Boost Your Prospecting with askClaude - Smart Summaries & Contextual Scoring in Google Sheets

You’ve got data. Leads. Lists. But you’re missing the critical piece: the ability to quickly interpret, summarize, and act on it.

That’s exactly where askClaude comes in - a Derrick App feature that brings Anthropic’s powerful Claude language model directly into Google Sheets. The result? Reasoning, summarization, and prioritization at scale, without leaving your spreadsheet.

This playbook shows you three real-world use cases where askClaude shines - especially when context matters.

⚙️ Derrick Features Used in This Playbook

• Search Leads: Find LinkedIn profiles from first name, last name, and company.
• Lead & Company Enrichment: Pull all public LinkedIn data automatically.
• askClaude: Ask structured questions, get sales-ready, human-sounding answers instantly.

What "ask Claude in a spreadsheet" actually does

Asking Claude inside Google Sheets means running an AI model on each row of your data, the same way a formula runs on each cell. You write a prompt once, point it at the columns you want Claude to read, and Derrick fills a new column with Claude's answer for every line. No copy-paste into a chat window, no scripts, no API plumbing. The work happens where your data already lives.

In practice, that unlocks six everyday jobs on a list of leads or companies:

• Categorize: tag each row (industry, ICP fit, seniority bucket, intent level) so you can filter and route.
• Summarize: turn a long enriched profile or company description into a two-line brief.
• Extract: pull a specific fact out of messy text (a job title, a location, a tech mentioned in a bio).
• Clean: normalize inconsistent values (job titles, company names, country formats) into something usable.
• Generate: write a first-line icebreaker, a one-sentence value prop, or a subject line tailored to the row.
• Score and prioritize: rank a row against your ICP and explain the reasoning, so reps work the best contacts first.

Claude is well suited to the first five because they reward reading comprehension and judgment, not just pattern matching. The three deep playbooks below show summarizing and scoring in full. First, here is how the feature itself works inside Derrick.

Here is what that looks like in one row. Say column A holds a LinkedIn URL and Derrick's enrichment has filled column B with the full profile text: headline, current role, past roles, and the about section. You add an askClaude column C with the prompt "Summarize this person's role and likely priorities in two lines, then suggest one outreach angle. Profile: [B]". When you run the sheet, column C fills with a tailored brief for every line. The same pattern works for a thousand rows as for one, which is the whole point: you write the instruction once and Claude applies your judgment at list scale.

That is the mental shift worth making. A spreadsheet formula transforms data mechanically; askClaude reads it and reasons about it. Anywhere you would otherwise open each profile, think for thirty seconds, and type a note, you can encode that thinking as a prompt and let Claude run it across the column while you focus on the replies.

How askClaude works inside Derrick

askClaude is the Derrick feature that runs Anthropic's Claude on your spreadsheet rows. You add an askClaude column, write your prompt, and reference other columns by name so Claude reads the right data for each line. Derrick handles the connection to Claude, so there is no API key to manage and nothing to install beyond the Google Sheets add-on.

Each askClaude row costs 2 credits per line, billed per request. That means cost scales with how many rows you run, not with a flat subscription, so you can test a prompt on ten rows before committing a list of a thousand. Pair it with Derrick's enrichment so Claude always reads complete, current LinkedIn data rather than whatever was in your sheet last quarter.

A typical pipeline looks like this: enrich your leads first, then point askClaude at the enriched columns. Claude is only as good as the context you feed it, so the enrichment step is what turns a thin row into a useful one. If you want the model trade-offs, the askOpenAI playbook covers the same workflow with OpenAI models, and the AI data enrichment hub maps when to reach for which.

🧠 Use Case #1 - Summarize a Lead Profile

Best for: SDRs and AEs who want to quickly understand a prospect before outreach.

Goal:

Get a clean, actionable summary of a LinkedIn profile to prep your message or call.

How to do it:

1. Search Leads → Input First Name, Last Name, Company → Get LinkedIn URL
2. Enrich Lead → Input LinkedIn URL → Pull all available public info
3. askClaude → Sample prompt:

Summarize this profile in 3 bullet points: 1) Current role, 2) Focus areas or interests, 3) Outreach angle that could resonate. Info: [enriched content here]

Expected output:
A short, natural-language summary ready to use in an icebreaker or CRM.

🏢 Use Case #2 - Summarize a Company Profile

Best for: Sales teams prepping for intro calls with minimal time.

Goal:

Get a structured company profile based on enriched LinkedIn data.

How to do it:

1. Search Companies → Get the company’s LinkedIn page
2. Enrich Company → Pull company size, description, sector, etc.
3. askClaude → Sample prompt:

Summarize this company for a sales discovery call: 1) What they do, 2) Team structure (sales, marketing, product), 3) Possible entry point for a multichannel prospecting tool. Data: [enriched content here]

Expected output:
A concise briefing to help you qualify the account and steer your pitch.

🧲 Use Case #3 - Pick the Right Lead in a Target Account

Best for: Reps working strategic accounts with multiple potential contacts.

Goal:

Compare multiple leads at the same company and identify the best fit based on your target persona.

