43 件（catlog22 のリポジトリ） ・ 人気順
概要と使いどころ
Intent-driven issue lifecycle management — describe what you want in natural language (报告一个 bug / 列出开放 issue / 关掉 ISS-xxx / 关联到 task / 扫描发现问题) and the workflow routes to the right operation. Operates on .workflow/issues/. 知识管理走 /maestro-knowledge；knowhow 沉淀走 /maestro-knowhow；约束规则走 /maestro-spec。Triggers on "issue 管理", "报 bug", "记录问题", "issue list", "关闭 issue", "issue discover", "发现问题".
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。
Organize messy conference LaTeX template .zip files into clean Overleaf-ready structure. Extracts, analyzes, cleans up, and generates README with submission requirements. Triggers on "organize LaTeX template", "clean up template", "prepare Overleaf template", "整理LaTeX模板".
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。
Iterative skill tuning via execute-evaluate-improve feedback loop. Uses maestro delegate Claude to execute skill, Agy to evaluate quality, and Agent to apply improvements. Iterates until quality threshold or max iterations. Triggers on "skill iter tune", "iterative skill tuning", "tune 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。
Unified team skill for issue resolution. Uses team-worker agent architecture with role directories for domain logic. Coordinator orchestrates pipeline, workers are team-worker agents. Triggers on "team issue".
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。
Unified team skill for code review. 3-role pipeline: scanner, reviewer, fixer. Triggers on team-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。
Unified team skill for testing team. Progressive test coverage through Generator-Critic loops, shared memory, and dynamic layer selection. Triggers on "team testing".
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。
Remove AI writing patterns from academic prose. Detects and fixes inflated symbolism, promotional language, superficial analyses, vague attributions, AI vocabulary, and formulaic structures. Supports English and Chinese. Triggers on "remove AI patterns", "humanize text", "anti-AI polish", "去除AI写作痕迹", "人性化处理".
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。
Four-layer citation verification for academic papers. Scans LaTeX/BibTeX files, verifies every citation via WebSearch and Google Scholar, generates verification report with fix suggestions. Triggers on "verify citations", "check references", "citation verification", "prevent fake citations", "引用验证".
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。
Systematic experimental results analysis workflow for ML/AI research papers. Connects experimental data to publication-ready Results sections with statistical validation, visualizations, and quality checks. Triggers on "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization".
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。
Research ideation workflow from literature search to research planning. Triggers on "brainstorm research ideas", "identify research gaps", "conduct gap analysis", "start research project", "conduct literature review", "define research question", "select research method", "plan research", "research ideation".
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 academic paper writing workflow. Takes a research repository and produces a publication-ready LaTeX manuscript for top ML/AI conferences (NeurIPS, ICML, ICLR, ACL, AAAI, COLM). Covers repo understanding, structure planning, section drafting, citation management, anti-AI polishing, and conference formatting. Triggers on "write paper", "draft paper", "scholar writing", "paper writing workflow".
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。
Initialize project with auto state detection
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。
Intent-driven knowhow precipitation — describe what you want to capture (记一个关于X的决策 / 保存这段代码模板 / 写个部署配方 / 存个调试技巧) and the workflow infers the type and records it into .workflow/knowhow/. Pure capture surface; knowhow 的管理/审计走 /maestro-knowledge；项目约束规则走 /maestro-spec add。Triggers on "knowhow capture", "知识沉淀", "沉淀经验", "记录模板", "记录决策", "adr", "存个技巧".
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。
Closed-loop policy over the canonical Session/Run chain
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。
Intent-driven spec precipitation — state a constraint in natural language (加一条规范：禁止用 any / 记录架构约束：服务间走 gRPC / 质量规则：覆盖率≥80%) and the workflow infers the category and records a <spec-entry>. Spec = 项目约束规则（编码规范、架构约束、质量标准）；可复用知识文档走 /maestro-knowhow capture。Triggers on "maestro-spec add", "记录规范", "添加约束", "添加规则", "加一条规范", "spec add". Terminology：spec = project constraints/rules (<spec-entry>). Reusable knowledge documents use /maestro-knowhow capture. Learning discoveries from /maestro-learn use <learning-entry> tags in learnings.md (separate from spec entries).
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。
Post-acceptance conference preparation workflow covering presentation slides, academic posters, and promotion content. Triggers on "scholar publish", "conference preparation", "prepare presentation", "create poster", "write promotion", "post-acceptance".
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 or sync session worktree for parallel dev
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。
Merge session worktree branch back to main
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。
Swarm intelligence team skill — ACO-driven multi-agent exploration with hybrid LLM coordinator + Python optimization controller. Coordinator generates swarm-config from user task, then runs K iterations of N parallel ants guided by pheromone state. Universal task space via config (nodes + scoring rule). Triggers on "team swarm", "swarm intelligence", "蚁群".
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。
Quick execution for small tasks — minimal run lifecycle (start + done) with evidence recording. Full LLM capability, scoped to mechanically clear 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。
Unified entry for all development intents — classify intent, assess complexity, route to the correct execution channel: /maestro-companion (lightweight), standard single run, or /maestro and /maestro-ralph (multi-step manual/orchestrated). Pure router, never runs execution loops itself
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 editing boundary restrictions
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。