搜索「Data Analysis」的结果

io.github.ArchiveBox/ArchiveBox/archivebox

Use this when working on the ArchiveBox app, CLI, server, Docker image, Admin UI, REST API, data dirs, crawls, snapshots, and release/deploy scripts.

io.github.TryGhost/Ghost/convert-internal-package-to-typescript

Convert a legacy internal Ghost workspace package from JavaScript or CommonJS to the repository's TypeScript and ESM golden path while preserving file history and runtime compatibility. Use when modernizing an existing or newly migrated private package, including staged lib-to-src moves, JS-to-TS renames, consumer analysis, and package or archive verification.

io.github.affaan-m/ECC/ito-baskets

Read-only Itô basket and prediction-market data skill. Index the live basket catalog, compare a basket against user-supplied research or a watchlist, build a source-grounded market brief, or draft a non-executable planning worksheet. Use when a user asks to browse or index Itô baskets, compare a basket against notes or a thesis, research prediction-market events/venues/liquidity, or plan a basket or market idea without trading. Never advises, orders, trades, reserves, or executes.

io.github.PostHog/posthog/qa-team

Multi-agent QA review team for code changes. This skill should be used when the user asks to "review my code", "run QA", "qa-team", "review this branch", "code review", "check my changes", or wants a comprehensive multi-perspective code review of the current branch's changes. Spawns parallel specialist agents (security, database, reliability, compatibility, data integrity, performance, frontend, copy) that independently review the diff and produce a converged report. Also includes two generalist reviewers for convergence validation.

io.github.affaan-m/ECC/ml-adoption-playbook

End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.

io.github.affaan-m/ECC/pytorch-patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.

io.github.affaan-m/ECC/production-audit

Local-evidence production readiness audit for shipped apps, pre-launch reviews, post-merge checks, and "what breaks in prod?" questions without sending repo data to an external audit service. Use when auditing production readiness before launch, after a merge, or when asked what breaks in prod.

io.github.affaan-m/ECC/prediction-market-oracle-research

Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard.

io.github.affaan-m/ECC/redis-patterns

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications. Use when adding caching, a distributed lock, rate limiting, or pub/sub with Redis, or when key design needs review.

io.github.affaan-m/ECC/swift-actor-persistence

Thread-safe data persistence in Swift using actors — in-memory cache with file-backed storage, eliminating data races by design. Use when persisting data in Swift and a data race or thread-safety problem needs designing out.

io.github.affaan-m/ECC/clickhouse-io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads. Use when writing ClickHouse schemas or queries, or when an analytical query is too slow.

io.github.affaan-m/ECC/laravel-verification

Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness. Use when verifying a Laravel project before merge or deploy — lint, static analysis, tests, coverage, security.

io.github.affaan-m/ECC/quarkus-patterns

Quarkus 3.x LTS architecture patterns with Camel for messaging, RESTful API design, CDI services, data access with Panache, and async processing. Use for Java Quarkus backend work with event-driven architectures. Use when building or reviewing a Quarkus service, especially with Camel messaging or Panache data access.

io.github.affaan-m/ECC/springboot-patterns

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work. Use when building or reviewing a Spring Boot backend — REST layer, services, data access, caching, or async work.

io.github.affaan-m/ECC/generating-python-installer

Commercial-grade Python installer expert for Windows: Nuitka extreme compilation, dist slimming, DLL footprint analysis, and Inno Setup packaging to ship the smallest, fastest installers. Use when a Python app must ship as a minimal, fast-starting Windows installer; not for basic script-to-exe conversion. 中文触发：Nuitka 极限优化、Python 商业打包、极限编译 Python、dist 瘦身、DLL 分析、最小安装包、最快启动、商业级打包风格

io.github.affaan-m/ECC/backend-patterns

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.

io.github.affaan-m/ECC/backend-patterns

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.

io.github.PostHog/posthog/triaging-warehouse-sync-tickets

Debug a customer's data warehouse source, schema, or table from a support ticket, using PostHog's own production data. Use when a ticket says a warehouse table is stale, empty, stuck, duplicated, missing rows, or failing to sync, and you need the real state of the sync rather than the customer's description. The customer is on a team your MCP session cannot reach, so every answer comes from execute-sql with a connectionId set to a direct-connect source over PostHog's live production databases, which hold all customers' data. Covers region detection (US vs EU), the Production Postgres connection (externaldatasource / externaldataschema / externaldatajob / datawarehousetable), the Production ClickHouse connection (log_entries, app_metrics2), cross-region access through posthog-connection-call, why the external-data-* product tools silently answer from your own project instead, and how to end with one recommended action plus who can run it. Internal only: every query returns another customer's data.

io.github.affaan-m/ECC/quarkus-verification

Verification loop for Quarkus projects: build, static analysis, tests with coverage, security scans, native compilation, and diff review before release or PR.

io.github.affaan-m/ECC/quarkus-verification

Verification loop for Quarkus projects: build, static analysis, tests with coverage, security scans, native compilation, and diff review before release or PR.

io.github.upstash/context7/context7-mcp

Fetches current, version-specific library documentation and code examples through the Context7 MCP server. Use whenever the user asks about a library, framework, SDK, API, CLI tool, or cloud service, including API syntax, configuration, setup instructions, version migration, CLI usage, and library-specific debugging. Use when generating code that calls a third-party library, and when the user names a version such as Next.js 15 or React 19. Use even for well-known libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Django, and Spring Boot, because training data may not reflect recent changes. Prefer this over web search for library documentation. Do not use it for refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts, or when the user has already supplied the relevant documentation.

io.github.rohitg00/ai-engineering-from-scratch/migration-review-skill

Review database migration files when a change adds or modifies paths under migrations/. Use it before merge to collect forward, rollback, locking, and data-safety evidence.

io.github.zhaoxuya520/reverse-skill/ctf-sandbox-orchestrator

Default entrypoint and master ctf-sandbox-orchestrator workflow for CTF, exploit, reverse engineering, DFIR, pwnable, crypto, stego, mobile, AI-agent, cloud, container, Active Directory, Windows-host, and identity challenges. Use first when the user presents challenge infrastructure, binaries, prompts, hosts, or identities that should be treated as sandbox-internal by default and Codex needs to choose, route, and load the right downstream analysis path with concise evidence.

io.github.zhaoxuya520/reverse-skill/competition-android-hooking

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Android APK hooking, Frida tracing, request-signing recovery, SSL pinning bypass, JNI boundary inspection, and app trust-boundary analysis. Use when the user asks to hook an APK, inspect signer logic, trace Java or native boundaries, bypass pinning or root checks, inspect shared prefs or app databases, or replay accepted mobile requests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.