「brain」の検索結果

Provider-neutral deep-research orchestration for brainstorming, technology choices, comparisons, and state-of-the-art surveys. The Research Lead makes scope and decision calls while native subagents scout, search, verify, synthesize, audit, write, and commit every research artifact.

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Think and work like an expert Computational Neuroscientist. Use when a task calls for Computational Neuroscientist judgment. Reasons from encoding/decoding, GLM/LNP spike-train likelihood, mean-field E-I balance, and neural manifolds through NEST/Brian/NEURON/BMTK, GPFA/LFADS, Brain-Score alignment, and trained-RNN reverse engineering while treating spike-sorting contamination, model non-identifiability, nested-CV leakage, and task-optimization≠mechanism as first-class failure modes.

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Think and work like an expert Distributed Systems Researcher. Use when a task calls for Distributed Systems Researcher judgment. Reasons from failure models, consistency contracts, and tail-latency-and-recovery performance through TLA+ model checking, Jepsen and Porcupine linearizability checking, and YCSB/DeathStarBench benchmarking with iptables and kill -9 fault injection, while treating unbounded leases and split-brain, clock skew under NTP, and GC-induced p99 spikes as first-class failure modes.

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A React Native developer-platform whose marketing site reads like a quietly-confident infrastructure brand. The base canvas is pure white with a soft sky-blue gradient atmospheric wash behind the hero; near-black ink (`#171717`) carries body and display alike. The single brand voltage is **pure black** (`#000000`) for primary CTAs — minimal and editorial-feeling, paired with a small blue text-link accent (`#0d74ce`) reserved for inline body links. Type pairs Inter at modest weights (display 600, body 400) with JetBrains Mono on every code surface. The brand's strongest visual signature is the **device-mockup hero** — a centered MacBook + iPhone composite showing real Expo dev surfaces — over the gradient sky wash.

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Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.

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Use when starting creative work (features, components, functionality, or behavior changes) to clarify user intent, requirements, and design before implementation.

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Test payment and checkout flows end to end against PSP sandboxes — Stripe first, with the general pattern for Adyen/Braintree/PayPal. Covers Stripe test-mode card numbers and their decline codes, the 3DS/SCA challenge flow and its nested-iframe handling in Playwright, test clocks for subscription/billing-cycle simulation, webhook testing (stripe listen/trigger, signature verification, idempotency), failed/retried payments and refunds, and never using real cards. Use when: "test Stripe checkout," "payment test," "3DS test," "test webhook signature," "test subscription renewal," "test clock," "refund test," "decline card test," "checkout E2E." Not for: General API contract testing of non-payment endpoints — api-testing. PCI-DSS/regulatory compliance audit — compliance-testing. Related: api-testing, playwright-automation, compliance-testing, test-data-management, qa-project-context.

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Use when the user has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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Render Rove's recent releases as a self-contained, Rove-styled HTML page (Chinese by default). Reads shipped notes from packages/rove/CHANGELOG.md + git tags, filters to user-facing changes, and emits one standalone .html file in the `claude` theme (terracotta on near-black, JetBrains Mono, terminal chrome). Use when the user asks for a "release page", "发版速览", "recent release html", "把发版做成网页", or wants to share/post what changed.

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Use when the user has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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Continue is an open-source AI code assistant for VS Code, JetBrains and the terminal (the cn CLI) that works with any LLM: Claude, GPT, Gemini, Ollama and other local models. Use when a user asks to configure Continue with config.yaml, add chat or tab autocomplete models, write rules or custom prompts, connect MCP servers, run cn headless in scripts, or migrate from config.json. Continue was acquired by Cursor in June 2026: the release from that month is final and the Hub is offline, so the skill covers local, self-configured use.

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Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.

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Most startup ideas are bad. Not because founders lack creativity, but because they're solving invented problems instead of lived ones. Paul Graham's essays reveal a pattern: the best ideas come from living in the future and noticing what's missing. Schlep blindness hides gold. Organic ideas beat brainstormed ones. Frighteningly ambitious ideas are often easier. This skill synthesizes PG's idea-finding wisdom into actionable guidance. Use when "keywords, contexts, " mentioned.

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Start a feature: brainstorm a spec, get it approved, then drive it test-first through the pipeline. The one entry to implementation.

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The Socratic spec-refinement front of /feature, and the planning front of /sprint. Routed to BEFORE any code — it turns a one-line idea into a concrete spec with testable criteria, ready for initial combined sprint review or explicit sequential approval. Five gated phases — frame, shape, refine, write, review-and-approve. No implementation before an approved spec; the caller retains required planning, execution and delivery ordering. Each acceptance criterion becomes one tdd Phase 1 obligation.

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Mandatory pre-code gate and definition-of-done for ANY Odoo change (add field, override method, inherit view/xpath, OWL/JS patch, wizard, cron, controller, report, security, migration, bug fix, refactor, "optimize", "clean code", "stick to base"), Odoo 16-19. Also when the user pastes an Odoo traceback or install/upgrade error (ParseError "cannot be located in parent view", "Field ... does not exist", missing xmlid) or asks where an Odoo model, field, method, view or xmlid is defined. Run BEFORE brainstorming or planning: Odoo design questions are answered by tracing the real source into a Context Brief (file:line), not by open-ended Q&A or memory. Resolves the Odoo runtime and version, loads the matching odoo-<major> pack, and closes with install, test, upgrade, i18n, security and hygiene checks before commit.

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Use when code needs a judgment about natural language that rules or regex cannot make (classify, route, triage, moderate, score, rank, match, dedupe, filter, extract, verify, or gate an action), when brainstorming where AI could fit in an app, when an LLM call exists only to pick a label, score, or yes/no, or when code uses TypeSafe AI, Jev, System One, typesafe-sdk, or @typesafe-ai/sdk.

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When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth ideas,' 'how to market,' 'marketing strategies,' 'marketing tactics,' 'ways to promote,' 'ideas to grow,' 'what else can I try,' 'I don't know how to market this,' 'brainstorm marketing,' or 'what marketing should I do.' Use this as a starting point whenever someone is stuck or looking for inspiration on how to grow. For specific channel execution, see the relevant skill (ads, social, emails, etc.).

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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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Read the feature roadmap, find the next unplanned phase, brainstorm it, and update the roadmap with the resulting document name

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Check product and brand names for conflicts across trademarks, domains, social handles, and package registries. Returns a risk level and Proceed/Modify/Abandon verdict. Don't use for name brainstorming, logo design, or trademark filings.

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Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Use this to channel the thinking of Virginia M.-Y. Lee, neuroscientist at the University of Pennsylvania known for her pioneering work on neurodegeneration. Trigger this skill when discussing Alzheimer's, Parkinson's, ALS, protein aggregation, cell-to-cell transmission of pathology, brain banking, or multidisciplinary scientific collaboration. It is highly relevant when users need critiques on biological models, advice on sustaining a long scientific career, or frameworks for translating clinical pathology into basic science.

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Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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