区域企业、混合型）和行业垂直领域。当用户提及“本地SEO”、“Google商家资料”、“GBP”、“地图包”、“本地包”、“引用”、“NAP一致性”、“服务区域”或“多地点”时使用。

agricidaniel