How to do it:

1. Import a list of leads from the same company
2. Enrich them to pull job titles, experience, and descriptions
3. Brief Claude with your ideal persona, ex:

You’re a prospecting expert. Here are 4 leads from the same company. Based on this ICP: "Head of Growth or Sales Ops, interested in multichannel prospecting tools, results-driven", choose the best-fit contact and explain why. Leads: [enriched list here]

Expected output:
A clear recommendation with supporting rationale - ideal for smart lead scoring.

Prompt recipes by column

The fastest way to get value is to keep one askClaude column per job, each with a tight prompt. Here are ready-to-adapt recipes you can drop next to your enriched data.

• ICP fit tag: "Based on this title and company description, answer ICP-FIT or NOT-FIT for a multichannel prospecting tool sold to revenue teams. One word only. Title: [title]. Company: [company description]."
• Seniority bucket: "Classify this job title into one of: IC, Manager, Director, VP, C-level. Answer with the bucket only. Title: [title]."
• One-line summary: "Summarize what this person does in one sentence a salesperson can skim. Profile: [enriched profile]."
• Icebreaker: "Write a one-line, specific opener referencing something concrete from this profile. No flattery, no questions. Profile: [enriched profile]."
• Pain hypothesis: "Given this company's size and sector, name the single most likely prospecting pain they face. One sentence. Company: [enriched company]."
• Clean the title: "Rewrite this job title in plain English, removing emojis, taglines, and buzzwords. Return the cleaned title only. Title: [raw title]."

Keep each prompt narrow and ask for the output format you want. A column that returns one word is filterable; a column that returns a paragraph is readable. Mixing both in one prompt is what makes answers feel vague.

Because askClaude bills per line, the economics reward this discipline too. A one-word ICP tag and a two-line summary cost the same per row, so the cheapest way to waste credits is a sprawling prompt that returns mush you then have to re-run. Build the prompt on a ten-row sample, confirm the column is clean, and only then drag it down the full list. Treating prompts as reusable assets, named and saved per job, turns askClaude from a one-off trick into a repeatable part of your prospecting workflow.

Best practices that move the needle

• Enrich before you ask. Claude reasons over what is in the row. Run Derrick's lead and company enrichment first so the model reads a full profile, not a name and a guess.
• Test on a sample. Run a prompt on ten rows, read the outputs, tighten the wording, then scale. It saves credits and surfaces bad prompts early.
• Constrain the format. "Answer in one word", "respond in 3 bullet points", "return only the tag" all make outputs usable downstream.
• One job per column. Don't ask Claude to summarize and score and write an icebreaker in a single prompt. Split it into columns you can sort and trust.
• Give it a role. Telling Claude it is "a prospecting expert" or "an SDR reviewing a list" sharpens judgment-heavy tasks like scoring.

Limits and when not to use Claude

Claude reasons over the text you give it. It does not look anything up. If a fact is not in the row, the model cannot invent it reliably, so treat askClaude as an interpreter of enriched data, not a data source. For finding a verified email, a phone number, or a LinkedIn URL, use Derrick's dedicated enrichment features and let Claude work on the result.

A few other guardrails: very long prompts (over roughly 5,000 characters) get truncated, so split big jobs into columns. Free-form questions without a format produce free-form answers that are hard to filter. And for a purely mechanical transform, a spreadsheet formula is cheaper than a model call. Reach for Claude when the task needs reading comprehension or judgment, not when a REGEXEXTRACT would do.

Claude vs the other models in Derrick

Derrick lets you call more than one model from the same sheet, so the question is rarely "Claude or nothing" but "which model for this column". Claude tends to shine on tasks that reward nuance and careful reading: summarizing a dense profile, weighing several leads against an ICP, or writing copy that sounds human rather than templated.

For high-volume, structured generation such as bulk icebreakers or tight classification, the askOpenAI workflow is worth A/B testing on the same rows, since the right pick depends on your prompt and your list. The pragmatic move is to run both on a sample column and keep whichever output you trust. The AI data enrichment hub walks through that comparison and the rest of Derrick's AI columns in one place.

🧠 Pro Tips

• Claude performs best on tasks involving reasoning, empathy, and abstraction
• Think beyond text generation - use it for contextual analysis
• Give as much context as possible for best results
• Structure your prompts like you're talking to a colleague, not coding an API

🧱 Troubleshooting

• Too generic? → Add structure, e.g. “Respond in 3 points”
• Prompt too long? → Keep under 5,000 characters or split it
• Wrong output? → Rephrase your ask more clearly, add persona brief
• Slow response? → Batch your requests in smaller chunks

🚀 Recap

askClaude doesn’t just generate text - it analyzes, summarizes, and prioritizes with a high-context understanding built for sales workflows.

📍 Summarize a lead
🏢 Analyze a company
🎯 Identify the best contact

All in a few clicks - straight from Google Sheets.

Ready to run Claude on your own list? Start with Derrick, enrich your leads, and add your first askClaude column in minutes.

👉 Try askClaude inside Derrick App today.
Turn raw data into sales intelligence - without writing a single line of code.