「ara」の検索結果

Codex-only team orchestration: run a named team of cooperating Codex workers with durable, script-managed state. MUST USE when the user asks Codex to create, run, coordinate, inspect, archive, or delete a team of agents/threads/sessions, or to work on something as a team in parallel. FIRST inspects the active tool surface (checking tool_search for deferred tools) and tells the user the route: native MultiAgentV2 agents (flat spawn_agent with task_name) when available, Codex App threads as the fallback, or a plain-subagent split when neither set exists. The main session is always the leader; members are defined by a concrete part, ownership area, or perspective - never a vague job role; a bundled cross-platform script writes the .omo/teams state plus an auto-generated member field manual. Use a team when the work is not perfectly isolated but parallelizing helps; use plain subagents when scope is perfectly isolated or the goal is ambiguous. Triggers: team mode, teammode, make a team, run as a team, team of agents, coordinate threads, parallel Codex threads, archive the team.

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Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

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Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

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Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.

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Use this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from the full public enum (`DOCA_ARGP_TYPE_STRING`, `_INT`, `_BOOLEAN`, `_DEVICE`, `_DEVICE_REP`, `_DOUBLE` — six values, not three); preserving the standard `--device` / `--representor` / `--json` (`-j`; real flag is `--json`, NOT `--json-config`) / `--sdk-log-level` surface; or debugging `DOCA_ERROR_BAD_STATE` / `INVALID_VALUE` / `NOT_SUPPORTED` / `IO_FAILED` from `doca_argp_*`. Trigger on implicit phrasings: "add a custom flag to a DOCA sample", "should I use getopt here", "BAD_STATE registering a new param", "my JSON config key is rejected", or "my sample's --json is ignored". Refuse and route elsewhere for variadic-flag / subcommand / shell-completion features, DOCA Core context, or DOCA Log internals.

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Execute automate SOC 2 audit preparation including evidence gathering, control assessment, and compliance gap identification. Use when you need to prepare for SOC 2 audits, assess Trust Service Criteria compliance, document security controls, or generate readiness reports. Trigger with phrases like "SOC 2 audit preparation", "SOC 2 readiness assessment", "collect SOC 2 evidence", or "Trust Service Criteria compliance".

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GraphQL and hidden parameter testing playbook. Use when exploring introspection, batching, undocumented fields, hidden parameters, schema abuse, and GraphQL authorization gaps.

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Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity. Use when the user mentions "grand slam offer", "make my offer more compelling", "what bonuses should I add", "guarantee strategy", "offer naming", or "people say its too expensive". Also trigger when packaging a product for higher perceived value, justifying premium pricing instead of discounting, designing a money-back guarantee, or structuring tiers to maximize conversions. Covers the MAGIC naming formula and starving-crowd targeting. For product positioning, see obviously-awesome. For outbound sales, see predictable-revenue.

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Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...

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Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.

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Für Formulararbeitsvertrag: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Einreichungsplan mit Form- und Nachweischeck. Fachgebiet: AGB-Recht-Prüfer. Route: formulararbeitsvertrag-haendlervertrag-agb.

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Für Formulararbeitsvertrag: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Einreichungsplan mit Form- und Nachweischeck. Fachgebiet: AGB-Recht-Prüfer. Route: agb-formulararbeitsvertrag-305ff-bgb.

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Generate, visually expand, optimize, diagnose, and route structured AI image prompts for adult female portraits. Use for lifestyle, curve-focused, fashion, oriental, fantasy, realism, beauty, CCD, low-key cinematic, studio, sport, travel, and e-commerce portrait requests; onboarding or usage help; parameter recommendations; prompt rewrites; image-to-prompt preparation; direct image generation; or identity- and product-preserving edits from authorized reference images. Preserve explicit user parameters and locked reference subjects while creating a coherent photography-directed scene.

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Project orchestrator for the screenwriting skill set (剧本项目主线调度 / 状态存档 / story bible) — a meta-skill that adds no new craft knowledge but routes a screenplay or stage-play project through stages (premise → structure → character → scenes → draft → revision → submission) and a TV / streaming series project (电视剧 / 剧集 / pilot / 一季) through a separate series stage table (engine → character network & season arc → documents/bible → pilot structure → break story & outline → draft → submission), names which sw-* skill to invoke at each stage, defines each stage's deliverable and advisory exit check, and keeps all project state in a story-bible.md file so work can resume across sessions. Use when starting a new script project, resuming one ("continue my screenplay", "where were we"), when the user asks "what should I do next" on a script, when converting a vague idea into a full development pipeline, or when the other sw-* skills are firing individually and the overall process needs sequencing.

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Dialogue writing and revision for screen and stage (对白/台词/潜台词) — merged from Robert McKee's Dialogue (dialogue as action, said/unsaid/unsayable, exposition as ammunition, beat = action/reaction gerunds, flaws of credibility/language/content/design, character-specific vocabulary and grammar, seven scene analyses), Richard Walter (economy, subtext, argument, no small talk, no parentheticals, silence), Neill Hicks (listening, on-the-nose exceptions, energy, five-step exercise), Lu Jun (语言要美; 典雅美/通俗美, 对话三要求 动作性/性格化/潜台词), Lajos Egri (dialogue born of three-dimensional character, escalation), Blake Snyder (cover-the-names test), and screwball-comedy dialogue conventions. Use when writing or polishing dialogue, fixing on-the-nose or expository lines, giving each character a distinct voice, converting a two-hander into a trialogue, writing comedy exchanges, or cutting speeches. Chinese-opera lyrics (戏曲唱词) are in sw-chinese-opera-banqiang, not here.

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Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

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Write Unreal Engine 5 C++ gameplay code: the UCLASS/UPROPERTY/UFUNCTION reflection macros, the Gameplay Framework (GameMode, Pawn, Character, PlayerController, Actor components), and the module Build.cs. Use when writing or debugging UE C++, deriving from AActor/ACharacter/ AGameModeBase, exposing properties to the editor or Blueprints, or when the user mentions Unreal C++, UCLASS, GENERATED_BODY, GameMode, ACharacter, or .Build.cs.

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Drive Unity 6.3 LTS character animation with Animator Controllers: states, transitions, parameters, blend trees, animation layers, and humanoid Avatar IK. Use when wiring an Animator, setting parameters from script (SetFloat/SetBool/SetTrigger), building blend trees, or when the user mentions Animator, Mecanim, state machine, blend tree, or .controller.

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Plan preparation for the Chinese postgraduate entrance examination (考研): choosing target schools and programmes with realistic odds, a phased study plan for the national subjects and the specialist papers, and preparation for the re-examination (复试) and adjustment (调剂). Use when asked 考研怎么规划, 帮我做考研复习计划, 择校择专业, 考研时间线, 复试怎么准备, 调剂, or plan my kaoyan. Produces a school-and-programme shortlist method with the data to collect, a month-by-month study plan by subject, weekly templates, the key-date checklist to confirm against official notices, and a 复试 and 调剂 preparation guide.

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Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Capture a URL into a full-page screenshot, cropped UI item PNGs, and a JSON manifest. Use when the user says `separateweb capture <url>`, asks to capture a website, or wants UI extraction assets without running the SeparateWeb web app.

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Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration host character (who, where, tone, voice and music come from the recipe choices) walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.

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Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch sweeps via parsim, custom input signals via Dataset, and simulation data retrieval via logsout. Use when running sim()/parsim() with setVariable, setBlockParameter, setExternalInput, or when performing parameter sweeps and multi-run analysis. Not needed for one-shot simulations without configuration.

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