「across」の検索結果

Trend Analysis workflow skill. Use this skill when the user needs Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Universal Web Scraper workflow skill. Use this skill when the user needs AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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 Web Scraper workflow skill. Use this skill when the user needs AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Trend Analysis workflow skill. Use this skill when the user needs Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

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Apply semi-formal certificate reasoning to code analysis — patch verification, fault localization, patch equivalence. Use when reviewing patches, hunting bugs across scopes, comparing fixes, or when code reasoning requires tracing execution across files/modules. Triggers on code review, bug localization, patch comparison, name shadowing, scope analysis, regression checking.

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。

Manage git worktrees across any repository with the `gwm` Rust binary (CLI + ratatui TUI). Use when the user asks to create / list / remove / bootstrap / switch / link worktrees, run a command across worktrees (`gwm exec`) or reclaim build artifacts (`gwm clean`), materialise a PR into a worktree (`gwm review`), drive a multi-repo workspace (`--workspace`), run the JSON daemon / statusline (`gwm daemon`, `gwm statusline`), list or pin AI-agent sessions per worktree (`gwm agents`, Claude Code / Codex / opencode / Mistral Vibe detection), seed config from a preset (`gwm init --preset`), diagnose with `gwm doctor`, drive tmux/zellij, or wire `gcd` via `gwm shell-init`. Also covers multi-forge (#419): GitHub via `gh` **or** GitLab via `glab`, the `forge` / `[forge_hosts]` keys, and `gwm trust add`. Triggers on `gwm`, `gwq`, `git worktree`, `.gwm.toml`, `forge`, `forge_hosts`, `glab`, `gwm trust add`, GitLab MR, `gwm create`, `gwm list`, `gwm exec`, `gwm clean`, `gwm review`, `gwm daemon`, `gwm statusline`, `gwm agents`, `gwm note`, `gwm bootstrap`, `gwm doctor`, `gwm switch`, `gwm tmux`, `gwm zellij`, `gwm link`, `gwm status`, `gwm shell-init`, `gcd`, `feat/#`, `fix/#`, GitHub issue/PR linking on a worktree.

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A dated chronology of one topic across the user's recorded coding-agent sessions — the lessons the graph learned about it and the prompts that named it, oldest first. Use only when the user explicitly asks for the history or timeline of a topic across their sessions ("how did X evolve", "when did we first discuss X") or asks for the `cognee-timeline` skill by name. Not for questions about code or the current task.

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 dated chronology of one topic across the user's recorded coding-agent sessions — the lessons the graph learned about it and the prompts that named it, oldest first. Use only when the user explicitly asks for the history or timeline of a topic across their sessions ("how did X evolve", "when did we first discuss X") or invokes /cognee-timeline. Not for questions about code or the current task.

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。

Search and ask questions about coding agent session history across Claude Code, Codex, and Cursor. Use when asking what was worked on, what was tried before, how a problem was investigated across sessions, what happened recently, or any question about past agent sessions. Also use when the user references prior sessions, previous attempts, or past investigations — even without saying 'sessions' explicitly.

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Audit designs against 19 rules across Figma files and code (HTML/CSS/React/Vue/Tailwind). Auto-detects framework and design system (MUI, Chakra, shadcn/ui, Ant Design, Radix, Bootstrap). Runs code superpowers across all 19 categories: aria, focus, contrast, tokens, responsive, motion, forms, navigation, spacing, states, microcopy, elevation, iconography/SVG. Flags color blindness risk. Audits dark patterns, ethical design, Nielsen's heuristics. Outputs before/after diffs, developer handoff reports, wireframe-to-spec. English and Korean. Triggers on: check my design, review my UI, audit my layout, is this accessible, design review, color contrast, WCAG, a11y, pixel perfect, Figma audit, CSS check, dark patterns, is this GDPR compliant, is this manipulative, is my form accessible, is my dark mode correct, wireframe to spec, heuristic review, Nielsen audit, usability heuristics, 디자인 검토, 접근성 확인, 색상 대비, 다크 패턴, 와이어프레임 스펙.

