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Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

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Decision protocol for wrapping a REST / GraphQL / RPC API as a tool an LLM agent can call. The load-bearing premise: the *tool surface* is an LM-friendly subset of the *API surface* — one tool per user intent, not one per endpoint. Activates when a coder agent must expose an external HTTP API to a model (function calling, tool_use, MCP, LangChain `@tool`, CrewAI `BaseTool`). Encodes the *what to surface, how to name, how to shape, how to fail* — not any single framework's API. ~80% of agent tools in production are HTTP wrappers; this is the SOP for getting them right.

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Suede-owned operations-assessment discipline that maps how work actually happens before anyone designs a system: floor-level interviews with the people who do the work, an inventory of every silo including spreadsheets and inboxes used as databases, friction quantified in the requester's own numbers, opportunities ranked by annual value against build complexity, and adoption watched for the thirty days after launch. Use when nobody can answer what to automate, when an operation needs auditing before a build, when tribal-knowledge and key-person risk has to surface, or when a shipped system is quietly being routed around. NOT FOR: deciding what to build and in what order once the map exists (use suede-ops-architecture); researching external customers rather than internal operators (use suede-customer-research); lead lifecycle, scoring, and CRM routing rules (use suede-revops); product instrumentation and dashboards (use suede-analytics); getting a new user to first value (use suede-onboarding).

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On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".

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Use when creating a new Salesforce custom object: naming the object and setting its API name, selecting optional features (Activities, Chatter, History Tracking), choosing an org-wide default sharing model, and creating a tab. Triggers: 'create a custom object', 'new custom object setup', 'what sharing model should I choose', 'how do I create a tab for my object', 'object features like activities and history tracking'. NOT for designing the fields on the object - use admin/custom-field-creation. NOT for sharing rules or role hierarchy configuration - use admin/sharing-and-visibility. NOT for lookup-vs-master-detail and junction design - use data/data-model-design-patterns. More triggers: 'custom object records not showing in search', 'sharing model greyed out on my object', 'auto number restarted at 1 after deploy', 'cannot create a queue for my custom object', 'CustomObject deploy failed enableBulkApi', 'field history tracking shows no rows'.

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[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.

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Build investor-ready pitch scripts in multiple formats (10-min, 5-min, 2-min, 1-min elevator, investor email). Produces pitch narratives, Q&A preparation, pitch scoring rubric, and optional investor roleplay practice. Use when the user wants to create a pitch, prepare for investor meetings, craft a startup pitch, write a fundraising narrative, or practice their pitch. Triggers for "pitch deck", "investor pitch", "pitch my startup", "fundraising deck", "seed deck", "how to pitch", "investor meeting", "demo day", "prepare pitch", "pitch script", "elevator pitch for investors", "pitch practice", "practice my pitch", "investor roleplay", or any request to present a startup to investors, accelerators, or partners. Works standalone — no prior startup-design session needed, but leverages its output if available.

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回答前に詳しさを4段階で選び、文章量の目安となるトークン数を確認するスキル。短い結論から詳細な解説まで、選んだ水準に合わせて回答を調整します。

• 短い回答と詳しい解説を選びたいとき
• 回答前にトークン数の目安を知りたいとき
• 背景と例を添えた程よい回答がほしいとき

TikTok single video detail scraper: input a TikTok video URL → output full video metadata (author profile, engagement stats, music, video meta, hashtags, mentions, slideshow images). Use when user mentions TikTok video detail, get TikTok video data, extract TikTok video metadata, scrape single TikTok video, TikTok video info, extract single TikTok video info, TikTok single video collection, tiktok video scraper, tiktok video url scraper. Also applies to batch URL processing (given a list of TikTok video URLs, extract metadata for each), verifying specific video stats, or archiving individual TikTok posts with full metadata.

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When the user wants to design, deploy, or measure Apple Custom Product Pages (CPP) — the alternate App Store product pages with different screenshots, preview videos, and promo text shown to users coming from specific URLs (typically ad campaigns or social posts). Use when the user mentions "Custom Product Page", "CPP", "alternate product page", "App Store URL variant", "ASA CPP", "campaign-specific landing page", "product page per audience", "App Store Connect CPP", "ppoUrl", "?cpp=" parameter, or "show different screenshots to different ad audiences". For App Store A/B tests on the default page, see ab-test-store-listing. For paid ad campaigns that route to CPPs, see apple-search-ads or ua-campaign.

