「ara」の検索結果

Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"

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Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

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d3k assistant for debugging web apps

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Tauri v2+ cross-platform app development with Rust backend. Use when configuring tauri.conf.json, implementing Rust commands (#[tauri::command]), setting up IPC patterns (invoke, emit, channels), configuring permissions/capabilities, troubleshooting build issues, or deploying desktop/mobile apps. Triggers on Tauri, src-tauri, invoke, emit, capabilities.json.

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Start long-lived provider LLM teammates as native Claude Code agent-team members (the native Agent tool with a ccf-<provider> type) that you message with SendMessage — multi-turn, collaborative, watchable in tmux panes. Use for sustained parallel build/work ("start 3 glm teammates", N teammates on N files), or when you need a collaborator you message across turns, from a terminal `claude` session. NOT a fire-and-forget one-shot or a flat batch of independent prompts — that is /cc-fleet:subagent. NOT a scripted multi-phase run — that is /cc-fleet:workflow.

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Run a one-shot or flat parallel batch of provider LLM subagents (headless `cc-fleet subagent`) that return a result. Use when fanning out N independent tasks, doing bulk per-file work, or calling a specialized provider model (DeepSeek / GLM / Kimi / Qwen / MiniMax). NOT a long-lived collaborator you message back and forth (that is /cc-fleet:team); NOT a multi-phase pipeline with dependencies or resume (that is /cc-fleet:workflow); NOT trivial work the main session should just do.

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Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

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Fans Claude work out over the Anthropic API from surfaces with no native subagents, chiefly claude.ai chat and projects, with parallel calls with a shared cached context, streaming, long-lived conversation threads with cached history, and named agent pools; also delegates to Gemini through the Cloudflare AI Gateway on any surface. Use for multi-perspective analysis, a panel of reviewers, or task decomposition from claude.ai, or for Python code that calls several Claude instances concurrently over the API.

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Tulis dan sunting teks Bahasa Indonesia agar jelas, spesifik, alami untuk audiensnya, dan sesuai suara yang diminta. Gunakan untuk artikel, esai, blog, media sosial, atau permintaan mengurangi pola tulisan generik. Jangan menjanjikan hasil detektor AI.

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Full Agency Onboard — launches 5 parallel audit teams and produces a unified client-ready report with composite scoring

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Preprocesses photographed sheets of many business cards — slicing each into overlapping high-resolution tiles and de-glaring them with container tooling (OpenCV/ImageMagick) — then reads every card via cheap parallel temperature-0 API calls (Haiku or Sonnet) using a distilled extraction prompt, and writes deduped contact fields to a CSV. Use when a user has photos or scans holding multiple business cards per image, mentions glare or unreadable cards, batch card transcription, contact extraction, or wants to read many cards without an expensive in-conversation pass. Triggers on 'business cards', 'card scan', 'extract contacts', 'read these cards', 'card glare', 'too many cards per photo'.

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Invokes Google Gemini models for structured outputs, image generation, text-to-speech narration, multi-modal tasks, and Google-specific features. Use when users request Gemini, image generation, Gemini TTS or a synthesized voice, structured JSON output, Google API integration, or cost-effective parallel processing.

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Generates WAFFLES Declarations for social media posts — preemptive lists of what a post does NOT say. Use when users mention WAFFLES, ask for clarifications on their post, want to prevent misinterpretation, or request disclaimers for controversial/nuanced takes.

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Skill-aware orchestration with context routing. Decomposes complex tasks into skill-typed subtasks, extracts targeted context subsets, executes subagents in parallel, and synthesizes results. Self-answers trivial lookups inline. No SDK dependency — uses raw HTTP via httpx. Use when tasks require multiple analytical perspectives, when context is large and subtasks only need portions, or when orchestrating-chat-agents spawns too many redundant subagents.

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DEPRECATED, renamed to orchestrating-chat-agents. Kept so existing imports of its scripts keep working; every module here loads the same module from orchestrating-chat-agents. Use orchestrating-chat-agents for parallel Claude calls over the API from claude.ai, and dispatching-subagents for fan-out in Claude Code.

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Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-chat-agents skill.

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Text-prompted image zone detection using TIPSv2 B/14 on CPU. Produces `focus_targets` / `focus_edges` bbox lists from natural-language labels, ready to feed into `svg-portrait-mode`. Use when you want automatic foreground/background separation from prompts like "dog face" + "wooden floor" instead of hand-annotating bboxes.

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Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

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Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.

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Draws images, PDF/SVG vector files and animation frames with pycairo, and supplies what cairo lacks: shaped text for Arabic, Indic and other scripts, text along curves, blur/glow/drop shadows, path booleans, stroke-to-outline, and Gouraud-shaded 3D. Use when writing pycairo or cairo drawing code, generative art, custom diagrams or posters rendered in Python, or when cairo output needs non-Latin text, soft effects, merged shapes or shaded surfaces.

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Create interactive data visualizations using Vega-Lite declarative JSON grammar. Supports 20+ chart types (bar, line, scatter, histogram, boxplot, grouped/stacked variations, etc.) via templates and programmatic builders. Use when users upload data for charting, request specific chart types, or mention visualizations. Produces portable JSON specs with inline data islands that work in Claude artifacts and can be adapted for production.

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Delegates work to a subagent or spawned session by writing only the task, while a PreToolUse hook pages the parent transcript through Jev and appends the chunks that task needs. Use before writing any Agent tool prompt or create_session prompt, and when asked to delegate this, hand this off, use a subagent, spawn an agent, run agents in parallel, or brief a subagent on what we have done so far.

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Use when implementing "go back" / "rewind" / step-navigation features in a Pixi'VN game, or when you need to read the player-facing dialogue/choice history (e.g. for a backlog or "history log" UI) — covers the History module's `stepHistory` API and the "step" vs "paragraph" goBackMode.

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Index and entry point of the Pixi'VN skill set — start here for any task on a @drincs/pixi-vn project, then follow the skill map to the right skill (narration, canvas, characters, assets, sound, storage, saves, history, ui, minigames, testing, migration). Use when setting up a new or existing project, wiring the main.ts entry point, or calling the top-level Game API (Game.init, Game.start, Game.onEnd, Game.addOnError, Game.onNavigate, Game.clear) — every Pixi'VN project needs this before touching canvas, narration, sound, storage, or history.

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。