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When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.

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Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정, revisar artículo, revisión entre pares, revisión de manuscrito, informe de árbitro, revisa mi artículo, criticar artículo, simular revisión, revisión editorial, calibrar revisor.

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Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.

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Performs genomic interval overlap, nearest, merge, coverage, complement and subtraction on Polars DataFrames, and reads or writes BED, VCF, BCF, BAM, CRAM, GFF, GTF, FASTA and FASTQ data. Use for coordinate-aware genomic joins, read-depth analysis, lazy bioinformatics I/O, SQL queries or migration from bioframe.

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Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

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Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Covers the native 5.0 build and environment API, published 3.0.0 Gymnasium/PettingZoo adaptation, and a pinned historical 4.0 profile.

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Builds, registers, debugs, and operates bioinformatics workflows on Latch using the Python SDK, CLI, Latch Data and Registry, Nextflow, Snakemake, programmatic execution, and Latch MCP. Use when authoring or deploying Latch workflows, configuring resources or interfaces, moving data, integrating Registry, or launching and monitoring runs.

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Designs experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.

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Provides a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with BioServices.

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。

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

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 user needs lawful academic full text, CNKI institutional access, English OA retrieval, publisher API access, institutional browser fallback, or supporting information downloads.

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Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.

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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.

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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.

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。

Snapshot of a single wallet's token holdings on a specific chain — list of every token currently held with name, symbol, current price, 24h price change, and holding quantity. Use when the user provides an explicit wallet address (or says "my wallet") and wants the current portfolio: "what does 0x... hold", "wallet balance breakdown", "list positions for this address", "what tokens are in this wallet", "show me the holdings of <address>".

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。

Decides what information belongs on a resume or CV, what to leave off and in what order, and audits an existing resume against those norms. Covers every section (contact details, photo, summary, experience, education and GPA, skills, languages, references), candidate situations (new grad, career change, gaps, layoffs, veterans, academic, visa and more) and country and industry norms. Use when the user asks "what should I put on my resume", "what should I leave off", "should I include my photo / GPA / date of birth", "how do I show a gap", "summary or objective?", "how long should my CV be", "CV for a job in Dubai / Germany", "I'm moving to another country, what should I change", "what to put on my resume as a veteran" or "audit / review my resume". Works with pasted text or the Reactive Resume MCP server. For rewriting bullets use resume-bullet-writer; for fitting a resume to one job posting use resume-tailor; for building a new resume as JSON use resume-builder.

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。

Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include "check my Slack", "what channels have unreads", "send a message to", "search Slack for", "extract from Slack", "find who said", or any task requiring programmatic Slack interaction.

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。

.NET / C# 二进制逆向。当目标是 .NET assembly（PE 头含 CLR、.exe/.dll 托管程序）、C# 编译产物（含 NativeAOT）、红队 Sharp* 工具（Rubeus / SharpHound / SharpHound 等）、.NET 混淆程序（ConfuserEx / SmartAssembly / Babel / Eazfuscator）、.NET loader / info-stealer / 套壳 malware 时使用。优先用 dnSpyEx + de4dot，需要 AI 直接操作时联动 dnSpy MCP。不用于纯 native 二进制（走 reverse-engineering / ida-reverse）。

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。

Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.

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Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.

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 selecting and placing approved supporting icons, images, SVGs, diagrams, or infographics in an editable PPTX deck.

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。

CommunityToolkit.Mvvm (the MVVM Toolkit) core: source generators ([ObservableProperty], [RelayCommand], [NotifyPropertyChangedFor], [NotifyCanExecuteChangedFor], [NotifyDataErrorInfo]), base classes (ObservableObject / ObservableValidator / ObservableRecipient), commands (RelayCommand / AsyncRelayCommand), and validation. Companion skills: mvvm-toolkit-messenger for pub/sub, mvvm-toolkit-di for Microsoft.Extensions.DependencyInjection wiring. Works across WPF, WinUI 3, MAUI, Uno, and Avalonia.

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。

Audits, writes, and refines maintained first-party source-code comments and declaration-level documentation across languages. Use when adding, editing, reviewing, cleaning up, or auditing comments, doc comments, docstrings, TODO, FIXME, NOTE, suppressions, or other source commentary, including while modifying code and deciding whether a comment is warranted at all. Defaults to no comment unless it preserves meaningful, non-obvious information that cannot reasonably be recovered from the code or nearby repository context.

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。

Enable code intelligence (go-to-definition, find-references, hover, type info) for any programming language by installing and configuring an LSP server for Copilot CLI. Detects the OS, installs the right server, and generates the JSON configuration (user-level or repo-level). Use when you need deeper code understanding and no LSP server is configured, or when the user asks to set up, install, or configure an LSP 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。