151 件 ・ 関連度順
概要と使いどころ
When the user wants to write, publish, republish, or optimize posts on Medium.com (canonical tags, distribution, Medium SEO). Also use when the user mentions "Medium," "Medium article," "Medium story," "Medium publishing," "canonical Medium," or "publish on Medium." Not for general parasite SEO when Medium is not the target platform—use parasite-seo. For AI search visibility strategy, use generative-engine-optimization.
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。
Medium-native long-form research, author-assistance, writing, editing, review, topic discovery, publication matching, and packaging workflow. Use when creating, outlining, researching, revising, auditing, or packaging Medium stories, essays, tutorials, reviews, explainers, comparisons, case studies, publication submissions, Medium 长文, Medium 选题, Medium Topics, or Medium publishing packs that need original author perspective, narrative craft, relevant topics, responsible AI disclosure, and reader-centered distribution readiness.
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 choose or execute third-party platform SEO (high-authority sites for rankings or backlinks). Also use when the user mentions "parasite SEO," "parasitic SEO," "barnacle SEO," "hosted content," "third-party publishing," "Medium SEO," "Reddit SEO," "GitHub parasite SEO," "LinkedIn Pulse SEO," "high-authority platforms," "distributed authority," "borrow domain authority," or "rank without own website." For GitHub-specific playbooks, use github. For Medium.com posts, use medium-posts. For Grokipedia, use grokipedia-recommendations. For AI answer-engine visibility (not platform selection), use generative-engine-optimization.
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。
End-to-end content creation and publishing. Takes a topic (or generates one), drafts in the user's voice, gets approval via Telegram, and publishes to Medium via browser automation. Invoke with "publish a post about X", "write and publish to Medium", "content-publish", or when an ego-dispatched session needs to create and distribute 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。
Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.
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。
Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for 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。
Hunt Forgot Password / Account Recovery Authentication Flaws — 5 distinct patterns: (1) username enumeration via different responses for valid vs invalid email, (2) reset token exposed directly in the API response body, (3) reset token not invalidated after use (replay), (4) password reset link works from a different IP/browser (no binding), (5) no rate limit on the reset request endpoint. These are the standalone recovery-flow broken-auth primitives — distinct from reset-email host-header poisoning (hunt-host-header) and the full ATO chain (hunt-ato owns password-reset as an ATO path; prove the primitive here, chain it there). Detection: trace the full forgot-password flow from request to token to use; check response diffs between valid/invalid emails; test token replay after consumption. Medium to High (enumeration=Medium, token-reuse=High, account-takeover=Critical when chained to known-email).
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。
Write and distribute launch and publicity content across LinkedIn, X/Twitter, Instagram, Discord, Medium, Substack, and YouTube. Produces per-platform copy that does not read as AI-written (no em dashes, no "thrilled to announce", no engagement-bait), plus the exact image/video specs and character limits each platform wants. Use this skill whenever the user wants to announce, launch, publicize, promote, cross-post, or share anything - a product, a feature, an open-source release, a research paper, a blog post, a demo video, a milestone, a job opening, or a conference talk. Trigger it on phrases like "post this on LinkedIn", "write a launch tweet", "announce this", "share this in Discord", "turn this into a thread", "what should I post", "write a Medium article about", "help me with distribution", "draft a YouTube description", "make this sound less like AI", or "de-slop this post". Also use it when the user has just finished shipping something and asks how to get it in front of people.
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 optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-strategy.
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 use GitHub for SEO, parasite SEO, GEO, open source marketing, README optimization, or curated Awesome lists. Also use when the user mentions "GitHub," "GitHub SEO," "GitHub parasite SEO," "GitHub GEO," "awesome list," "GitHub README," "profile README," "pinned repositories," "Trending," "Explore," "repository name," "About section," "GitHub description," "GitHub topics," "Website field," "GitHub Pages," "github.io," "user site," "project site," "GitHub gist," "curated list," or "navigation list." Not for Medium or other non-GitHub platforms—use parasite-seo or medium-posts. For OSS business model, use open-source-strategy.
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。
Editorial / magazine / long-form / Medium / Substack / content-heavy UIs. Locked knobs: CRAFT=9, MOTION=4, DENSITY=3. Serif display + humanist body, wide reading column, drop caps, OpenType. Trigger on: editorial, magazine, long-form, blog, Medium-like, Substack-like.