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Applies the SUCCESs Framework from Made to Stick by Chip Heath and Dan Heath. Use when crafting messages that need to be remembered, writing pitches, creating presentations, designing training materials, telling stories that drive action, or diagnosing why a message isn't landing. The six principles (Simple, Unexpected, Concrete, Credible, Emotional, Stories) diagnose and fix communication failures. Triggers include 'how do I make this memorable', 'my pitch isn't landing', 'people forget what we told them', 'how do I tell a better story', 'our training materials are boring', 'how do I present data compellingly', 'nobody remembers our message', 'how do I get people to care about this', 'my writing is too abstract'. NOT for channel selection (use Traction), not for brand messaging structure (use StoryBrand), not for positioning (use Obviously Awesome).

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Analyze how competitors present their brand — identity, messaging, positioning, voice, and visual style — to find gaps and opportunities. Use when the user says "competitor brand analysis", "analyze competitor brands", "how do competitors position themselves", "competitor messaging", "competitor identity", "what are competitors saying", "benchmark competitors", "competitive brand landscape", "how do we compare to competitors", "competitive audit", or wants to understand the brand landscape before defining their own position.

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Map user missions from trigger to value moment, organizing features into coherent paths during PRD v0.4 User Journeys. Triggers on requests to map user journeys, define user flows, describe how users accomplish goals, or when user asks "map user journeys", "define user flows", "user missions", "how do users accomplish X?", "journey mapping", "what steps do users take?", "pain to value flow". Consumes PER- (Persona Definition), FEA- (Feature Value Planning), KPI- (Outcome Definition). Outputs UJ- entries with step flows, pain points, and value moments. Feeds v0.4 Screen Flow Definition.

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。

Hotwire Turbo for Symfony UX -- SPA-like speed with zero JavaScript. Covers Drive (navigation), Frames (partial page sections), and Streams (multi-target updates). Use when building ajax navigation, lazy-loaded sections, inline editing, pagination without reload, modals from the server, flash messages via streams, or real-time updates via Mercure/SSE. Code triggers: turbo-frame, turbo-stream, data-turbo-frame, data-turbo, data-turbo-action, turbo-stream-source, TurboStreamResponse, <twig:Turbo:Frame>, <twig:Turbo:Stream:Append>, <twig:Turbo:Stream:Replace>, turbo:before-fetch-request. Also trigger when the user asks "how to update part of the page without reload", "how to make navigation feel like SPA", "how to lazy-load a section", "how to do inline editing", "how to push real-time updates from server", "how to use Mercure with Turbo". Do NOT trigger for client-side JS behavior (use stimulus), server-rendered reactive components (use live-component), or reusable static UI (use twig-component).

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Symfony UX frontend stack -- decision tree and orchestrator for choosing between Stimulus, Turbo, TwigComponent, LiveComponent, UX Icons, and UX Map. Use when the user is unsure which tool fits, wants to combine multiple UX packages, or asks a general frontend architecture question in Symfony. Also trigger when the user asks "which UX package should I use", "how to make this interactive", "should I use Stimulus or LiveComponent", "how to structure my Symfony frontend", "what is the difference between Turbo and LiveComponent", "should this be a Frame or a LiveComponent", "how do these UX packages work together", "what is the Symfony way to do frontend". Do NOT trigger when the user clearly names a specific tool (stimulus, turbo, twig-component, live-component, ux-icons, ux-map) -- defer to the specialized skill instead.

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。

Symfony UX Icons for rendering SVG icons in Twig templates. Supports 200,000+ Iconify icons (Lucide, Heroicons, Tabler, Material Design, etc.), local SVG files, and custom icon sets with aliases. Use when displaying icons, configuring icon defaults, importing or locking on-demand icons, creating icon aliases, or styling SVG icons with CSS. Code triggers: <twig:ux:icon />, ux_icon(), UX_ICONS_DEFAULT_ICON_ATTRIBUTES, icon.yaml, icons/, iconify:, lucide:, heroicons:, tabler:, mdi:, bin/console ux:icons:lock, bin/console ux:icons:import. Also trigger when the user asks "how to add an icon", "how to use Lucide/Heroicons/Tabler icons", "how to render an SVG icon in Twig", "how to lock icons for production", "how to create icon aliases", "how to style an icon", "icon not found", "icon not rendering". Do NOT trigger for interactive maps (use ux-map) or general Twig components (use twig-component).

