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Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

日本語の概要は準備中です。原文の説明を表示しています。

Review homework notebooks for completeness, reproducibility, safe execution, and academic-integrity boundaries.

日本語の概要は準備中です。原文の説明を表示しています。

Review final-assignment materials for requirements, citations, AI-use disclosure, and data-analysis completeness.

日本語の概要は準備中です。原文の説明を表示しています。

Design and implement context-fit frontend interfaces with deliberate art direction, accessible interaction, responsive behavior, complete states, and rendered verification. Use when creating or substantially reworking pages, components, product workspaces, dashboards, marketing sites, editorial surfaces, commerce flows, or justified immersive experiences.

日本語の概要は準備中です。原文の説明を表示しています。

Complete feature development with structured options for merge, PR, or cleanup

日本語の概要は準備中です。原文の説明を表示しています。

Validate documentation before merging - check completeness, broken links, code example accuracy, and factual correctness. Use when reviewing docs for quality gates or running pre-merge doc validation.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for optimizing MSBuild incremental builds. Only activate in MSBuild/.NET build context. USE FOR: builds slower than expected on subsequent runs, 'nothing changed but it rebuilds anyway', diagnosing why targets re-execute unnecessarily, fixing broken no-op builds. Covers 8 common causes: missing Inputs/Outputs on custom targets, volatile properties in output paths (timestamps/GUIDs), file writes outside tracked Outputs, missing FileWrites registration, glob changes, Visual Studio Fast Up-to-Date Check (FUTDC) issues. Key diagnostic: look for 'Building target completely' vs 'Skipping target' in binlog. DO NOT USE FOR: first-time build slowness (use build-perf-baseline), parallelism issues (use build-parallelism), evaluation-phase slowness (use eval-performance), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, search, target details); falls back to dotnet build /bl, binlog replay with diagnostic verbosity.

日本語の概要は準備中です。原文の説明を表示しています。

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

日本語の概要は準備中です。原文の説明を表示しています。

Design and implement a complete ML pipeline for: $ARGUMENTS

日本語の概要は準備中です。原文の説明を表示しています。

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task requires exhaustive unabridged output, complete files, or strict prevention of placeholders and skipped code.

日本語の概要は準備中です。原文の説明を表示しています。

Generate two complementary onboarding documents that together give any engineer — from newcomer to principal — a complete understanding of a codebase. Use when user asks for onboarding docs or getting-started guides, user runs /deep-wiki, or user wants to help new team members understand a codebase.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Complete toolkit for senior architect with modern tools and best practices.

日本語の概要は準備中です。原文の説明を表示しています。

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

日本語の概要は準備中です。原文の説明を表示しています。

Verifies bug fixes are correct and complete by tracing callers, checking imports/exports, validating path resolution, and confirming no regressions. Does the code work? Use when reviewing code changes for functional correctness.

日本語の概要は準備中です。原文の説明を表示しています。

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

日本語の概要は準備中です。原文の説明を表示しています。

Bring this machine to a complete, verified AOS installation (skills, MCP servers, gate hooks, memB, OpenWiki, Synapse). Generated from plugin-commands.json; invoke as $bdb-aos:setup.

日本語の概要は準備中です。原文の説明を表示しています。

Run or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

日本語の概要は準備中です。原文の説明を表示しています。

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

日本語の概要は準備中です。原文の説明を表示しています。

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

日本語の概要は準備中です。原文の説明を表示しています。

Set up a complete CI/CD pipeline for a project. Detects the stack, then generates and validates GitHub Actions workflows, a Dockerfile, and a Makefile as one coherent pipeline. Use when asked to "set up CI", "add pipeline", "create GitHub Actions", "ci/cd setup", "automate builds", "containerize", or "add a Makefile".

日本語の概要は準備中です。原文の説明を表示しています。