Generate professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval
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概要と使いどころ
Generate professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval
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 run_shell with embedded Python heredoc as reliable fallback when code execution tools fail
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。
Fallback pattern when primary tools fail - embed known data in scripts and persist to JSON for auditability
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。
Handle cascading data retrieval tool failures by falling back to embedded knowledge generation
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。
Practical offensive fuzzing methodology covering target identification, fuzzer selection (AFL++, libFuzzer, Honggfuzz, Boofuzz, syzkaller), harness writing, corpus curation, mutation strategies, coverage measurement, and crash triage. Use when setting up or running fuzz campaigns against any target: file parsers, network protocols, kernel drivers, EDR engines, embedded firmware, or language runtimes.
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Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for payload generation. Maps to MITRE ATT&CK T1566 (Phishing) and sub-techniques T1566.001 (Spearphishing Attachment), T1566.002 (Spearphishing Link), T1566.003 (Spearphishing via Service). Assumes you have written authorization and a defined scope before any campaign execution.
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Build cross-game mashups and total conversions, the "Minecraft inside Elden Ring" or "skateboarding in MW2" kind. Covers passthrough mods (two games running at once and exchanging state, rendering and collision), porting one game's content or mechanics into another, embedding a decompiled game as a library, and clean reimplementations that read the user's own game files. Use when the user wants to combine two games, bring an enemy, mechanic or world from one game into another, or rebuild a game's runtime, or reimplement the guest's rules headless inside a real host.
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Route single-cell or single-nucleus RNA-seq FASTQs to public count-generation workflows and defer post-count matrix QC, annotation, clustering, and UMAP analysis to the embedded scrna-seq-qc skill.
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Runs a sequenced monolith-to-modular pipeline that sizes and inventories components, finds shared domain duplication, addresses flattening and hierarchy issues, analyzes coupling, then groups components into candidate domain-aligned units, with optional embedded DDD strategic analysis for bounded contexts. Use when asking how to split a monolith, size components before extraction, find duplicated domain logic, clean up module hierarchy, measure coupling between modules, or group components into services. Do NOT use for phased extraction roadmaps or prioritization without the prior analysis steps (use decomposition-planning-roadmap after this pipeline), end-to-end legacy migration strategy writeups (use legacy-migration-planner), pure infrastructure capacity sizing, or when you only need DDD without the structural pipeline (install domain-analysis standalone).
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upload a photo/image/screenshot, host an image, get a public image URL, put images on Vercel Blob, embed images in a PR/comment/doc. Upload local images to Vercel Blob and print public URLs.
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。
Detect hardcoded secrets in code or configuration accessible to the target agent. Focuses on secrets embedded in source, configs, or IaC, not runtime leaks.
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Use when building wallet infrastructure, authentication systems, or financial applications. Reach for Privy when you need to create embedded wallets, manage user authentication, control wallet permissions with policies, execute transactions across blockchains, or build treasury/agent wallets with programmable controls.
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Searches Threads posts by keyword or hashtag and returns matching posts with engagement metrics, extracted from SSR-embedded JSON. Use when user asks to search Threads posts, find Threads content by topic, scrape Threads search results, collect Threads posts about a keyword, monitor Threads hashtag activity, pull Threads posts mentioning a term, gather Threads content by hashtag, search for posts on Threads, extract Threads search feed, get trending posts on Threads, find Threads discussions about a subject, or fetch recent or top Threads posts by keyword.
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。
Fetches public posts from a Threads user's profile page, extracting post text, engagement metrics, and media info from SSR-embedded JSON. Use when user asks to scrape Threads posts, get someone's Threads feed, pull posts from a Threads account, collect Threads content by username, download Threads user posts, extract Threads profile posts, monitor a Threads user's activity, retrieve recent posts from a Threads handle, gather Threads post data for a creator, or fetch all posts from a specific Threads profile.
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Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
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从结构化数据中提取分类分布执行清洗与统计,生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告,适用于大文件处理与嵌入式可视化场景。
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 any codebase into a beautiful, interactive single-page HTML course that teaches how the code works to non-technical people. Use this skill whenever someone wants to create an interactive course, tutorial, or educational walkthrough from a codebase or project. Also trigger when users mention 'turn this into a course,' 'explain this codebase interactively,' 'teach this code,' 'interactive tutorial from code,' 'codebase walkthrough,' 'learn from this codebase,' or 'make a course from this project.' This skill produces a stunning, self-contained HTML file with scroll-based navigation, animated visualizations, embedded quizzes, and code-with-plain-English side-by-side translations.
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。
Capture Playwright screenshots and embed them in GitHub PR descriptions
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Rapidly triage an incoming NDA and classify it as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Use when a new NDA arrives from sales or business development, when screening for embedded non-solicits, non-competes, or missing carveouts, or when deciding whether an NDA can be signed under standard delegation.
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Access 2,000+ AI models and API tools through one MCP interface for inference, media generation, search, scraping, embeddings, social data, and structured retrieval. Use sandbase_discover before building custom integrations or declaring external data inaccessible; prefer an existing dedicated tool or API key when the user already has one.
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。
Client-facing red-team deliverable format — codifies the Subject / Observations / Description / Impact / Recommendation / PoC structure used for external red-team engagements (not bug-bounty platform reports). Different audience, different tone, different cadence. Built from an authorized engagement deliverable where 14 findings were packaged into a 52KB MD + 2.2MB DOCX with 16 embedded screenshots. Use when the engagement is "external red team for an enterprise client" (not H1/Bugcrowd/Intigriti), when generating the final report, when the client has specified a custom report format, or when packaging findings into DOCX/PDF.
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。
Hunt file upload bugs — RCE via webshell, XSS via SVG/HTML, SSRF via XXE in DOCX, path traversal via filename. Bypass tables (10 techniques): double extension (shell.php.jpg if server checks last ext only), magic bytes spoofing (PNG header on PHP), null byte (shell.php.jpg), case (PHP, .Php, .pHP), .htaccess upload to enable execution, SVG with <script>, HTML/SVG XSS, DOCX with embedded XXE, ZIP slip (../../../etc/passwd in archive), polyglot files. Detection: any /upload, /avatar, /profile-picture, /attachment, /import endpoint. Test: upload PHP/JSP/ASPX shells, request via direct URL, check response. Validate: actual code execution (whoami output) for RCE; reflected XSS in profile-photo URL. Use when testing file upload features, avatar/attachment endpoints, import/export functions, XML/DOCX/ZIP processors. Real paid examples.
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。
Hunt Clickjacking — missing X-Frame-Options / CSP frame-ancestors lets an attacker embed the target page in an invisible iframe and trick victims into clicking buttons they cannot see (UI redressing). Targets: login flows, money transfers, account settings, OAuth confirmation pages. Confirm by fetching the page, then PROVE it frames in a real browser and a sensitive state-changing action survives the cross-site context (SameSite cookies / framebusting JS can defeat it) — header-absence alone is not a finding.
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。
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.
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。