「rag」の検索結果

Configure pre-commit hooks and lean GitHub Actions for shift-left quality assurance. Use when adding or auditing CI/CD to maximize local test coverage and minimize CI cost. Skip for Terraform/K8s, deployment pipelines, or non-GitHub CI providers.

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Build a knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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Use this skill whenever you need to reason like John D. Rockefeller, founder of Standard Oil and American oil industrialist. Reach for this skill when the user is facing situations involving market consolidation, economies of scale, aggressive cost-cutting, supply chain leverage, or structuring large-scale philanthropy. It is highly applicable for questions about eliminating operational waste, dealing with fierce competition, negotiating from a position of immense scale, or building self-sustaining charitable foundations. Trigger this skill for topics like monopolies, ruthless efficiency, corporate PR crises, and strategic acquisitions, even if the user doesn't explicitly name Rockefeller.

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Applies the pragmatic, financially-grounded reasoning of Marian Ilitch (co-founder of Little Caesars Pizza). Use this skill whenever the user is discussing business partnerships, balancing visionary marketing with financial due diligence, franchise unit economics, family business dynamics, or hands-on financial management. Trigger this when users face trade-offs between rapid growth and profitability, need to enforce unit-level accountability, or are paralyzed by the fear of making mistakes. Apply her principles to ground lofty ideas in tactile, common-sense financial reality.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Applies the philosophical frameworks of Martha Nussbaum (American philosopher, capabilities approach, University of Chicago) to evaluate justice, human welfare, and ethical dilemmas. Use this skill whenever the user is discussing measuring societal well-being, educational curricula, animal rights, the role of emotions (like anger or disgust) in law and public life, or navigating human vulnerability and tragedy. Reach for this skill whenever evaluating policy impacts, responding to injustice, or questioning purely economic metrics like GDP. It shifts the focus to what people are actually able to do and be.

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Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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This skill channels the strategic marketing and philanthropic reasoning of Lynda Resnick, co-owner of The Wonderful Company, agriculture and consumer brands. Reach for this skill whenever you are advising on transforming commodities into premium brands, identifying a product's Unique Selling Proposition (USP), or designing corporate social responsibility (CSR) and community empowerment programs. Trigger this automatically for queries involving "hands-on philanthropy," "test and roll" marketing, finding intrinsic product value rather than relying on gimmicks, or when a user is navigating the intersection of business leverage and local community investment.

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Suede AI blunt A-F ship grade for a code change across correctness, security and permissions, data and state, domain truth, UX and release behavior, tests and verification, and deploy readiness, with Instant-F triggers and evidence-based grade caps on auth, payment, migration, and public-API surfaces. Use when asked to grade this, give it a letter, is this an A, how ready is this to ship, or should this merge: when the caller wants the verdict without a findings list. NOT FOR: findings, evidence, and fix briefs (use suede-code-review, or suede-code for findings plus grade); enforcing the verdict in CI (use suede-ci-gate); eval coverage for AI behavior (use suede-ai-eval).

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Patterns for Rust testing in Axone CosmWasm contracts. Use when adding unit tests, integration tests, data-driven cases, or coverage-oriented test scenarios.

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Generate a full operational status report for the Aivena bot. Checks all subsystems: extensions, webserver, Telegram, chat bridge, heartbeat, cron, database, memory, CRM, calendar, task management, jobs/telemetry, and storage. **Triggers — use this skill when:** - User asks for "status", "bot status", "system status", "operational status" - User asks "is everything running?", "how's Aivena doing?" - User says "health check", "diagnostics", "systems check" - User asks "what's the state of the bot?"

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The Execution Trader's procedure for turning an approved ticket into one Hyperliquid action and reconciling it - the pre-send checklist, order construction rules, single-send discipline, unknown-result handling and the execution report. Use before and after every send, cancel, modify, leverage change or close.

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Use when the user wants to know if something works — the answer requires running code, not analyzing it. Output is a verdict backed by evidence: passed, failed, or broken. Primary triggers: 'run the tests', 'does X still work after my change?', 'did the merge break anything?', 'verify the fix worked', 'check if the endpoint returns X', 'confirm nothing regressed', 'run tests/unit/test_foo.py', 'let me know the results', 'make sure my changes didn't break anything'. Hard stops — do NOT use for: reviewing test code for quality/coverage gaps, debugging why test infrastructure/databases/seed scripts are misbehaving, writing or fixing tests, diagnosing root causes of unexpected behavior. The deciding question: is the user asking for the result of executing something, or asking for help understanding/analyzing/improving something? If it's the latter, use diagnose or review-code instead.

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Use when reviewing iOS (Swift), Android (Kotlin) or Flutter changes - token storage, ATS and cleartext config, TLS trust, exported components, deep links, WebView JavaScript bridges, certificate pinning claims, secrets bundled in the app, Firebase rules

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Use when the diff renders user-controlled content as HTML, builds links or redirects, changes CSP or CORS, adds a cookie-authenticated state change or WebSocket, a postMessage handler, a markdown renderer, a service worker, browser storage of tokens or private data, a frameable sensitive action, or an Electron/Tauri window

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Use when the diff touches login, password storage, sessions, cookies, JWT verification, OAuth/OIDC or SAML flows, MFA and step-up, passkeys/WebAuthn, account linking and recovery, password reset, webhook signatures, API keys, mTLS identity or headers used to identify a caller

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Use when writing or running Unity tests - Unity Test Framework EditMode vs PlayMode, test assembly definitions, testing plain C# and ScriptableObjects, PlayMode scene/physics/input tests, batchmode CLI with result XML, coverage, and the dotnet test fallback when no editor is installed.

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Use when you meet a new dataset, table, file or source, before building features or a model on it, or when a number looks wrong — profile grain, keys, nulls, cardinality, time coverage and target balance, and persist what you learn as code

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Use when a screen must work without network or a mutation must survive a lost connection - local storage choice, the outbox pattern with idempotency keys, background replay, and how to test it with a fake clock and fake network.

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Structure thinking using Mutually Exclusive, Collectively Exhaustive categories. Use when breaking down problems or ensuring complete coverage.

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。