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KotlinのKtorでHTTPサーバーを作るための実装例をまとめたスキル。APIのルート、認証、JSON変換、部品の組み立て、通信とテストを整理します。

• KtorでREST APIを作りたいとき
• JWT認証でルートを保護したいとき
• Koinでアプリの部品を整理したいとき

営業・提携・資金調達の候補者を調べて優先順位を付け、共通の知人を通じた紹介経路を探し、相手の近況と送り手の文体に合わせてメールやSNSの連絡文を下書きします。

• 特定業界の見込み客を探したいとき
• 提携先や投資家候補の優先順位付け
• 共通の知人からの紹介経路の調査

SNS向けの正方形カード3枚を、見出しがつながるシリーズとして作成します。ブランド表記や連番をそろえ、映画のような色調を持つHTMLにまとめます。

• Instagramの3枚投稿を作りたいとき
• LinkedInの連続投稿用デザイン
• Xのスレッド用カード制作

市場・競合・消費者・財務などの調査について、章立てと必要データを設計し、収集済みの情報を図表・出典・戦略上の示唆を含むレポートにまとめるスキル。

• 調査の章立てと収集データを設計したいとき
• 市場・競合の調査結果を報告書にしたいとき
• 消費者理解やブランド戦略を整理したいとき

AIエンジニアリングの教材をターミナルで一節ずつ学び、質問やコード演習、確認クイズに取り組めます。学習記録に沿った再開や復習にも対応します。

• 学習記録から続きを学びたいとき
• コード演習で概念を理解したいとき
• レッスンクイズで理解度の確認

普段使っているChromeのログイン状態を使い、ページ操作やフォーム入力、操作のGIF記録を進めるために、タブ管理やサイト権限、エラー時の対応を定めるスキル。

• Chromeのログイン状態で作業したいとき
• ブラウザー操作をGIFで残したいとき
• ページのコンソールログ調査

音声付きの短い動画を文章や参照画像から作り、会話で変更点を伝えて編集します。物の追加・削除、照明や画面内の文字の変更、秒数に沿った演出の指定を支援します。

• 文章から音声付き短編動画を作りたいとき
• 参照画像で人物や商品の役割を指定したいとき
• 会話で物や照明、文字を調整したいとき

タンパク質に小分子が結合する位置と向きの候補を予測します。単一ペアや複数条件のドッキングを進め、信頼度による候補整理と構造の検証を支援するスキルです。

• タンパク質と小分子の結合姿勢予測
• 一括ドッキングの入力準備と検証
• 受容体の構造別に姿勢を比較したいとき

Detects unauthorized runtime drift in containers by monitoring binary execution, filesystem changes, and configuration deviation from the original immutable image, using Falco and Microsoft Defender for Containers. Use when validating immutable-infrastructure controls, hunting for unexpected package installs or binaries written inside a running container, or determining whether a container diverged from the image it was built from. Keywords: drift, immutable infrastructure, new binary executed, package install, image mismatch, Falco. Do not use for detecting breakout from the container to the host - use detecting-container-escape-attempts.

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 methodology for auditing and exploiting TLS/SSL implementations and misconfigurations across network services and mobile applications. Covers protocol downgrade attacks including POODLE (CVE-2014-3566) against SSLv3 CBC padding, DROWN (CVE-2016-0800) cross-protocol attack leveraging SSLv2 export ciphers to decrypt TLS sessions, and FREAK (CVE-2015-0204) forcing RSA export-grade key exchange. Addresses BEAST (CVE-2011-3389) exploiting CBC IV predictability in TLS 1.0, CRIME (CVE-2012-4929) and BREACH targeting TLS-level and HTTP-level compression oracles respectively, and Heartbleed (CVE-2014-0160) for OpenSSL memory disclosure. Covers certificate validation bypass techniques for applications with improper hostname verification or chain validation, certificate pinning bypass using Frida and Objection for mobile application interception, HSTS bypass via NTP manipulation and subdomain exploitation, TLS 1.3 0-RTT replay attacks against non-idempotent endpoints, mutual TLS (mTLS) authentication attacks including client certificate theft and relay, and Certificate Transparency log monitoring for reconnaissance. Primary tooling includes testssl.sh for comprehensive TLS auditing, sslyze for Python-integrated scanning, sslscan for quick cipher enumeration, and tlsx for high-speed TLS probing at scale. Maps to CWE-295 (Improper Certificate Validation), CWE-319 (Cleartext Transmission of Sensitive Information), and CWE-757 (Selection of Less-Secure Algorithm During Negotiation).

