「parallel」の検索結果

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".

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Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to flag hallucinated citations, build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

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Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.

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Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).

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Expand all TaskMaster tasks with deep research before coding begins. Reads tasks.json, launches parallel research agents per task in waves using the research-expander agent. Writes findings back to tasks.json. Part of the prd-taskmaster toolkit. Use after PRD is parsed and before implementation. Invoke with /expand-tasks.

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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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AI image generation with OpenAI, Google, DashScope and Replicate APIs. Supports text-to-image, reference images, aspect ratios. Sequential by default; parallel generation available on request. Use when user asks to generate, create, or draw images.

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Use when the user runs /gsd-workstreams or asks to list, create, switch, status, progress, complete, or resume parallel workstreams for concurrent milestone work. The active one is tracked in the GSD_WS variable.

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Use when onboarding to an unfamiliar codebase, before a major refactor, on a brownfield project before /gsd-new-project, or to refresh .planning/codebase/. Spawns up to 4 parallel mapper agents that write 7 documents.

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Use when you need an isolated copy of one or more repos with its own .planning/, or want to work on several repos in parallel. Creates worktrees or clones plus WORKSPACE.md; then run /gsd-new-project inside.

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Use when the user wants a dashboard of all phases or to run more than one phase in parallel. Shows phase status with recommended next actions, runs discuss inline, and dispatches plan and execute as background agents.

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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.

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 facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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。

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

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Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

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Configure file-system routing with nested layouts, route groups, parallel routes, and error boundaries in Next.js App Router. Use when creating page routes, adding loading/error states, or organizing routes with groups and dynamic segments.

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Testing standards using JUnit 5, AssertJ, Mockito, Cucumber, and Spring Boot integration tests for Java. Use when writing or reviewing Java test behavior, including parallel execution and BDD; defer Kotlin-only tests, virtual-thread test infrastructure, and coverage-report/tooling configuration.

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Create or sync session worktree for parallel dev

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Unified team skill for performance optimization. Coordinator orchestrates pipeline, workers are team-worker agents. Supports single/fan-out/independent parallel modes. Triggers on "team perf-opt".

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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".

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Review Elixir/Phoenix changes before committing — bugs, security, Ecto, LiveView, and Oban anti-patterns, via parallel specialist agents. Use when asked to review changes, a component, a diff, or a PR.

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Find the root cause of Elixir/Phoenix bugs — crashes, exceptions, stack traces, compile errors, LiveView that won't update, silent failures. Use when something is broken or misbehaves. --parallel for 4 tracks.

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Orchestrates implementation of a plan file by delegating work to subagents in parallel. Verifies git branch state, tracks progress, and ensures high-quality implementation. Invoke with a plan file path and optional model override: /implement plans/my-plan.md [--model sonnet]

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Guide a feature implementation through a structured seven-phase workflow with deep codebase understanding, clarifying questions, parallel architecture design, and quality review. Use this skill when the user asks to build a new feature, add functionality, or wants a methodical approach to implementation rather than diving straight to code.

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。