「argument」の検索結果

Search the asset database using the search filter string. Allows you to search for Assets. The string argument can provide names, labels or types (classnames).

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Use when the user mentions tosutil, TOS object storage, bucket/object operations, batch upload or download, object metadata, TOS connectivity, or errors such as 403/network/argument failures. 围绕火山引擎对象存储 tosutil 生成与校验命令、规划桶/对象操作并诊断错误；当用户提到 tosutil、TOS 桶对象管理、批量上传下载或相关排障时调用。

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Guidance for Tauri v2 single-instance behavior and second-launch argument handling.

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Guidance for Tauri v2 CLI plugin with argument schema and app command routing.

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Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

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Use when designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).

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Expert in proof techniques, mathematical reasoning, and rigorous argumentation for students learning to write proofs

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Master AI debate techniques with comprehensive counter-arguments against slop criticism, safety concerns, philosophical objections, and political rhetoric

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Fix a specific bug or problem in a {{engine_name}} project. Supports two modes — immediate fix or plan-first. Without arguments executes existing .unikit/code/FIX_PLAN.md. Checks {{engine_name}} compilation, suggests test coverage, and creates self-improvement patches. Use when user says "fix bug", "debug this", "something is broken", "fix the tests", "fix the errors in the tests", "read the errors in the {{engine_name}} console logs and fix the problems", or pastes an error message or {{engine_name}} console log. Also trigger when user shares a NullReferenceException, MissingReferenceException, compilation error, or any {{engine_name}}-specific error.

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Record a finding about the configured engine MCP server into .unikit/MCP-RECHECK-NOTES.md — the project's log of what has to be re-checked here. Use right after a call reported success while changing nothing, ate an argument, or validated a broken state, e.g. "write this down", "record this MCP finding", "the server lied — note it", "add a recheck note", "trap this". Takes three forms of input: the finding in one line, a path to a plan file (harvests the "## MCP Findings" table of that plan and nothing else), or nothing at all — then it takes findings already in the session first, and offers to scan the tables of plans touched since the last audit. The table only, never the plan body. Writes in the one allowed genre — a check to perform; never a lifted gate, never a named workaround, never a claim about what the server can or cannot do. Makes zero MCP calls and needs no editor. To replay, retire, or upstream existing notes use /unikit-mcp-audit instead.

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Train an ML model in the QuantConnect ML-Training-Pipeline with thermal-safe GPU usage and rigorous validation. Arguments: <architecture|script> [--dry-run] [--seeds 0,1,7,42,99] [--folds 5] [--bg]

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Execute iterative improvement workflow for QuantConnect strategies. Arguments: [strategy|issue#] [--iterations=N] [--no-backtest] [--commit]

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Detect cell-ordering / enchainement problems in Jupyter notebooks (canonical-order slippage, misplaced or forgotten cells). Arguments: [target] [--severity HIGH|MED|LOW] [--json]

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Review and merge student exercise PRs during TP sessions. Arguments: <repo-url> [--class <class-id>] [--timeslot <HH:MM-HH:MM>] [--dry-run]

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Analyze PowerPoint slides qualitatively using vision AI. Arguments: <deck_path> [--render] [--slides 1,5,10]

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Enrich Jupyter notebooks with pedagogical markdown content. Arguments: [target] [--execute] [--fix-errors] [--strict] [--consecutive] [--iterate]

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Build or improve Jupyter notebooks iteratively with quality scoring. Arguments: <new|improve|fix> <path> [--topic] [--domain] [--level] [--quality] [--max-iter]

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Cycle de coordination ai-01 (coordinateur UNIQUEMENT — jamais sur un worker). Lit memoire + dashboards + inbox + GitHub, merge les PRs pretes, tranche les design-gates, dispatche des grains par lane, reporte. Arguments: [--dispatch] [--focus <topic>]

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Clean up and reorganize markdown in enriched Jupyter notebooks. Arguments: [target] [--dry-run] [--aggressive] [--hierarchy-only]

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Execute Jupyter notebooks using local scripts or MCP Jupyter. Arguments: <notebook_path> [--mode] [--cells] [--timeout] [--batch] [--save]

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Verify and test Jupyter notebooks with iterative fixing. Arguments: [target] [--quick] [--fix] [--python-only] [--dotnet-only]

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Validate the GenAI stack (services, authentication, models, notebooks, GPU). Arguments: [all|services|auth|models|notebooks|vram] [--full] [--auth-only] [--nodes-only] [--notebooks]

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Iterate on a GenAI notebook against the self-hosted stack via the genai-stack CLI (config dirs, auth, subdomains, quantization, GPU/VRAM). Arguments: <notebook|service> [--service comfyui|forge|vllm] [--quant int4|fp8] [--validate] [--bg]

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Runs a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination.

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。