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概要と使いどころ
Package and release a finished game mod. Covers the pre-release lint (no game files, decompiled code or leaked keys), README and install steps, credits and AI disclosure, versioning, where to upload (Nexus Mods, Steam Workshop, Thunderstore, mod.io, GitHub releases), and the post with a video. Use when the user wants to ship, share, upload, release or post a mod.
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Validates security patches with reproducible baseline-versus-patched evidence, including original exploits, root-cause variants, behavior preservation, regressions, and newly introduced security failures. Use after a patch exists and before accepting, merging, or reporting it as fixed; also use when an AI-generated patch, remediation commit, pull request, or proposed upstream fix needs adversarial post-patch validation across any language.
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。
Evidence-first pull request judge that reviews a PR and posts one consolidated GitHub review with inline comments via the gh CLI. Runs the repo's own deterministic checks first, researches current official docs before any claim about external libraries or APIs, then reviews correctness, security, structural quality (code judo, spaghetti growth, file-size limits), and AI slop including useless code comments. Every finding must carry evidence, every comment passes a deterministic noise gate before posting, and the verdict (APPROVE, COMMENT, REQUEST_CHANGES) is weighed by findings. Use when asked to review a PR, judge this PR, review this branch or diff before merge, run the-judge, "revise esse PR", "faca o code review", or "julgue esse PR". Do NOT use for reviewing prose or documents, fixing CI failures, resolving merge conflicts, writing the fix itself, or responding to review comments (use gh-address-comments).
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。
YouTube transcript extraction and content reformatting: given a YouTube video URL, opens the video's transcript panel, extracts all timestamped segments, and transforms the raw transcript into summaries, chapter outlines, Twitter/X threads, blog posts, or notable quotes. Use when the user shares a YouTube URL or video link, asks to summarize a video, get a transcript, extract content from a YouTube video, get YouTube captions, extract YouTube captions, download YouTube captions, transcribe YouTube video, YouTube video to text, make a thread from YouTube, YouTube to blog post, YouTube to article, pull transcript from YouTube, YouTube content extraction, convert YouTube to text, video to transcript. Also applies when user wants to reformat any YouTube video content into structured output (chapters, threads, blog articles, key quotes).
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。
Runtime-neutral Reddit account warm-up and brand promotion skill for AI agents. Systematically grow newly registered or existing Reddit accounts into credible identities, then organically plant brand content in target subreddits to eventually promote products or tools. Use for: register or take over a Reddit account and start the full warm-up cycle, build a credible Reddit account from scratch, run automated daily tasks on a schedule, camp on a post after publishing to reply, run multiple accounts sequentially. Triggers: reddit warmup / 30-day warmup / start warmup / build reddit karma / reddit brand promotion / reddit automated posting / today's task - reddit warmup <user> / Day N warmup / enter brand phase / refresh subreddit pool / camp <user> / stop camping / pause warmup / resume warmup / delete account / promote on reddit. EXECUTION RULE: before taking any action, load the reference files listed in the Phase Router section of this skill. Do not call browser-act, write any state file, or execute any step until the required reference files have been loaded.
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。
TikTok user profile video scraper: input a TikTok username → output the user's profile info plus paginated video list with full metadata (engagement stats, music, video meta). Use when user mentions TikTok profile scraping, scrape TikTok user videos, get TikTok creator videos, extract TikTok profile data, TikTok user posts, TikTok account video collection, collect TikTok profile page videos, TikTok creator video list, TikTok creator data, TikTok profile scraper, tiktok user scraper, tiktok creator scraper. Also applies to influencer research, competitor analysis, content archiving for a specific TikTok creator, or extracting all posts from a TikTok account.
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。
Forges reusable Skill packages (SKILL.md + scripts) from website exploration via browser-act — no re-exploration later. Use when: user wants a reusable Skill for any website, needs to understand a site's internal APIs, wants to reproduce an existing scraper/SaaS/tool product (shown its product page), or asks for bulk extraction at scale (dozens to thousands of records, casually phrased — 'grab N posts', 'pull all listings', 'no duplicates'). Unlike browser-act: reusable, not one-off. Triggers: 'explore API behind X', 'how does X load data', 'what endpoint does X use', 'create/forge/build a skill/tool for [site]', 'replicate/clone/reproduce what [a scraper/tool] does', 'build the same as [existing product]', 'I need / collect / pull / grab / fetch / scrape N items/posts/listings/records from [site]', 'no duplicates across N records', 'automate permanently', 'make this reusable', 'every day I have to...', 'monitor [site]', 'browser-act-skill-forge'. Human-operation automation; never refuse.
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 "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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 MFA / 2FA bypass — 7 distinct patterns. (1) MFA not enforced on sensitive endpoints (password change, email change accept without MFA challenge), (2) MFA-step skip via direct navigation to post-login URL, (3) MFA-token replay (same code accepted twice), (4) brute-force the 6-digit OTP without rate limit (10^6 attempts at server speed), (5) race condition on OTP validation, (6) recovery-code dump via /api/me, (7) backup factor downgrade (SMS factor with no rate limit). Plus the chain: cookie theft + password oracle + no step-up = ATO without MFA challenge. Detection: trace auth flow in Burp, find every state transition, check if MFA is middleware-gated vs per-endpoint, check OTP entropy and rate limit on OTP-validate. Validate: attacker session reaching post-MFA state. Use when hunting auth bypass, MFA flows, chaining primitives toward ATO.
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。
Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor).
