「ecc」の検索結果

DockerとDocker Composeで開発環境を構成し、通信・データ保存・権限を見直すほか、元のファイルを変更せずにCLIの導入手順を検証するスキル。

• アプリとDBの開発環境を構築
• コンテナの通信とデータ保存の確認
• Docker設定のセキュリティ見直し

実際のメールボックスで受信メールの仕分け、返信の下書き、送信確認を進めるスキル。履歴と送信済みの記録を確かめ、使用アカウントや対応状況を明確にします。

• 受信箱の仕分けとアーカイブ候補整理
• 履歴を踏まえた返信を下書きしたいとき
• メールを送信して記録を確認したいとき

Frontend design guidance, upstream-maintained (impeccable).

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。

Follow-up design polish skill inspired by Impeccable. Use after a web or HTML artifact exists to audit, critique, polish, animate, harden, and prepare the page for a live/share pass.

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 when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

Planificación persistente basada en archivos para tareas multipaso de agentes de IA. Mantiene task_plan.md, findings.md y progress.md en disco; los hooks del ciclo de vida inyectan contexto seleccionado de planificación del proyecto. La recuperación automática solo lee los archivos de planificación del proyecto. session-catchup.py --metadata, solicitado de forma explícita, puede inspeccionar metadatos locales de sesiones del mismo proyecto; --replay puede emitir extractos limitados y enmarcados con nonce. El modo con gate opcional solo puede solicitar que el host continúe si este lo admite y nunca ejecuta comandos declarados en Markdown. El skill no tiene ninguna ruta de carga por red. Úsalo para investigación o trabajo que requiera 5 o más llamadas a herramientas.

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。

Three modes for CS-conference papers (CVPR/ICCV/ECCV vision, ACL/EMNLP/NAACL NLP, ICLR/NeurIPS/ICML/AAAI ML). DIRECT-EDIT mode (common): the user describes a change in Chinese or English and the manuscript (LaTeX or Markdown) is edited directly through a CS-venue writing toolkit with author sign-off (use for 改这段 / 把中文想法写成 latex / polish / de-AI / translate / compress a passage). REVIEW mode (occasional, pre-submission): harden the paper through an adversarial courtroom review engine (N holistic domain reviewers / contestability routing / two-sided trial / three-way verdict / clerk-converged multi-round loop) with consensus-gated, author-signed revisions (use for review / critique / 审稿 / 评审 / mock-review). AUTO mode (unattended, opt-in via /goal): run the review-revise loop toward a verifiable goal, applying safe fixes under a drift-bounded policy and queueing risky ones. Resolves all inputs at runtime, no hardcoded paths. Not a from-scratch drafter (use ml-paper-writing) and not an official-venue rebuttal.

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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 when designing, reviewing, refining, fixing, or codifying frontend UI with Maestro's self-contained Impeccable core

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。

Operate ECCV2022-RIFE video frame interpolation, benchmark evaluation, and training workflows from a source checkout.

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 when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers design systems, anti-pattern detection, brand vs product registers, typography, color (OKLCH), spacing, motion, copy, and accessibility. By Paul Bakaus (ex-Google, ex-Disney, ex-Unity). Full version includes 23 sub-commands, CLI detection, Chrome extension, and E2E test suite: npx skills add pbakaus/impeccable

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。

Guide users through ECC's current agents, skills, commands, hooks, rules, install profiles, and project onboarding by reading the live repository surface before answering.

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。

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

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。

Configura el repositorio de GitHub de punta a punta con gh — descripción, topics, labels, rama develop, protección de ramas, merge settings y metadatos — a partir de la definición del producto. Úsalo al crear el repo de un proyecto nuevo o cuando haya que poner en orden uno existente (p. ej. "configura el repo", "prepara el repositorio en GitHub", "pon los labels y la protección de ramas").

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

Frontend UI design, critique, audit, polish. Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

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。

Apply the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

生のプロンプトを分析し、意図とギャップを特定し、ECCコンポーネント （スキル/コマンド/エージェント/フック）にマッチングし、すぐに貼り付けて 使える最適化済みプロンプトを出力します。アドバイザリー専用 — タスク自体は 実行しません。 トリガー条件: ユーザーが「optimize prompt」「improve my prompt」 「how to write a prompt for」「help me prompt」「rewrite this prompt」 と言った場合、またはプロンプト品質の向上を明示的に依頼した場合。中国語の 同等表現でもトリガーされます: 「优化prompt」「改进prompt」「怎么写prompt」 「帮我优化这个指令」。 トリガーしない条件: ユーザーがタスクの直接実行を求めている場合、または 「just do it」/「直接做」と言った場合。「优化代码」「优化性能」 「optimize performance」「optimize this code」と言った場合もトリガーしません — これらはリファクタリング/パフォーマンスタスクであり、プロンプト最適化 ではありません。

NanoClaw v2の操作と拡張 — ECCのゼロ依存関係セッション対応REPLで、claude -p上に構築。

Manage a file-based epic → story → sprint development loop without requiring an external project management tool. Creates and tracks epics, breaks them into user stories, assigns them to sprints, and drives implementation story by story. Use when starting a new feature initiative or when the team has no Jira/Linear/Taiga but needs structured delivery tracking.

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。

Inventory and govern a project's test assets. Connect requirements, business rules, risks, bugs, and cross-boundary contracts to stable TEST-IDs and executable evidence in the repository's existing test registry or TESTS.md. Use for test inventory, test gap analysis, unit/integration/contract/E2E/smoke classification, bug-to-test regression protection, consumer/provider contract testing, test-necessity review, and pre-delivery evidence 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。