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🇺🇸 Reddit Marketing & Growth Playbook 2026 — The complete Reddit operations SOP for AI/SaaS founders. Covers account warming (Karma 0→500 in 20 days), shadow ban prevention, multi-account matrix via fingerprint browsers, subreddit selection across 10K+ communities, content templates (90/10 rule), Product Hunt × Reddit flywheel, AMA strategy. Why this matters NOW: Reddit content is 40.11% of ChatGPT/Claude training data — the highest-weighted English UGC source. Battle-tested by AFFiNE Reddit campaigns generating 3-4万有效曝光 with 5-8% GitHub Star conversion, plus 7 case studies (Adobe 3x conversion, Base44 $80M AMA, SenseNova-U1 multi-sub launch, Starterstory 0→3K users). 🇨🇳 Reddit 运营增长实战手册 — AI/SaaS 出海 founder 的 Reddit 完整 SOP。覆盖账号体系(20天养号 Karma 0→500)、影子封禁防御、指纹浏览器矩阵、10万+ subreddits 筛选、内容模板(90/10 法则)、Product Hunt × Reddit 飞轮、AMA 打法。**为什么现在做:Reddit 内容占 ChatGPT/Claude 训练数据 40.11%,是最高权重的英文 UGC 来源**。基于 AFFiNE Reddit 实战 3-4 万曝光 + 5-8% Star 转化,含 7 个完整案例(Adobe 3x 转化、Base44 $80M AMA、SenseNova-U1 多社区联动、Starterstory 0→3K 用户冷启动)。 🇯🇵 Reddit マーケティング＆グロースプレイブック — AI/SaaS創業者向けの完全なReddit運用SOP。アカウント育成（20日でKarma 0→500）、シャドウBAN防止、フィンガープリントブラウザマトリックス、サブレディット選定、コンテンツテンプレート（90/10ルール）、Product Hunt × Redditフライホイール、AMA戦略をカバー。**重要な理由：Reddit コンテンツは ChatGPT/Claude のトレーニングデータの40.11%を占める英語 UGC 最大の情報源**。AFFiNE Redditキャンペーン3-4万インプレッション＋5-8% GitHubスター変換で実証。7つの実例（Adobe 3倍変換、Base44 $80M AMA、SenseNova-U1、Starterstory 0→3Kユーザー）。 🇰🇷 Reddit 마케팅 & 그로스 플레이북 — AI/SaaS 창업자를 위한 완전한 Reddit 운영 SOP. 계정 워밍업(20일 Karma 0→500), Shadow Ban 방지, 핑거프린트 브라우저 매트릭스, 서브레딧 선정, 콘텐츠 템플릿(90/10 룰), Product Hunt × Reddit 플라이휠, AMA 전략을 다룹니다. **지금 해야 하는 이유: Reddit 콘텐츠는 ChatGPT/Claude 학습 데이터의 40.11%를 차지하는 최대 영어 UGC 소스**. AFFiNE Reddit 캠페인 3-4만 노출 + 5-8% GitHub Star 전환으로 검증. 7가지 사례 연구(Adobe 3배 전환, Base44 $80M AMA, SenseNova-U1, Starterstory 0→3K 사용자). Triggers: "reddit marketing" | "reddit growth" | "reddit ads" | "reddit advertising" | "reddit strategy" | "reddit promotion" | "subreddit marketing" | "reddit playbook" | "reddit SOP" | "reddit shadow ban" | "shadow ban prevention" | "reddit karma" | "reddit AMA" | "reddit product launch" | "reddit AI training data" | "reddit GEO" | "AFFiNE reddit case" | "Base44 AMA" | "SenseNova Reddit" | "subreddit selection" | "r/SaaS posting" | "r/MachineLearning self-promotion" | "fingerprint browser matrix" | "AdsPower reddit" | "reddit account farming" | "reddit 90/10 rule" | "reddit content strategy" | "reddit indie founder" | "reddit cold start" | "Reddit 运营" | "Reddit 营销" | "Reddit 防封" | "Reddit 矩阵号" | "Reddit 出海" | "Reddit 引流" | "Reddit 冷启动" | "影子封禁" | "Reddit Karma" | "Reddit 养号" | "Reddit AMA" | "Reddit マーケティング" | "Reddit グロース" | "Reddit 마케팅" | "Reddit 그로스"