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。
マーケティングコンテンツ制作スキル。X/Instagram投稿、Note/Medium記事、 バナー画像、動画スクリプト等を作成する。 「投稿作って」「バナー作成」「記事書いて」「コピー作成」等で発動。 product-context.mdを参照してブランド一貫性を保つ。
Create original short videos using the 手势边界换媒｜局部窗口、多形态验真与回归 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants a hand-defined local medium window, repeated geometry proof, strict clipping, and a controlled return with new subjects and surfaces.
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。
音声ファイルをテキストに文字起こしするスキル。mp3/wav/m4a/ogg/flac形式に対応。whisperベースのtranscriber_toolを使用し、tiny/base/small/medium/largeの5つのモデルから精度と速度のバランスを選択可能。長時間音声はバックグラウンド実行に対応。「文字起こしして」「音声をテキストに変換して」で使用。
微信公众号文章的选题、研究、写作、改稿、审稿与交付工作流。Use when creating, outlining, researching, rewriting, auditing, or packaging Chinese WeChat Official Account articles, 微信文章, 公众号推文, 公众号长文, 热点解读, 干货教程, 观点文, 故事文, 对比测评, 复盘文, 标题摘要, 配图方案, or 微信移动端排版稿. 通用网站 SEO 文章优先使用 writer；LinkedIn 长文优先使用 linkedin-writer；Medium 或英文叙事长文优先使用 medium-writer。
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。
A workflow skill that lets Codex keep coding for 60 hours on medium and large projects: Claude Code CLI plans through an interactive requirements questionnaire, PRD, specs, and roadmap; Codex implements verified vertical slices, with resumable Long-Run Execution for lengthy work; Trellis maintains project memory. Use when the user mentions 大项目工作流, Codex 连续开发 60 小时, Codex 长任务, long-running or resumable Codex implementation, large/medium project, new SaaS, multi-module system, requirements questionnaire, Claude Code CLI planning, Codex implementation handoff, Trellis bootstrap, non-system-drive project, or vertical slice. Skip for: single-file edits, casual Q&A, copy polish, throwaway prototypes, or tasks the user explicitly wants done fast without process.
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。
PR レビュー。PR 番号・URL・ブランチ名・Slack リンク等を添えて PR の「レビュー」を依頼された時に使用（ビルトインの /review ではなく本スキル）。Claude Code と外部 CLI のマルチモデルレビューで Critical/High/Medium の指摘を報告する。境界: ローカル未コミット変更は /code-review、自ブランチの提出前チェックは self-review、特定コメントへの対応は pr-comment。PR の状態確認・作成相談では使わない。
実装前に呼ぶ。High/Medium リスク変更のテストオラクル（期待値の根拠）とファルシフィケーション項目を定義する。
Webサービスを攻撃者目線でレビューし、実在する攻撃ベクターに基づいて脆弱性を発見・整理するスキル。 静的レビューと実証検証は別サブエージェントに委任し、メインセッションは両者の結果を 査読・比較して次アクションを判断する。Medium以上の実証済み所見、または実証未了でも 優先度が高い所見がある場合はメインセッションで対策プランの作成を促す。 Trigger: 脆弱性レビュー, セキュリティ診断, 攻撃者目線レビュー, ペネトレーション観点, /dev:attack-surface-review, attack surface review, pentest perspective, exploit path
音声ファイルをローカルで文字起こしし、テキストや翻訳字幕に出力するスキル。APIキー不要で、モデルの大きさを選び、処理速度と精度を調整できます。
Enterprise-review-grade threat model from `harness threat-model {path}`. Categorizes MCP-surface threats; emits `worst: 'clean'|'low'|'medium'|'high'` + per-threat findings. Pure-read.
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。
Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low|medium|high|xhigh|max: from few high-confidence findings up to many, some of them uncertain; ultra: deep multi-agent review in the cloud); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. Pass --max-findings <n> to report up to n findings, or --max-findings all for every finding. The choice stays until you pass --max-findings default. For ultra on a GitHub.com PR target, --post asks to post the finished review’s findings to the PR as a single comment from the user’s GitHub account (not a review; the launch dialog still confirms in interactive sessions, while non-interactive mode posts on the flag alone) and --no-post hides that option.
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 this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: "chart", "graph", "plot", "data viz", "visualization", "dashboard", "analytics", "visualize data", "categorical colors", "sequential / diverging palette", "stat tile", "sparkline", "heatmap", "legend", "axis", "tooltip", "chart colors", "color by series".
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。
Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.
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。