「fact」の検索結果

Scaffold and operate Claude Blog Brain, a source-cited Obsidian brain for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill. Use when the user says "claude-blog-brain", "Claude Blog Brain", "create a blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill brain", "import sources", "synthesize plan", "render report", or wants a persistent vault-backed operating system for blog content creation, optimization, and management dual-optimized for Google rankings (E-E-A-T, the 2026 core updates) and AI citations (GEO/AEO), spanning writing, rewriting and freshness, SERP-informed briefs and outlines, editorial calendars and strategy, semantic topic clusters, schema and internal linking, multilingual publishing, the FLOW framework, factchecking, personas, distribution, and the blog delivery contract, grounded in the claude-blog skill.

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Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".

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Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.

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Perform cross-artifact consistency and coverage analysis across constitution, specification, plan, and task artifacts to detect gaps, conflicts, and misalignments before implementation.

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

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Run Chain-of-Verification (CoVe) on a draft or a block of text with factual claims. Spawns the `claim-verifier` agent in a fresh context (never a conversation fork) so it never sees the draft — then reports which claims are supported, contradicted, or unverifiable. Use when user says "verify these citations", "check the claims in X", "did I hallucinate anything", "fact-check this draft", "run CoVe on this", or after any text generation that asserts facts about papers, datasets, or numerical results. NOT for style/grammar review (use `/proofread`) or substance review (use `/review-paper`).

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Prove that the file you are about to send, publish, or hand off IS the thing you mean — before it leaves your hands. Rebuild from source, check integrity, diff the derived artifact against its source, and require the recipient to echo what they received. Use before sending a paper/PDF for review, shipping a release or replication package, handing files to a coauthor or collaborator, uploading anything to an external reviewer or model, or publishing. Prevents an entire review/QA cycle from being spent on defects that exist only in the artifact, not in the work.

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Decode a car lease offer — the money factor converted to APR, the cap-cost math, mileage and disposition traps, and what to negotiate. Use when asked to decode my car lease, is this lease deal good, what's a money factor, or review this lease before I sign. Produces the real-numbers decode (money factor → APR, total lease cost), the trap list with dollar exposure, and the negotiation points dealers expect to concede.

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Extract a brand's visual and verbal identity into an applicable guideline kit — tokens, voice rules, and do/don't pairs — then apply it consistently to any artifact. Use when asked to apply brand guidelines to a document/deck/page, to extract a brand kit from existing materials or a website, to keep AI-produced artifacts on-brand, or to write lightweight brand guidelines for a startup. Produces a compact brand kit (visual tokens + voice rules + application examples) and/or an artifact restyled to it. For a creator's personal voice use creator-brand-kit; for building new UI systems use frontend-design.

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Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.

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当你为《电子学报》(Acta Electronica Sinica, AES) 投稿准备代码、数据与硬件设计等研究制品(artifact)的可用性说明时使用。中文学报暂无独立的 artifact evaluation 徽章委员会，本技能说明本刊现状、如何自愿开放代码/数据以增强稿件可信度、如何在正文与附录中规范披露制品、如何在双盲阶段匿名托管仓库、以及电子/信号处理类工作(算法、电路、FPGA、数据集)的制品打包要点。帮助作者在没有强制徽章的前提下最大化《电子学报》稿件的可复现说服力。

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。

Refactor code to improve structure and maintainability

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Manufacturing and fabrication workflow for KiCAD projects via MCP tools. Triggers on: "send to fab", "order boards", "gerbers", "JLCPCB", "manufacturing", "export for production", "pick and place", "assembly files", "generate fabrication outputs", "BOM for fab", "production files", "fab house".

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World-class prompt powerhouse that generates production-ready mega-prompts for any role, industry, and task through intelligent 7-question flow, 69 comprehensive presets across 15 professional domains (technical, business, creative, legal, finance, HR, design, customer, executive, manufacturing, R&D, regulatory, specialized-technical, research, creative-media), multiple output formats (XML/Claude/ChatGPT/Gemini), quality validation gates, and contextual best practices from OpenAI/Anthropic/Google. Supports both core and advanced modes with testing scenarios and prompt variations.

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Adversarial verification of AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when a user asks to verify, fact-check, red-team, validate sources, or quality-control a legal document, briefing, compliance summary, or regulatory analysis before it is distributed to clients, stakeholders, or published. Trigger phrases include "verify", "fact-check", "red team", "red-flag", "check accuracy", "validate sources", "quality control", "is this correct", and "review for errors". Produces a structured verification report with severity-categorized errors, verified sources, unsupported claim list, missing disclaimers, and an explicit distribution-readiness assessment.

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When your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit packet with: SHA-256 chain of evidence (source files + matter.yml + active disclosure pack + verifiable artifact self-hash), attorney identity and signature block, a bar-opinion disclosure pack with starter language for five jurisdictions, and content-aware deterministic narratives derived from filename and tool shape — never from prompt text by default. The tool refuses to bill on its own. --strict mode refuses to ship the artifact if any audit invariant fails (broad routes, missing attorney, missing/unverified disclosure). Comes as a Node CLI and a self-contained browser version (no backend; JSONL never leaves the page). 15 invariant tests verify the contract. AGPL-3.0.

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Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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Generate test fixture factories for your models. Builder pattern and static factories for zero-boilerplate test data. Use when tests need sample data setup.

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Pre-refactor safety checklist. Verifies test coverage exists before AI modifies existing code. Use before asking AI to refactor anything.

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 knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring IR/SSIS/SHIR, mapping data flows, SAP/SaaS connectors, CI/CD deployments, or secure VNets/Key Vault, and other Azure Data Factory related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics), Azure Databricks (use azure-databricks).

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Expert knowledge for Azure Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, .npmrc auth, GitHub Actions CI/CD, or package retention limits, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Boards (use azure-boards).

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Expert knowledge for Azure Artifact Signing development including decision making, security, configuration, and integrations & coding patterns. Use when handling cert lifecycles, RBAC for signers, DGSSv2 migration, diagnostic logs, or CI/CD signing integration, and other Azure Artifact Signing related development tasks.

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Judge REM memory relations, rewrites, verification, and clause carry by meaning. Use for REM update model calls that decide which older facts are superseded, which facts remain current, and whether a proposed rewrite preserves all surviving facts.

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Shared artifact registry, common artifact patterns, and integration validation. Load when tracking data that flows across journey steps or validating horizontal coherence.

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。