「aso」の検索結果

Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. This should trigger for requests such as Review JDBC or SQL data access in a Micronaut project; Improve transactions and parameter binding for Micronaut JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix self-invocation bypassing @Transactional in Micronaut; Review Hikari or pooled datasource usage in Micronaut JDBC. Part of Plinth Toolkit

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Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.

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App Store and Google Play screenshot creation with exact platform specs. Covers iOS/Android dimensions, gallery ordering, device mockups, and preview videos. Use for: app store optimization, ASO, app screenshots, app preview, play store listing. Triggers: app store screenshots, aso, app store optimization, play store screenshots, app preview, app listing, ios screenshots, android screenshots, app store images, app mockup, device mockup, app gallery, store listing

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Trigger for: Labor-Hour IGCE, LH estimate, Time-and-Materials IGCE, T&M estimate, burdened hourly rate, burden multiplier, labor-category ceiling hours, materials estimate, proposed LH/T&M rate validation, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable LH/T&M estimates using BLS OEWS wages, explicit burden multipliers, GSA CALC+ positioning, GSA Per Diem travel, and separately priced T&M materials. Do NOT use for FFP, cost-reimbursement, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Trigger for: cost-reimbursement IGCE, CR cost estimate, CPFF, CPAF, CPIF, cost-plus estimate, BAA estimate, fixed-fee analysis, award-fee analysis, incentive-fee analysis, proposed CR rate validation, cost-pool buildup, share-ratio scenario, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable CR estimates using BLS OEWS wages, fringe/overhead/G&A/FCCM pools, contract-type-specific fee, GSA CALC+ positioning, and GSA Per Diem travel. Do NOT use for FFP, LH/T&M, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks such as broad codebase analysis, fast iterations with Gemini 3 Flash, or deep architectural reasoning with Gemini 3 Pro. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task", "use gemini 3 flash", "use gemini 3 pro".

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Makes agent reasoning visible, surfaces tradeoffs, and fades help so humans build judgment. Use when reviewing or learning from agent-written code.

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Optimize Apple App Store metadata in store.config.json for ASO (App Store Optimization). Use when working with store.config.json, App Store keywords, titles, subtitles, descriptions, or localizing app metadata. Helps maximize app visibility and downloads.

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Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

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Run an offline ASO audit on canonical App Store metadata under `./Store` and surface keyword gaps using Astro MCP. Use after pulling metadata with `asc metadata pull`.

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Think and work like an expert Knowledge Representation Researcher. Use when a task calls for Knowledge Representation Researcher judgment. Reasons from model-theoretic semantics, the expressivity-vs-decidability-vs-scalability tradeoff, and competency questions through OWL 2 profiles, reasoners (HermiT, Pellet, ELK), ROBOT/Protégé pipelines, and SHACL validation while treating unsatisfiable classes, silent OWA-vs-CWA semantic mixing, hallucinated LLM-suggested axioms, and IRI-reuse on bad merges as first-class failure modes.

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Think and work like an expert Geodesist. Use when a task calls for Geodesist judgment. Reasons from coordinates as four-dimensional objects with epoch, velocity, and frame realization (ITRS vs. ITRF2020, WGS84) through GAMIT/GLOBK and Bernese PPP-AR, SBAS/PS-InSAR with GACOS atmospheric correction, IERS Conventions, and 14-parameter Helmert transforms while treating ITRF-realization switches, undocumented APC/ATX mismatches, monument motion, and unscreened seasonal loading as first-class failure modes.

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Think and work like an expert Environmental Scientist. Use when a task calls for Environmental Scientist judgment. Reasons from source-pathway-receptor linkages, multimedia partitioning, and dose-as-exposure through conceptual site models, fate models (MODFLOW/MT3DMS, AERMOD), SW-846 QA/QC chains, and ProUCL/Mann-Kendall statistics while treating censoring bias, conceptual-model error, well-construction artifacts, and seasonal confounding as first-class failure modes.

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Think and work like an expert Composites Engineer. Use when a task calls for Composites Engineer judgment. Reasons from CLT/ABD laminate mechanics, Halpin–Tsai micromechanics, and CMH-17/NCAMP allowables through autoclave/OOA/RTM process control, ASTM D30 mechanical qualification, ultrasonic C-scan and CAI damage tolerance while treating fiber waviness, void content, under-cure, and quasi-isotropic strength traps as first-class failure modes.

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Complete marketing pipeline for apps, dev tools, libraries, and SaaS products in 2026. Generates positioning, listing/ASO/package-SEO assets, press kit, launch plan, social calendar, email sequences, growth/referral strategy, paid playbook, retention, and community plan. Adapts automatically to app vs dev-tool vs SaaS. Trigger on: marketing, launch plan, app launch, go-to-market, ASO, press kit, product hunt launch, hacker news launch, dev tool marketing, growth strategy, launch checklist, marketing plan, distribution plan.

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Optimize mobile app listings for discovery and conversion in Apple App Store and Google Play. Use when tasks involve ASO keyword research, title and subtitle optimization, screenshot and preview video design, A/B testing store listings, review management, localization for international markets, tracking keyword rankings, or improving download conversion rates. Covers both iOS and Android store algorithms and best practices.

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Integrate Claude AI into applications with the Anthropic SDK. Use when a user asks to add Claude to an app, use Claude for text generation, build a chatbot with Claude, use Claude's long context window, implement tool use with Claude, stream Claude responses, use Claude for code generation, document analysis, or reasoning tasks. Covers Messages API, streaming, tool use, vision, system prompts, extended thinking, and batch processing.

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。