🇺🇸 Product Launch & Product Hunt #1 Playbook — Multi-channel launch timing and sequencing. T-14 warm-up → T-0 launch day → T+7 momentum. Hunter selection, maker comment templates, comment cadence, anti-shadowban rules. Distilled from 30+ Product Hunt #1 daily launches across 150+ AI startups. **Supports 3 product paths: developer/maker (PH+GitHub), B2C consumer (Reddit/communities/TikTok, skip PH), B2B (LinkedIn+media).** 🇨🇳 产品发布与 Product Hunt 夺冠手册 — 多渠道发布的时序与节奏。T-14 预热 → T-0 发布日 → T+7 后续。Hunter 选择、maker comment 模板、评论节奏、防 shadowban。提炼自 30+ 次 Product Hunt 日冠、150+ AI 创业公司实战。**支持三种产品路径：开发者/maker（PH+GitHub）、2C 消费者（社区种子/TikTok，跳过 PH）、B2B（LinkedIn+媒体）。** 🇯🇵 プロダクトローンチ＆Product Hunt 1位プレイブック — マルチチャネルのローンチ時系列。T-14準備→T-0当日→T+7モメンタム。ハンター選定、メーカーコメント、コメントリズム、シャドウバン回避。**3つの製品パス対応：開発者/maker（PH+GitHub）、2C消費者（コミュニティ種・TikTok、PHスキップ）、B2B（LinkedIn+メディア）。** 🇰🇷 제품 출시 & Product Hunt 1위 플레이북 — 멀티채널 출시 타이밍과 시퀀싱. T-14 워밍업 → T-0 출시일 → T+7 모멘텀. 헌터 선정, 메이커 코멘트, 댓글 리듬, 섀도밴 방지. **3가지 제품 경로 지원: 개발자/maker(PH+GitHub), 2C 소비자(커뮤니티 씨드/TikTok, PH 건너뜀), B2B(LinkedIn+미디어).** Triggers: "product launch" | "launch strategy" | "Product Hunt" | "PH launch" | "how to launch" | "go to market" | "GTM" | "launch day" | "find a hunter" | "maker comment" | "launch sequencing" | "发布策略" | "产品发布" | "发 PH" | "Product Hunt 准备" | "找 hunter" | "ローンチ" | "출시 전략"

Design or audit a feature store — serving, freshness, and sharing across models. Use when asked "do we need a feature store", "design a feature store", or "share features across models".

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Compare API performance across versions — regression detection and root cause analysis. Use when asked "did performance regress", "compare API latency across versions", or "why is this slower".

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Grammar and syntactic structures across human languages -- word order typology (SVO, SOV, VSO, VOS, OVS, OSV), morphological systems (isolating, agglutinative, fusional, polysynthetic), agreement patterns (gender, number, case, person), phrase structure, clause embedding, and cross-linguistic universals. Covers constituent analysis, dependency vs. constituency grammars, grammatical relations (subject, object, oblique), ergativity vs. accusativity, tense-aspect-mood systems, and how grammar encodes meaning differently across language families. Use when analyzing sentence structure, comparing grammar systems, teaching grammatical concepts, or understanding why a learner makes transfer errors from their L1.

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Execute large-scale automated code transformations safely and idempotently. Use when renaming symbols across a codebase, migrating API call-sites, enforcing new patterns at scale, or applying structural edits to many files at once. Triggers on: 'codemod', 'mass rename', 'migrate all usages', 'transform codebase', 'apply pattern at scale', AST-based refactoring, or any task requiring consistent edits across 10 or more files.