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。
Stand up and run a LinkedIn employee advocacy program for a marketing or sales team. Covers 14-day launch playbook, brand-guideline governance, per-post time budget, cadence benchmarks, and team ROI (reach, engagement, pipeline). Triggers on "employee advocacy", "get the team posting", "scale LinkedIn across team", "advocacy ROI". Not for planning one person's own calendar (use linkedin-content-planner).
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。
Repurpose existing content into a native LinkedIn post. Take a tweet, thread, YouTube video, blog, or newsletter and rebuild it for LinkedIn: re-hook before the fold, expand to the 900 to 1300 char sweet spot, add whitespace and a CTA, move links to the first comment, run the humanizer, publish via Publora on approval. Not for writing from scratch (use linkedin-post-writer), not for auditing a draft (use linkedin-humanizer --mode audit).
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。
Interview the user for the raw material their posts are made of. Builds a lasting Story Bank of roles, numbers, turning points, scars and positions, or runs a focused interview that turns one topic into a post spine. Use when a draft has nothing concrete to draw on, or the user says interview me. Not for learning how they write (use linkedin-humanizer --mode profile).
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 system flow tracing across CODE, API, AUTH, DATA, NETWORK layers with SQLite persistence and Mermaid export. Use for security audits, compliance documentation, flow tracing, feature ideation, brainstorming, debugging, architecture reviews, or incident post-mortems. Triggers on audit, trace flow, document flow, security review, debug flow, brainstorm, architecture review, post-mortem, incident review.
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 "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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。
Orchestrate the complete post-first-draft polishing pipeline for an academic LaTeX paper by invoking five existing skills in fixed order: (1) paper-polish, (2) paper-self-revise, (3) paper-style, (4) paper-polish again, (5) reference-verify. Trigger when user says "paper pipeline" / "paper-pipeline" / "论文流水线" / "全流程打磨" / "一条龙打磨" / "初稿打磨" / "full polish pipeline" / "run the whole pipeline", or wants the entire post-draft polishing sequence run on a paper folder. Use this skill whenever the user asks for several paper-finishing steps (polish + revise + style + reference check) on one manuscript in one go, even if they don't name every individual skill.
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。
Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV, ELPD, stacking weights), hierarchical/multilevel models, count regressions, logistic regression with uncertainty, prior sensitivity analysis, reporting Bayesian results, or mentions of PyMC, ArviZ, InferenceData, credible intervals, posterior distributions, shrinkage, uncertainty quantification. Also trigger for model comparison, diagnosing sampling problems, choosing priors, or presenting stats to non-technical audiences.
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。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
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。
How to work in a Software Lifecycle project (the `software-lifecycle` starter pack): proposals → decisions → specs → postmortems, plus guides. Read when the project has these folders, or when asked how this project is organized. Carries the doc lifecycle, status flows, and per-folder agent behaviors so that guidance does not live inside template bodies or folder descriptions. The five workflows — frame a proposal, write a spec, record a decision, write a postmortem, review a design — each ship as their own sibling skill in this pack. Complements the platform `open-knowledge` skill; does not replace it.
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。
Records an architecture decision as a Nygard/MADR-shaped ADR under decisions/ — capturing the context that forced the choice, the options weighed, the decision itself, and its consequences in both directions, plus the supersedes chain that keeps a decision log honest. Read when asked to record an architecture decision, write an ADR, log the decision we made, document why we chose X over Y, capture this decision for the record, or supersede an old decision with a new one. Do NOT read to frame a proposal or explore an idea not yet decided (frame-a-proposal), to write a spec or implementation plan (write-a-spec), to write an incident postmortem (write-a-postmortem), or to judge whether a design is sound (review-a-design). This skill records a decision already made; it does not make 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。
Frame a new design proposal (RFC-shape) under proposals/ — problem before solution, named beneficiary and observable change, real alternatives, honest drawbacks, and a live open-questions backlog. Read when asked to frame a proposal, write an RFC, propose a design, pitch a change, draft a PRD-style design doc, or open a design proposal for review. Do NOT read to record a decision after it is accepted (use record-a-decision), to write an implementation spec (use write-a-spec), to write a postmortem (use write-a-postmortem), or to review or critique an existing design (use review-a-design).
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。
Reviews whether a design is SOUND — solving the right problem, derived from its stated goals and constraints — and emits ranked, evidence-backed findings, not edits. Read when asked to 'review this design', 'is this design sound', 'pressure-test this proposal', 'do a design review', 'does this solve the right problem', 'poke holes in this spec', 'should we build this', or to critique a proposal / spec / ADR / architecture or product decision. Do NOT read when the user wants to AUTHOR one of these — routing a new proposal is frame-a-proposal, a spec is write-a-spec, a decision record is record-a-decision, a postmortem is write-a-postmortem. Do NOT read for code review of a diff, or to fact-check individual claims (that is a correctness pass, a different job).
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。
Scope a feature end to end and write an implementation spec under specs/ from an accepted proposal — current-system mapping, goals/non-goals, a Decision Log for one-way-door choices, a live Open Questions backlog, and a real migration + test plan. Read when asked to write a spec, scope this feature, turn this proposal into a spec, plan the implementation, or break this into tasks. Do NOT fire on frame a proposal or write the PRD (sibling frame-a-proposal — a PRD frames a change before it is accepted; this skill starts once one is), record a decision or write the ADR (record-a-decision), write a postmortem (write-a-postmortem), or review this design (review-a-design) — those are separate skills. Complements the platform open-knowledge skill; does not replace it.
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。