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Save the current code state in a local Git commit before risky changes, then return to that checkpoint if needed. Useful for multi-file refactoring and trying another approach after an unsuccessful edit.
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Process datasets that exceed available memory and run Python computations across multiple cores or machines. The skill guides large table and array operations, multi-file cleaning and aggregation, custom task dependencies, and execution settings for parallel workflows.
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You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.
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Expert Cursor (IDE IA, multi-file edits, composer, contexte, rules, contexte DZ)
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LAM Coordinator - タスクを分解し、適切な Subagent で並列実行する。 複数ファイル/モジュールにまたがる作業の自動分解・並列実行に使用。 Use proactively when the user requests multi-file or multi-module operations.
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让AI智能体按准确性、完整性、清晰度、可操作性和简洁性五个维度自评成果，给出有具体依据的分数、平均分，以及按优先级排列的改进建议。
在跨文件代码改写前，整理各文件的职责、修改对象、执行顺序与风险，生成供后续编辑、差异审查和构建测试参考的计划。
为跨文件开发或审计初始化由三个及以上人工智能代理组成的团队，分配专业角色，并配置团队结构、并行执行和代理间通信。
Coordinate large code migrations in reviewable batches. The agent changes local files, while the Composio CLI connects tracking issues, pull requests, and automated checks so each batch can be tested and reviewed before proceeding.
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Turn a multi-file development task or technology migration into a specification with acceptance criteria, then launch background implementation and verification. The workflow clarifies missing requirements, reviews the specification, and tracks progress and blockers through linked jobs.
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Apply related edits across several files using a unified diff, a file describing code changes. Check the patch with Git before applying it, and rebuild rejected patches using the current file contents.
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Plan complex coding changes by exploring existing code, clarifying requirements, and tracing effects across the system. Produce a PLAN.md with recommended changes, affected files, and verification steps, then implement after the user approves.
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Count rows across Excel sheets to assess data size, then analyze an existing Parquet file, a format for storing tabular data. Calculate category counts and percentages, and save the results as an Excel summary and a pie chart.
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Run small C# apps without creating a project, using the .NET CLI, its command-line tool. Use it to test language features, explore APIs, or prototype utilities with helper files and NuGet packages.
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Coordinates multiple AI providers to research, define, implement, and validate development tasks. It supports parallel task decomposition, comparison of proposed solutions, cross-model critique, and quality checks that feed into combined reports and final deliverables.
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Use whenever the agent creates, writes, moves, or renames a file in a team/delegate (shared) workspace, OR when the user asks to organize, clean up, restructure, audit, or find files in any workspace or the Vault, OR when starting a multi-file task or named project. Enforces a purpose-based folder convention (flat mode: notes/, data/, outputs/, scripts/, archive/; project mode: projects/<slug>/{docs,assets,source,reports,research}/), per-agent namespacing under shared/<agent_key>/, and pre-write discovery via memory_search, vault_search, knowledge_graph_search to surface related files and avoid duplicates. Trigger before any write_file or exec at workspace root, when starting a project, generating reports/assets/exports, delegating, or when the user says "messy", "where did I save", "tổ chức lại", "dọn workspace", "tạo report", "find related", "search vault". Do NOT trigger for read-only ops, edits inside an existing project tree (cloned repo), or short-lived files deleted in the same turn.
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Maintain a structured planning document tree made of roadmap/status files, implementation status or handoff TODO files, topic notes, decision records, open questions, ideas/inspiration pools, and repository/file-structure hygiene plans. Use when Codex needs to create, reorganize, audit, or update a multi-file plan, design-doc folder, roadmap tree, active implementation-status file, repo cleanup/filesystem plan, ADR/decision log, ideas inbox, or linked planning knowledge base; reconcile Done/In Progress/Next state; resume work from TODO/handoff state; move resolved questions into decisions; promote ideas into formal plan artifacts; or keep plan documents and file-structure planning internally consistent without making this project-specific.
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AI DevKit · Systematic structural or multi-file refactors across any stack while preserving behavior and public contracts. Use for reorganizing modules, boundaries, naming, APIs/contracts, staged refactor plans, or refactor risk review.
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Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.
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Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.
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Run file-based C# apps with the .NET CLI when the user explicitly wants C#/.NET code without creating a project. Use for C# language/API experiments, one-file C# apps, small multi-file C# apps composed with `#:include`/`#:exclude`, or C# file-based apps linked with `#:ref`. Do not use for language-agnostic throwaway scripts, generic computations, Python/PowerShell-style automation, full projects, or existing app integration.
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Tracked lightweight execution with composable rigor flags: --trivial, --discuss, --research, --full. Covers zero-ceremony inline fixes (typo, spelling fix, small mistake in a single file, ≤3 edits) through contained multi-file changes.
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Symbol-level code context for LLM coder-agents: tree-sitter extracts symbols, PageRank ranks them over the cross-file reference graph, and the top class/function signatures are fed to the LLM as a token-budgeted read-only map (not RAG, no vector index, human-auditable). Use when an agent must locate the right files in a large/multi-file repo, when builds/refreshes/scopes a repo-map, or when "model edits the wrong file" needs fixing.
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Use for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.
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