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。

LI.FI REST API for cross-chain and same-chain token swaps, bridging, DeFi deposits (Composer), yield discovery (Earn), and intent-based execution (Intents). USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Deposit into DeFi vaults/lending/staking in one click, including cross-chain (Composer: Aave, Morpho, Pendle, EtherFi, Yearn, etc.) - Discover yield opportunities, vault APY/TVL data, or track DeFi positions (Earn) - Execute gasless or intent-based transfers via a solver network (LI.FI Intents) - Move stablecoins cheaply with optimized defaults (stablecoin preset) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, bridges, or gas prices - Track status of a cross-chain transaction, recover from failed/partial transfers - Query transfer history/analytics or withdraw collected integrator fees - Build backends, bots, or AI agents (any language) that need cross-chain functionality

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。

Operate any Obsidian vault as a living, self-rewriting second brain (an evolution of Karpathy's LLM Wiki pattern: sources rewrite existing pages, contradictions reconcile automatically, scheduled agents maintain the vault while you sleep). Use this skill whenever the user asks Claude to read, write, update, search, or manage their Obsidian vault - including saving notes from conversation, creating daily entries, updating kanban boards, logging dev work, managing people notes, capturing decisions, tracking deals, or maintaining any vault structure. Also triggers when the user wants to bootstrap a new vault from scratch, run a vault health check, or drop a _CLAUDE.md into their vault so all Claude surfaces share the same operating rules. Includes a research toolkit (7 commands: /x-read, /x-pulse, /research, /research-deep, /notebooklm, /youtube, /podcast) for AI-powered research via Grok, Perplexity, NotebookLM, YouTube, and podcast feeds - findings save to the vault automatically following the AI-first vault rule. Use proactively whenever the conversation produces information worth preserving (decisions, people met, projects started, tasks completed, lessons learned, research findings).

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。

When the user wants to create LinkedIn post copy or optimize for LinkedIn. Also use when the user mentions "LinkedIn post," "LinkedIn article," "professional post," "post to LinkedIn," "LinkedIn content," "LinkedIn copy," "B2B LinkedIn," "LinkedIn engagement," "LinkedIn feed," "share box," "document post," "poll," "Newsletter," "reshare," or "LinkedIn marketing." For LinkedIn ads, use linkedin-ads.

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。

Build an influencer marketing strategy — finding influencers, briefing, contracts, deliverables, performance tracking, micro vs macro strategy, outreach, and FTC compliance. Use when the user says "influencer marketing", "influencer strategy", "work with influencers", "influencer outreach", "influencer brief", "find influencers", "micro influencer", "macro influencer", "nano influencer", "creator marketing", "brand ambassador", "influencer campaign", "influencer collaboration", "seed influencers", "gifting influencers", "paid influencer", "influencer ROI", or wants to use influencers or creators to grow the brand.

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。

Analyze site's internal link structure and optimize for hub-and-spoke SEO architecture. Triggers on: "optimize internal links", "internal linking", "link structure", "hub and spoke links", "orphan pages", "link equity", "internal link audit", "fix my internal links", "link architecture", "site structure optimization", "internal linking strategy", "link flow", "improve site structure", "find orphan pages", "maximize link equity", "internal link map".

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。

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-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。

Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-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。

Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

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 blog writing skill that handles technical blog posts, personal voice writing, brain dump transformation, and category-aware AEO-optimized content. Use when: (1) writing, editing, or proofreading a blog article or post, (2) transforming unstructured brain dumps into polished posts, (3) writing in specific personal voices (Jarad, Nick Nisi), (4) creating category-aware technology/company/product posts, (5) building tutorials, deep dives, postmortems, benchmarks, or architecture posts, (6) writing engineering blogs, dev blogs, programming blogs, coding tutorials, or documentation posts. Triggers: blog post, blog writing, technical blog, dev tutorial, brain dump, article, content writing, developer article, engineering blog, programming blog, coding tutorial, documentation post, technical writing, blog editing, proofreading, developer content

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。

Facilitate a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).

Use whenever the user wants to find, rank, or shortlist directories, listing sites, or launch platforms where they can submit a software product, SaaS, app, tool, or startup — to get backlinks, referral traffic, and launch-day reach. Triggers on "where can I submit my SaaS for launch", "list of Product Hunt alternatives", "directories to get backlinks for our app", "high-authority software listing sites", "where should I list my startup", or "pull submission details for these 8 directory domains", even when described indirectly (we're launching next week, where do we post; how do we get backlinks by listing in directories). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-mcp-directories for MCP-server listings and find-ai-directories for AI-tool / AI-agent / agent-skill listings. Skip finding a firm/agency to hire (use the find-* agency skills), finding products *inside* a directory ("recommend the best CRM"), building a directory site (do-the-work), local/business directories for brick-and-mortar listings, and link-building *services*.

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 structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.

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。

Configure Salesforce Billing (blng__ managed package) billing schedules, invoice plans, billing policies, and billing treatments on activated Orders. Triggers: 'billing schedule not generating invoices', 'blng__BillingSchedule__c missing after order activation', 'in-advance vs in-arrears billing', 'milestone billing', 'invoice run not picking up order products', 'evergreen billing setup', 'billing treatment configuration'. NOT for revenue schedules, GL transactions and ASC 606 - use admin/revenue-recognition-requirements. NOT for native Revenue Cloud (RLM) BillingSchedule objects - use data/revenue-cloud-data-model.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。