「sources」の検索結果

SEO/GEO Agent Operations SOP v2 — Rebuilt from real-world operations (analook.com 0→launch, GSC incident, DataForSEO monitoring, gingiris.tools post-migration recovery). What's new in v2: • Step-by-step onboarding (Q1→Q6), multiple choice wherever possible — no big forms • Mandatory daily SEO log — first item is always GSC token status check • Data cross-validation rule: any external metric must come from ≥2 sources before use • Backlink progress tracking (6-week wait → check → alert if not indexed) • New vs. established site strategy matrix (new site <6mo: KD≤20; mature: KD up to 35+) Real incidents baked in: GSC token expired silently for 2 weeks (analook.com), P0 keywords dropped out of Top 100 after domain migration (gingiris.tools), traffic data off by 17× from single-source DataForSEO. Triggers: "SEO agent" | "SEO daily report" | "GSC monitoring" | "DataForSEO" | "GEO agent" | "backlink tracking" | "IndexNow" | "competitor comparison page" | "SEO SOP" | "autonomous SEO" | "GEO triple combo" 🇨🇳 SEO/GEO Agent 运营 SOP v2 — 基于真实运营经验重写（analook.com 从零建站 + GSC 断档 2 周 + DataForSEO 监控 + gingiris.tools 迁移权重恢复）。 对话式开局、强制日报（GSC token 是第一项）、数据交叉验证铁律、外链进度追踪、新老站区分策略。

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Maintainer-only skill for refreshing VitaeContext wiki knowledge from official sources. Use only from a local repository clone when a maintainer asks to refresh one module, audit all module wiki entries, or audit module source lists.

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Use when the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

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Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.

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Write essays, articles, and blog posts in Paul Graham's voice — plain, spoken prose that reasons out loud and lands surprising true claims, not AI filler. Use when asked to write or rewrite something "like Paul Graham", "in PG's style", or to make a piece read like a person thinking on the page rather than generated text. Built from a stylistic model of 18 of his essays (see SOURCES.md).

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Use when reviewing Spring Boot 4 / Java 17+ code with concrete files or diffs — pull requests, modules, or pasted Java/Spring sources — for migration risks, architecture boundary leaks, JSpecify null-safety gaps, security flaws, performance regressions, or Spring Data pitfalls. Not for Kotlin-only code, non-Spring frameworks, or generic review advice without code context.

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Retrieves and analyzes FHIR Coverage and Organization resources to produce a structured insurance summary with coordination of benefits analysis. Use when user asks to "check insurance", "coverage details", "verify coverage", "insurance summary", "what plan is the patient on", "primary vs secondary insurance", or "coverage status". Do NOT use for clinical data, demographics beyond insurance, or billing claim submission.

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Generate Coverage and insurance resources with payer diversity including Medicare, Medicaid, commercial, Tricare, VA, and self-pay. Use when user mentions coverage, insurance, payer, Medicare, Medicaid, benefits, or coordination of benefits. For claims and EOB see the claims-eob skill.

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Evaluates environmental liability exposure with remediation cost estimation, regulatory compliance requirements, and insurance coverage assessment. Use when analyzing environmental liabilities, estimating cleanup costs, or assessing environmental risk.

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Generate Claim, ClaimResponse, and ExplanationOfBenefit (EOB) resources for healthcare billing including professional, institutional, pharmacy (Rx), dental, and vision claims. Use when user mentions claims, EOB, explanation of benefit, billing, reimbursement, adjudication, pharmacy claims, Rx claims, denied claims, copay, deductible, or allowed amount.

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Adaptive conversion of LaTeX, PDF, or Markdown sources into a complete Word .docx that follows a user-supplied .docx template. Use when pandoc --reference-doc alone is not enough — for thesis, dissertation, report, or institutional Word formatting that needs cover pages, declarations, TOC, heading numbering, captions, three-line tables, equations, citations, and visual verification.

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Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".

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Build production-grade infrastructure as code for a service or project. Use when asked to "set up infra", "provision infrastructure", "create cloud resources", "IaC for this project", "terraform for this", or "deploy this service".

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User segmentation and persona creation from mixed data sources — analytics, CRM, support tickets, reviews, or any combination. Use when asked to "build personas", "who are our users", "segment our users", "create user profiles", "define user archetypes", or "who is the target user".

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ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".

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Google Cloud Platform CLI - manage GCP resources. Use when working with Compute Engine, Cloud Run, GKE, Cloud Functions, Storage, BigQuery, or other GCP services.

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Lighthouse audits and Core Web Vitals — LCP, CLS, INP. Use when optimising page load, layout shift, input delay, images, fonts or render-blocking resources.

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Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.

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Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall, OpenAlex), and (3) a browser-based fallback for paywalled publishers without TDM access (ACS, RSC, IEEE, AIP, IOP, APS, Annual Reviews, T&F, Chinese journals). Browser fallback offers two routes — Route A drives the user's logged-in Chrome via the OpenClaw `browser` tool with `profile="user"` (best for interactive sessions), Route B uses the standalone `auto-paper-harvester` CLI with its built-in Playwright (best for unattended bulk runs). Use when the user wants to harvest, scrape, fetch, or bulk-download papers from a DOI list, savedrecs export, or Excel; or wants to fill missing full-text PDFs for an existing literature collection. Triggers on phrases like "批量下载文献", "下载全文", "harvest papers", "scrape full text", "TDM API", "下载 Elsevier 全文", "Wiley 批量下载", "下载 PDF".

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Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.

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。

Provides Infrastructure as Code best practices for Terraform, Pulumi, CloudFormation, and OpenTofu. Use when provisioning infrastructure, writing IaC modules, managing cloud resources, scanning for misconfigurations, or when user mentions 'terraform', 'pulumi', 'cloudformation', 'IaC', 'opentofu', 'infrastructure', 'tfsec', 'checkov', 'drift'.

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Primary and secondary source analysis for historical reasoning. Covers source classification, sourcing (author/context/purpose), corroboration across multiple sources, contextualization within time and place, and bias detection. Use when evaluating historical evidence, assessing source reliability, or constructing evidence-based historical arguments.

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。

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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。

Media literacy and critical analysis of media messages across platforms and formats. Covers media ecology (McLuhan), encoding/decoding (Hall), news literacy, digital media analysis, propaganda techniques, algorithmic curation, visual rhetoric, and ethical media consumption. Use when analyzing media messages, evaluating news sources, understanding how media technology shapes communication, detecting propaganda, or studying the relationship between medium and message.

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。