1,743 件 ・ 関連度順
概要と使いどころ
Rewrite Chinese AI or tech 文章、新闻素材、article titles, article drafts, and source material into 新智元-style headline options for Qiaomu workflows. Use this skill when the user asks for 新智元版标题, 新智元风格起标题, 微信公众号爆款标题改写, or to convert an article/title into a high-density AI media headline while preserving facts.
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。
Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs for Series A+ startups scaling internationally. Use when planning marketing strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or startup marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships for hybrid PLG/Sales-Led motions in EU/US/Canada markets.
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。
Cleans a Quarto (.qmd) file by replacing LaTeX holdovers with proper Quarto/Pandoc syntax so the document renders correctly to HTML, PDF (Typst/LaTeX), and Word. Fixes citations (\citep → [@key]), cross-references (\ref → @label), figures (\includegraphics → markdown), tables, text formatting (\textbf → **bold**), inline math (\( \) → $ $), hyperlinks (\href → [text](url)), footnotes (\footnote → ^[]), list environments, section headings, and R Markdown chunk options (dot-style → #| YAML). Use when asked to clean, fix, modernize, or port a Quarto or R Markdown document; when LaTeX commands appear in a .qmd file; when a document fails to render to Word or Typst; or when citations/cross-references do not appear in non-LaTeX output.
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。
Top-level orchestrator for Flutter feature implementation. Runs the full uninterrupted pipeline: tasks → implement-tasks-sequence → build-verify → pr-review uncommitted. Use after a PRD and tech spec are complete. Trigger phrases: "start job", "start the job", "run the full pipeline", "do it all", "implement the feature end-to-end".
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。
Configuring or troubleshooting Salesforce CPQ Advanced Approvals: SBAA__ApprovalRule__c, SBAA__ApprovalVariable__c for cross-line aggregation, SBAA__ApprovalChain__c for ordered approver sequences, escalation timeouts, Smart Approvals for re-quote skip logic, and the managed package's permission sets. Trigger keywords: CPQ Advanced Approvals, SBAA approval rule, approval variable, approval chain, discount approval, line-level approval, Smart Approvals, requote approval. NOT for standard non-CPQ Salesforce approval processes - use admin/approval-processes. NOT for CPQ price rules and discount schedules - use admin/cpq-pricing-rules.
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。
Bootstrap self-improving Symphony + Linear orchestration for a software repository. Use when Codex needs to inspect a repo, review or generate repo-local guidance, shape Linear issues, render a Symphony workflow, launch a high-leverage first execution wave, or run an orchestrator-led review, recovery, and learnings loop that makes future runs better.
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。
Create inline Mermaid v11 diagram blocks — flowcharts, sequence, class, ER, Gantt, state, timeline, user-journey, and 20+ other types — rendered in markdown files and browser environments. Zero external dependencies; mmdc is optional for CLI export.
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。
Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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。
WeChat article creator with Fu Sheng's writing style - conversational, story-driven, opinionated. Use this skill whenever the user mentions writing a WeChat article, drafting public account content, crafting a viral headline, creating article covers, writing in Fu Sheng's style, or any request involving 公众号, 微信文章, 爆款标题, or 傅盛风格 — even if they don't explicitly say "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。
Runs twice in a PFD to P&ID job. Part 1, before gate G3, validates the discipline proposals for provenance, UNKNOWN handling, conflicts, prohibited content and cross-section consistency and returns results and blocking items. Part 2, before gate G5, assembles the review package, with job header, gate record, every register and proposal, consolidated UNKNOWN list, feedback log and closing statement. Use when the user asks to "validate the enrichment", "run validation before G3", "assemble the review package" or "build the G5 package". Do not use for the readiness map, use pid-tool-readiness-mapping instead; do not use to produce a missing discipline section, use the discipline skill that owns it. Drafts for human review; never approves, authorises or signs 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。
Cut background heat and noise on this machine: audit scheduled tasks by runs per hour, autostart entries, GPU holders, power plan and top CPU, then propose disable or stretch candidates, decide line by line with the operator and apply only approved lines. Triggers: /deflate, fan is loud, laptop runs hot, what can I turn off, machine slow from background load.
