account-health

Prepare for an upcoming meeting with a company. Identify the account by ZoomInfo account/company ID (preferred) or by company name, domain, or ticker (which triggers a lookup step) — or name no account at all and let the skill pick the meeting from your upcoming calendar. Provide attendee names or emails and rich context on the meeting purpose, stakes, and known dynamics to get a tight, decision-ready brief — headline, relationship posture per attendee, prior-conversation context (open threads and unresolved questions from past calls and emails), ranked talking points, discovery questions, suggested agenda, and what NOT to do. Use when prepping for a sales or customer call, QBR, renewal, or demo. Optimized for a 30-min slot.

Generate 1-3 personalized email variants for a single prospect. The composition bar adapts to the use case — cold_outbound demands a signal → pain → positioning chain; follow-ups / recaps / renewals lean on prior context and next-step framing. Supports cold_outbound, discovery_follow_up, demo_recap, re_engagement, renewal, expansion, objection_handling. Returns subject lines and mobile-readable bodies with a rationale chain. Use for sales prospecting, lead generation, account-based selling, buyer-intent-driven outreach, B2B prospecting. Triggers on phrases like "write a cold email", "personalize an email", "draft outreach", "follow up on this prospect", "email this prospect", "outbound to X", "send a chaser".

Track the open objections and blockers on a deal and how they have evolved across recent calls. Uses conversation_intelligence over an account's or contact's recent engagements. Identify the account or contact by ZoomInfo ID (preferred) or name (triggers a lookup). Use when someone asks "what objections are still open with Acme", "what's blocking this deal", "how has the security concern evolved", or wants a blocker check before a forecast or a next call. Reasons over the last few engagements only, so it tracks recent movement rather than full deal history.

Recommend the next best action on a deal or relationship, grounded in the current conversation state. Uses conversation_intelligence as the primary source, with account_research for deal context. Identify the account or contact by ZoomInfo ID (preferred) or name (triggers a lookup), or point it at a specific engagement. Use when someone asks "what should I do next with Acme", "what's my next move here", "how do I advance this deal", or wants an evidence-based recommendation rather than a generic playbook. Reasons over the last few engagements only.

Build a mutual action plan (MAP) from what was actually discussed on recent calls — the shared, dated set of steps each side owns to reach the goal. Reviews recent engagements and goes deep with conversation_intelligence scoped to individual calls to pull open items, agreed next steps, owners, and the timelines discussed. Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup). Use when someone says "build a mutual action plan for Acme", "turn our last calls into a MAP", "what are the next steps and dates on both sides", or needs a close or onboarding plan. Evidence-based: it plans only what was discussed and asks for the target date if one was not stated.

Produce a succinct one-pager for an executive dropping into a call with zero context. Blends account_research (who they are, deal state), conversation_intelligence (what has been discussed and what is open), and contact_research (who is in the room). Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup); name the key attendees if known. Use when someone says "brief the VP for the Acme call", "exec one-pager for tomorrow", "my SVP is joining cold", or needs a tight pre-call brief for a senior stakeholder. Built to be read in two minutes.

Look up a person's full professional profile. Provide a name and company, email address, phone number, or ZoomInfo person ID. Returns title, department, contact details, accuracy score, and company info.

Assemble a QBR prep pack for a business review — value moments, usage and adoption themes, risks, and open items, with a deal and relationship snapshot. Combines account_research for the account picture with conversation_intelligence over recent calls (scoped to individual engagements) for what the customer actually said. Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup). Use when someone says "prep me for the Acme QBR", "build a QBR pack", "what should I cover in the business review", or needs a review-ready summary. Evidence-based: value moments, themes, and risks come from the conversations and CRM, not from assumption.

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.

Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

Search, scrape, crawl, extract structured data, and monitor web pages via the ScrapeGraph AI CLI. Use when the user asks to search the web, scrape a webpage, grab content from a URL, extract JSON from a site, crawl documentation or site sections, monitor a page for changes, inspect request history, check ScrapeGraph credits, or validate API setup.

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps,' 'cold call script,' or 'voicemail script.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.