outreach-research

Look up a person and get their verified phone numbers, email, and company context. Use when the user says "look up [name]", "get me the contact info for [person]", "find [name]'s phone number", "who is [name] at [company]", "enrich [email or name]", or any request to retrieve a single person's contact details.

Find companies or contacts similar to a set of references, then enrich results with verified phone numbers. Use when the user says "find companies like my best customers", "find more contacts like these", "expand from these accounts", "who else looks like [company]", "find similar companies to [list]", or any request to discover lookalike targets from a reference set. Requires at least 5 reference companies or contacts for quality results.

Drafts personalized B2B outreach (Email 1, optional Email 2 and 3, and LinkedIn copy) for contacts the user already has, from an outreach-research positioning brief plus Lusha buying signals. Use when asked to draft outreach, write emails or LinkedIn messages for a contact list or CSV, or personalize a sequence ("lusha outreach sequence", "write email 1 for this list"). Not for building the brief: use outreach-research first.

Build a targeted list of contacts or companies from Lusha and return verified phone numbers alongside emails. Use when the user says "find me [title] at [company type]", "build a list of [ICP description]", "prospect [criteria]", "who should I be calling at [industry]", or any request to generate a lead list from an ICP or persona description.

Save Lusha contacts or companies from this conversation to a Lusha Workspace table, or export them to the user's CRM (Salesforce, HubSpot and other supported CRMs). Use when the user says "save these to a table", "add them to my Lusha list", "put this in a Workspace table", "export to HubSpot", "push these to Salesforce", "send to my CRM", "sync these leads", or picks the save or export next step at the end of another Lusha skill. Works on Lusha ids already in the conversation; to find new people or companies, use prospect, signal-prospect or lookalike-prospect first.

Score and rank a list of companies or contacts the user already has (a CSV, a pasted list of domains or names, or a Lusha Workspace table) by fit to their ICP and by live buying signals, then split it into A, B and C tiers. Use when the user says "prioritise this list", "score these accounts", "rank my target list", "which of these companies should I work first", "tier my accounts", "which of these leads are hot", or brings a list and asks where to start. Not for finding new companies: use prospect, signal-prospect or lookalike-prospect for that.

Set up the user's Ideal Customer Profile (an ICP Target Profile in Lusha) by asking the profile questions one at a time, drafting each answer from what is already known about their company and customers, then creating the profile and previewing 10 matching accounts. Use when the user says "set up my ICP", "define my ideal customer", "who's my ideal customer", "create a target profile", "teach Lusha who I sell to", "my recommendations are off", or when another Lusha skill sends them here because no profile exists. Not for a one-off search by criteria: use prospect.

Find companies or contacts triggered by a buying signal, then surface verified phone numbers for the right decision makers. Use when the user says "find companies that just raised funding", "who's surging in sales hiring right now", "show me companies with headcount growth", "find contacts who just got promoted", "who changed jobs recently in [industry]", "show me [title] at companies that just [signal event]", or any request that starts with a real-world trigger rather than a static filter.

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.

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 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.

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.