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。
Social authentication and OAuth/SSO for Flutter supporting Google Sign-In, Apple Sign-In, Facebook Login, and LINE Login. Use when implementing social login, OAuth flows, sign-in with Google/Apple/Facebook/LINE, SSO integration, third-party authentication, platform credential configuration (SHA-1 fingerprint for Android, Service ID for Apple, App ID for Facebook, Channel ID for LINE), native auth flows, minimal viable examples, error handling patterns, or troubleshooting authentication failures, token handling, and sign-in errors (ApiException, PlatformException).
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。
Calculate and schedule quarterly estimated tax payments (Form 1040-ES) for self-employed individuals. Covers self-employment tax calculation (15.3%), income tax estimation, safe harbor rules (100%/110% of prior year), payment deadlines, and underpayment penalties. Trigger on "estimated taxes", "quarterly taxes", "1040-ES", "how much should I pay quarterly", "self-employment tax", "avoid tax penalty".
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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。
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.
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。
MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).
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。
Chains a somatic (tumor-normal) SNV/indel and structural-variant pipeline end to end with GATK Mutect2 (or Strelka2), wiring the somatic-specific machinery - panel-of-normals and gnomAD germline-resource priors, GetPileupSummaries/CalculateContamination, and LearnReadOrientationModel FFPE/oxoG orientation-bias filtering fed into FilterMutectCalls. Use when calling somatic mutations from a tumor-normal pair (or tumor-only with PoN caveats), deciding which artifact filter removes which class of false positive, reasoning about VAF/purity/ploidy and clonal-vs-subclonal detection, adding somatic SV/CNV or TMB/MSI/signatures, or routing variants to AMP/ASCO/CAP tier and oncogenicity interpretation (never germline ACMG).
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。
Stellar Assets (classic) + trustlines + Stellar Asset Contract (SAC) bridge to smart contracts. Covers asset issuance, distribution, authorization flags, clawback, regulated assets, trustline management, and the SAC interop layer that exposes classic assets as SEP-41 contract tokens. Use when tokenizing real-world assets, issuing stablecoins, managing trustlines, or bridging classic assets to smart contracts.
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。
Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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。
Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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。
Plan, set up, and monitor a multi-agent video production pipeline backed by Hermes Kanban. Use when the user wants to make ANY video — narrative film, product/marketing, music video, explainer, ASCII/terminal art, abstract/generative loop, comic, 3D, real-time/installation — and the work warrants decomposition into specialized profiles (writer, designer, animator, renderer, voice, editor, etc.) coordinated through a kanban board. Performs adaptive discovery to scope the brief, designs an appropriate team for the requested style, generates the setup script that creates Hermes profiles + initial kanban task, then helps monitor execution and intervene when tasks stall or fail. Routes scenes to whichever Hermes rendering / audio / design skill fits each beat (`ascii-video`, `manim-video`, `p5js`, `comfyui`, `touchdesigner-mcp`, `blender-mcp`, `pixel-art`, `baoyu-comic`, `claude-design`, `excalidraw`, `songsee`, `heartmula`, …) plus external APIs for TTS, image-gen, and image-to-video as needed.
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。
superpowersの作業記録を読み、作業の重複や計画逸脱、時間・トークン消費などを調べます。ファイルの行番号を根拠に、経緯を報告書へまとめるスキルです。
Notion-style team dashboard rendered as a Live Artifact. A single-page, self-contained HTML dashboard with KPIs, a 7-day sparkline, a real-time activity feed and a linked-database task table — wired to Notion via the Composio connector catalog. Refreshes on demand and when the artifact is opened. Falls back to seeded mock data when no connector is bound, so it works offline / in screenshots / in the picker preview.
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。