「tune」の検索結果

Design, tune, or review EF Core data access with proper modeling, migrations, query translation, performance, and lifetime management for modern .NET applications. USE FOR: DbContext, migrations, model configuration, EF queries, tracking, loading, performance, transactions, and EF6 migration decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Collaborative purple-team operations — threat-informed adversary emulation planning (ATT&CK, CTID, Atomic Red Team, CALDERA), the detect-tune-validate loop, detection coverage measurement (DeTT&CT/Navigator), safe execution and deconfliction, and MTTD/coverage reporting

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MapStage live tuner for map/globe projection, satellite, hypsometric relief, 3D terrain, region isolate, CSS color grade, and JSON presets.

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Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code; Improve Spring Boot configuration properties and beans; Review Spring Boot component scanning and profiles; Tune Spring Boot graceful shutdown or virtual threads. Part of Plinth Toolkit

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Use when building or reviewing core Quarkus applications with CDI beans and scopes, SmallRye Config and profiles, lifecycle, interceptors and events, virtual threads, and test-friendly design. This should trigger for requests such as Review Java code for Quarkus application structure and CDI; Apply best practices for Quarkus configuration and beans; Improve CDI interceptors, events, or programmatic injection in Quarkus; Add virtual-thread configuration or tune CDI lifecycle. ; Review Quarkus CDI bean lifecycle and configuration. Part of Plinth Toolkit

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Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.

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Adds and tunes a reranker stage for RAG using the retrieve-wide, rerank-narrow pattern. Use when relevant documents appear in the initial top-N but are buried by noise, top-1 precision or MRR is low despite adequate recall, or too many marginal chunks consume context. Covers cross-encoder, API, and local rerankers; N-to-k selection; and latency/cost tradeoffs. Do not use when retrieval recall itself is failing.

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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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Convert a markdown file to PDF using mistune + reportlab. Use when the user wants to convert a .md file to PDF, or when another skill needs to produce a PDF from markdown output.

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Use FFCV to convert datasets into .beton files, build fast PyTorch loader pipelines, handle image and custom fields/transforms, and tune cache, traversal, and throughput behavior.

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Use ColBERT/colbert-ai for late-interaction retrieval: prepare data, inspect configs, train/fine-tune, index collections, search rankings, update indexes, serve search, evaluate MS MARCO/LoTTE outputs, or reason about Baleen multi-hop retrieval.

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Use AutoKeras for Keras-based AutoML task APIs, custom AutoModel graphs, tuner search, and model export workflows.

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Review factory evidence and propose tighter or looser constraints without editing the charter. Use for a periodic constraint review.

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。

Meihua Yishu (梅花易數) Plum Blossom I Ching divination skill. Use when users request divination, fortune telling, hexagram casting, or character analysis (測字). Triggers on: 占卜, 算卦, 問卦, 起卦, 解卦, 測字, 拆字, meihua, plum blossom, I Ching divination, 梅花易數.

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Think and work like an expert Information Retrieval Scientist. Use when a task calls for Information Retrieval Scientist judgment. Reasons from the Probability Ranking Principle, Saracevic's relevance layers, and Cranfield pooling through tuned BM25, SPLADE/ColBERT/dense and LLM reranking, trec_eval/ir_measures with paired topic-level tests, interleaving, and nugget-based RAG evaluation while treating unjudged-as-nonrelevant pools, position-biased clicks, LLM-judge circularity, single-vector embedding limits, and benchmark contamination as first-class failure modes.

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Build, configure, and ship production voice agents on the ElevenLabs Agents platform (branded "ElevenAgents," formerly "Conversational AI"). Use when an agent must design a spoken-conversation system prompt, pick an LLM and TTS voice/model for a real-time voice bot, wire client/server/system tools and knowledge-base RAG, connect a channel (WebRTC/WebSocket SDK, embeddable widget, or telephony via Twilio/SIP), tune turn-taking and latency, set up simulation testing and evaluation, or reason about pricing, concurrency, and the consent/disclosure obligations of a synthetic voice talking to real people. Not for plain non-conversational text-to-speech, dubbing, or music generation.

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Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

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。

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

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。

Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

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DSPy is a Python framework from Stanford NLP that replaces hand-written prompts with code: you declare LLM tasks as typed signatures, compose them into modules, and let optimizers (BootstrapFewShot, MIPROv2, GEPA) tune the prompts and few-shot examples against your metric. Use when the user asks about DSPy, signatures, ChainOfThought, ReAct, teleprompters or optimizers, or wants to stop hand-tuning prompts.

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Configure and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.

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。

Restructure and optimize alert rules for monitoring platforms (Sentry, PagerDuty, Datadog, OpsGenie). Use when someone asks to "reduce alert noise", "fix alert fatigue", "create alert rules", "set up escalation policies", "tune alerting thresholds", or "create on-call runbooks". Generates platform-specific alert configurations and tiered escalation policies.

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。

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when "crewai, multi-agent team, agent roles, crew of agents, role-based agents, collaborative agents, crewai, multi-agent, agents, orchestration, roles, collaborative-ai" mentioned.

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The specialized craft of writing copy that sells—headlines that stop thumbs, body copy that persuades, CTAs that convert. This is copywriting specifically tuned for advertising: paid media, performance marketing, direct response. Ad copy is different from content writing. Every word costs money (CPM/CPC). Every character competes for attention. There's no time for warm-up—you have one chance to hook, persuade, and convert. The best ad copy feels effortless but is engineered word by word. In the age of AI-generated creative at scale, ad copywriting becomes both more important (you need more variations) and more challenging (everyone has access to the same tools). The differentiation is in the strategy, the insight, and the craft that AI assists but doesn't replace. Use when "ad copy, headline, ad text, CTA, call to action, Facebook ad, Google ad, ad variations, direct response, performance copy, primary text, ad headline, ad-copy, headlines, direct-response, performance, conversion, cta, advertising" mentioned.

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。