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概要と使いどころ
This skill should be used when the user asks to "run pentest commands", "scan with nmap", "use metasploit exploits", "crack passwords with hydra or john", "scan web vulnerabilities with nikto", "enumerate networks", or needs essential penetration testing command references.
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。
This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuffing tests", "evaluate password policies", "test for session fixation", or "identify authentication bypass flaws". It provides comprehensive techniques for identifying authentication and session management weaknesses in web applications.
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。
Upload and onboard multiple on-chain wallets or exchange accounts into Tres Finance. Use this skill whenever the user wants to add wallets, onboard wallets, import wallets, upload wallet addresses, ingest wallets, or connect exchanges into Tres — whether from a file (CSV/Excel) or by typing them manually. This skill guides Claude through the full flow: wallet type selection → input collection → validation → preview with editable HTML table → exchange credential collection (if exchange) → user confirmation → batch creation via the TRES MCP API. Always trigger this skill for wallet onboarding, wallet data ingestion, or any request to "add wallets to Tres".
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。
This skill should be used when the user asks to "brainstorm research ideas", "use 5W1H framework", "identify research gaps", "conduct gap analysis", "start research project", "conduct literature review", "define research question", "select research method", "plan research", or mentions research project initiation phase. Provides comprehensive guidance for research startup workflow from idea generation to planning.
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。
Humanization Pipeline Orchestrator v3.1 - Multi-pass 4-layer transformation pipeline Orchestrates G5 (Auditor), G6 (Humanizer), F5 (Verifier) in sequential passes Enforces checkpoints between every pass with mandatory AskUserQuestion Supports conservative (L1-2), balanced (L1-3), balanced-fast (L1-3 merged), aggressive (L1-4) modes Rich Checkpoint v2.0: section-level scores, selective humanization, target auto-stop G5+F5 parallel execution, section-selective humanization Triggers: humanize, humanize my draft, humanize manuscript, make natural, remove AI patterns Korean triggers: 휴먼화, 자연스럽게, AI 패턴 제거
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。
This skill should be used when the user asks to co-author documentation, draft a proposal, write a technical spec, create a decision doc or RFC, or structure a substantial document through iterative collaboration and reader testing.
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。
VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities Use when: building theoretical foundations, designing conceptual models, deriving hypotheses, critiquing frameworks, visualizing models Triggers: theoretical framework, 이론적 프레임워크, conceptual model, 개념적 모형, hypothesis derivation, critique, devil's advocate, 반론, visualization, diagram
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 manuscript review covering argument structure, econometric specification, citation completeness, and potential referee objections. Make sure to use this skill whenever the user wants substantive academic feedback on a paper — not just surface edits. Triggers include: "review my paper", "give me feedback on this draft", "what would a referee say", "anticipate referee objections", "act as a referee", "check my identification strategy", "is my argument convincing", "review this manuscript", "critique my paper", "will this pass review", or any request for deep critique of academic writing beyond typos and grammar.
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。
Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.
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。
Interactive setup wizard that configures a new project for the social-science-research plugin. Asks the user questions about their field, institution, journals, datasets, key researchers, and R color palette, then writes the answers into references/domain-profile.md and CLAUDE.md. Make sure to use this skill first whenever a user is starting fresh or wants to configure the plugin. Triggers include: "set up my project", "configure the plugin", "run setup", "initialize this project", "I just installed the plugin", "set my field", "set my institution", "configure my domain profile", or any request to personalize the plugin for a specific research 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。
This skill covers formal identification arguments and proofs in structural and reduced-form econometrics. Use when the user needs to prove or formalize that a parameter is identified — including writing identification propositions, stating regularity conditions, deriving rank conditions, or showing observational equivalence fails. Triggers on "identification proof", "identification argument", "identify the parameter", "show identification", "identification condition", "exclusion restriction proof", "rank condition", "order condition", "identification strategy formal", "nonparametric identification", "parametric identification", "local identification", "global identification", "observational equivalence", "identification at infinity", "completeness condition", "regularity conditions", "Rothenberg", "proof of identification", "identification result", "identified parameter", "point identified", "set identified", "partial identification".
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。
This skill covers game-theoretic methods in structural econometrics and industrial organization. Use when the user is working with strategic interactions, equilibrium analysis, or game-theoretic structural models — including entry games, conduct testing, auction models with strategic bidding, bargaining, or matching markets. Triggers on "Nash equilibrium", "subgame perfect", "best response", "strategic interaction", "entry game", "conduct testing", "auction", "mechanism design", "matching market", "bargaining", "BNE", "Bayesian Nash", "static game", "dynamic game", "repeated game", "multiple equilibria", "equilibrium selection", "discrete game", "oligopoly", "game-theoretic", "player", "payoff", "strategy", "dominant strategy", "Bresnahan-Reiss", "Ciliberto-Tamer", "partial identification", "set identification", or markup test.
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。
This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.
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。
专业Marp演示文稿制作助手。支持完整工作流程：工作空间初始化、内容分析、slides制作、多维度审阅、中文语言规范审阅（中文演示文稿）、PNG转换检查、终稿确定。所有产出物集中管理在项目工作文件夹中。当用户提到"制作slides"、"做PPT"、"演示文稿"、"Marp"、"幻灯片"、"presentation"等关键词时自动启用。Professional Marp presentation assistant with complete workflow: workspace initialization, content analysis, slide creation, multi-dimensional review, Chinese language review (for Chinese presentations), PNG conversion check, and finalization. All outputs organized in project workspace.
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。
Proofread and verify academic papers in LaTeX. Runs 19 sequential checks covering titles, consistency, citations, formatting, theoretical tension in motivation, concise results reporting, cross-section repetition, em-dash usage, and auxiliary-text-to-footnote conversion. Trigger when user says "check paper" / "proofread" / "paper-checker" / "校对" / "核查论文".
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。
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
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。
Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.
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。
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact. Since 2026-08 it also carries a provenance layer (去水印): deterministic, CJK-safe cleaning of invisible-character carriers and file metadata (docx / png / jpg / svg / pdf, incl. C2PA), an honest account of Claude's statistical text watermark, and an author "ownership pass" — it never claims a text is watermark-free.
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。
Read when the user asks what OpenKnowledge is, wants to install it on a repository, wants to open or preview a single markdown file that is not part of an OpenKnowledge project, wants to share an OpenKnowledge project with collaborators, asks whether OpenKnowledge supports a particular capability, or asks how `ok init` / `ok cowork` / OK Desktop set up a project. Do NOT load to perform OpenKnowledge reads/writes — the runtime guidance for editing markdown inside an initialized OK project ships as a separate project-local skill installed into each detected agent's skills dir (for example `.claude/skills/open-knowledge/`) whenever `ok init` runs.
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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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。
Create a new skill (MCP tool) for the Unity Editor by writing a C# (.cs) file that Unity compiles into the project. After compilation the new tool becomes callable through MCP. The file must be a partial class decorated with [AiToolType], each tool method must be decorated with [AiTool], the class name should match the file name, all Unity API calls must run via com.IvanMurzak.ReflectorNet.Utils.MainThread.Instance.Run(), and the method should either return a structured data model (for parseable output) or void (for side-effect-only operations). See the body of this skill for a full sample and best-practice notes.
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。
Read a `.cs` script file and return its content as a string. Supports a 1-based `lineFrom`/`lineTo` slice for partial reads. Pair with 'script-update-or-create' to write back.
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。
Request user-supplied credentials securely into a dotenv file without exposing their values to the agent.
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。
Fire the moment the user proposes a build. Instantly surface the 1-3 consequential choices hidden in their idea. Can also be invoked with /before-building.
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。