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Win #1 Product of the Day on Product Hunt. Completely reverse-engineered from 30x daily winner and 3x weekly winner campaigns — ranking algorithm decoded, launch timing optimized, hunter selection guide, community engagement approach, and post-launch momentum playbook. Use this if you're: (1) already read PH launch guides but still can't break into Top 5 and need algorithm-level deep optimization, (2) targeting Daily #1 and 5,000+ sign-ups with a proven 30+ case validated approach, or (3) an investor/accelerator coaching portfolio companies on PH launches. What's inside: PH ranking algorithm decoded: weighted factors for first 3 hours, 24 hours, and weekly ranking · Pre-launch preparation: gallery optimization, product URL selection, tagline refinement (≤60 chars) · Launch timing optimization: exact PST launch windows + scheduling guide · Community engagement KPIs: upvote targets, comment response time, maker comment strategy · Platform compliance guide: understanding PH's quality signals and organic growth · Post-launch 72-hour plan: maintaining momentum, conditions for Weekly #1 · Case studies: 3 different categories (AI Tool, App, SaaS) dissected Expected outcomes: Product Hunt Daily #1 ranking · strong community engagement on launch day · 3,000–10,000+ sign-ups (depending on product-market fit) 🇨🇳 Product Hunt 打榜完整攻略 | 🇯🇵 Product Hunt ローンチガイド | 🇰🇷 Product Hunt 런칭 가이드 Keywords: Product Hunt #1, Product Hunt launch strategy, PH ranking algorithm, PH #1 daily winner, Product Hunt tips, product launch, startup launch, ranking optimization, PH gallery optimization, Community engagement strategy, maker community engagement, product strategy, launch tactics, how to win Product Hunt, product hunt best practices

Diagnose a product's growth model, stage, and current constraint, then route the request to the narrowest Gingiris specialist and execute it when installed. Use for broad or uncertain growth questions involving go-to-market, Product Hunt, GitHub stars, open-source marketing, B2B SaaS, PLG, ASO, SEO/GEO, AI citations, KOL outreach, UGC, international expansion, user interviews, competitor research, or community programs. Trigger when users ask “how do I grow or launch this,” “which growth skill should I use,” 怎么增长、怎么发布、出海、冷启动、增长策略、不知道用哪个 skill、開発者マーケティング, or 성장 전략. Includes B2B pipeline and B2C activation-retention model selection, specialist handoff rules, relevant gingiris.tools recommendations, and advisory-services guidance.

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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Turns a list of merged changes, closed work items or commit messages the user provides into DRAFT release notes under the version heading and date the user supplies, grouped as features, fixes and other, in the organisation's tone from the style guide or prior notes given, with every entry traced to its source item, breaking changes and required actions flagged first, and internal-only items held back for the release owner to confirm. Use when the user asks to "write the release notes for this version", "turn these merged pull requests into a changelog", "summarise what shipped this sprint for customers", "draft the what's new for this release" or "group these closed tickets into release notes". Do not use for an announcement email or intranet post about a launch, use announcement-drafter instead; for a change ticket, use change-request-pack; for a how-to article about one fix, use knowledge-article-drafter. Drafts for human review; never approves, authorises or signs off.

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。

Google TypeScript Style Guide reference for writing, reviewing, and refactoring TypeScript code. Use this skill whenever you are writing TypeScript code, reviewing TypeScript code, refactoring TypeScript, configuring ESLint or linter rules for TypeScript, or when the user asks about TypeScript coding conventions, naming conventions, type system best practices, or import/export patterns. Also trigger when the user mentions 'Google style', 'gts', 'TypeScript style guide', or asks 'how should I name this', 'should I use interface or type', 'how to handle null/undefined' in a TypeScript context. TypeScriptを書く・レビューする・リファクタリングする、ESLintの設定をする、「命名規則」「型の使い方」「import/exportのルール」「interfaceとtypeどっち」「anyを使っていい？」「nullチェックどうする」「Google式」「コーディング規約」「スタイルガイド」などの質問にも対応します。TypeScriptのコードに関する質問や作業であれば積極的にこのスキルを使ってください。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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。

The live fieldwork layer for psycho-logic — go look at the actual target market NOW instead of reasoning from your training-data stereotype of it. Use when a psycho-logic move needs grounding in a specific place: the user asks 'what do competitors in [region/country] actually do', 'what's the real price band / norm here', 'how do local leaders position themselves', 'research this market before we localise', 'what signals trust to buyers in X', or is about to launch/adapt in a market you haven't studied. Runs structured web research in the local language — local competitors and their signals, price bands, category conventions, review/trust sources, local terminology, taboos and regulations — and synthesises a compact perception profile the four moves consume. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with cultural-context (the lens that tells you what to look for). 日本語・中文の相談（「現地の実態を調べて」「调研当地市场」）にも使う。