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Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

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Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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Run a marketing objective to completion over hours, days, or weeks — not a single deliverable. Takes a business outcome ("40 qualified demos a month from organic by Q4", "cut blended CAC below $90", "launch the product in September"), decomposes it into work items with declared ECO floors, executes them across context windows with resumable state, and stops only when an independent gate returns SHIPPED or CLOSED. Use when the request is an outcome rather than an artifact, when work must continue in the background or across sessions, or when the user says "goal", "run this until", "keep working on", "autonomous", "long-running", "over the next month", or "get us to <number>".

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Drive the ENTIRE tdmcp roadmap-to-1.0 to completion as a resumable, wave-by-wave campaign — Milestone 4 (generative-AI bridge wave), Milestone 5 (mixer scene arming), and the v1.0 consolidation gates G1–G6 — routing each class of work to the right existing sub-harness. Use whenever the user wants to "implement everything / all the milestones / the whole roadmap / finish the road to 1.0", run the next milestone, close the consolidation gates, or build a long autonomous campaign across M4/M5/G1–G6. ALSO use for every follow-up: continue/resume the roadmap campaign, run the next wave, re-run a failed wave, fold in QA results, check campaign status, re-prioritize, or scope to one milestone/gate. This is the CAMPAIGN layer ABOVE tdmcp-pipeline / tdmcp-feature-lead / tdmcp-backlog-campaign — it sequences across MULTIPLE sub-harnesses (tools, mixer, coverage, docs, bridge, recipes, submission), which the generic backlog-campaign does not. For a SINGLE feature use tdmcp-pipeline; for one tool-shaped backlog file use tdmcp-backlog-campaign. Simple questions can be answered directly.

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Produces structured markdown session continuation documents for multi-session workflows. Captures active work streams, decisions with rationale, knowledge-file deltas learned during the session, the exact files to load next conversation, ingested content, artifacts, and open items into an ingestion-optimized handoff. Carries pending KF updates and unresolved items forward across handoff chains so context never drops. Generates a closeout checklist and echoes the starter prompt to chat. Use when approaching context limits, wrapping up a session, or managing work across conversations. Trigger on: "create a handoff," "session closeout," "wrap up this session," "build session handoff," "BSH," "continue next session," "we're running out of context," "context is getting long." Do NOT use for Memory optimization, context budget analysis, project audits, deciding whether work is ready for autonomous/Claude Code execution (use rootnode-handoff-trigger-check), or summaries with no continuation intent.

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Guides selection of identity, reasoning, and output approaches for Claude prompts based on task characteristics. Trigger on: "help me choose an approach," "which approach fits this task," "recommend a prompt pattern," "compare reasoning methods," "map this task to the right approach," "what combination of approaches," "which identity fits," "which reasoning method." Also trigger on symptom-phrased: "my prompt feels generic," "I don't know which approach to use," "my output lacks domain depth." Covers decision-tree logic across 8 identity approaches, 18 reasoning variants, and 10 output formats. After selection, use the relevant catalog skill if available to retrieve full templates. Activate whenever approach-selection across multiple categories is the primary decision. Do NOT use when the user already names a specific approach to retrieve (use the relevant catalog skill directly if available) or for evaluating existing prompts (use rootnode-prompt-validation if available).

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Comprehensive audit of a user's entire Claude environment — combines Project audit (six-dimension Project Scorecard, seven anti-patterns) with Global audit (six-dimension Global Layer Scorecard) plus Cross-Layer Alignment Check across all nine layers and Evolutionary Recommendations across four pathways. Produces a unified action plan. Use when user says "full audit of everything," "audit my entire Claude setup," "full stack audit," "comprehensive review of my project and preferences," "check everything," or wants the complete health check of both a specific Project AND their global configuration. Also use when a project audit reveals cross-layer issues that require full-stack visibility. Do NOT use for auditing only a Project (use rootnode-project-audit if available) or only global layers (use rootnode-global-audit if available). Do NOT use for single prompt evaluation (use rootnode-prompt-validation if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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