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Use Google Sheets with an AI Browser for Candidate Sourcing

Run candidate sourcing in Strawberry using Google Sheets as one of the inputs. Specific surfaces, example prompt, real output, and tradeoffs vs alternatives.

If you use Google Sheets and you regularly need to source candidates, the bottleneck is usually the same: Google Sheets holds part of the context, but candidate sourcing also needs signals that live outside it - on the public web, in LinkedIn, in news, in other connected apps. Strawberry is built to combine the Google Sheets context with the rest of the browser, and run the full workflow as a companion you can re-trigger every week.

This page describes specifically how Strawberry handles candidate sourcing when Google Sheets is one of the inputs. It names the Google Sheets surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a recruiter, founder hiring, hiring manager is trying to do

The goal of candidate sourcing is to build a shortlist of 10-30 candidates who match the role and have at least one signal of openness. The success metric is concrete: 30% reply rate to first outreach, 5+ first-call conversions per 30 sourced. That definition matters because it shapes what Google Sheets needs to contribute to the workflow.

What signals candidate sourcing actually needs

For each signal below, here is whether Google Sheets can contribute directly or whether Strawberry has to find it via the browser:

  • Current role and tenure - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
  • Recent role changes (often visible on LinkedIn) - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
  • GitHub or content output for technical roles - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
  • Company stage match (someone leaving a Series B is more likely to talk to a seed-stage co) - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
  • Geo match for hybrid roles - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
  • Openness signals (LinkedIn open-to-work, recent comments about job search) - Google Sheets does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Google Sheets

Strawberry can read a tab, enrich each row with web research, and write the enriched columns back in place; ideal for lead lists, candidate sourcing, and account research.

Google Sheets surfaces Strawberry uses for this workflow: named ranges, tabs, filters, headers, cell formulas.

How Strawberry runs candidate sourcing with Google Sheets

  1. Strawberry opens the Google Sheets named ranges that contains the relevant context.
  2. The companion pulls related context from Google Sheets (tabs, history, attached files) where it exists.
  3. For the parts Google Sheets does not store, Strawberry uses the browser - web search, LinkedIn, news, the prospect's website.
  4. Strawberry synthesises the output in the shape this workflow needs: A shortlist with one row per candidate.
  5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Google Sheets or your system of record.

Example Strawberry prompt

Paste this in a new Strawberry chat with Google Sheets connected. Adjust the specifics to your actual ICP, role, or topic.

Read this Google Sheets named ranges and any linked context.
Then run a full candidate sourcing workflow on it. Use the browser to fill any gaps not in Google Sheets.
Return the output in the shape we use for candidate sourcing: A shortlist with one row per candidate: name, current role, target role fit (1-5), one personalised opening line, contact link.
Do not send anything externally. Save the draft to me to review.

What a good candidate sourcing output looks like

Here is what a finished output for candidate sourcing should look like in practice. The specifics will change for your use case, but the shape should look similar:

  • Role: Founding Engineer (Stockholm or remote EU)
  • Candidate: Marek Novak - Senior Engineer @ Klarna, 4 years
  • Fit: 5/5 (worked on payment systems, contributed to Rust open source, recent talk on type-safe APIs)
  • Opening line: noticed his RustConf talk on type-safe API contracts and our backend lead's tweet about Marek's library
  • Contact: LinkedIn DM + GitHub email

Why Google Sheets for this, and where to use a different tool

Google Sheets is strong for this workflow because Strawberry can read a tab, enrich each row with web research, and write the enriched columns back in place; ideal for lead lists, candidate sourcing, and account research.

Where Google Sheets falls short very large sheets (10k+ rows) need batching; complex formulas can be misread if cells contain HTML or markdown.

Consider also a CRM for relationship history.

Common mistakes when running candidate sourcing

  • Spray-and-pray DMs that mention nothing specific
  • Missing the obvious signals (someone just posted 'thinking about a change')
  • No quality bar - putting 200 names on the list to look productive

Connecting Google Sheets to Strawberry

Native Google Sheets integration with read+write scopes. Once connected, the companion can read the surfaces above without re-authenticating, and any write action still requires explicit human approval the first time the workflow runs.

Caveats

Do not let any AI agent send emails, update CRM records, or change shared systems without a clear approval step. Strawberry is strongest when the workflow combines browser context with connected-app context and a human review for sensitive actions.

How Google Sheets + Strawberry runs candidate sourcing

1 Google Sheets

Read

Open the relevant Google Sheets named ranges; pull related context.

2 Browser

Augment

Use the browser, LinkedIn, news, and other connected apps for signals outside the CRM/tool.

3 Output

Compose

Synthesise into the candidate sourcing shape: A shortlist with one row per candidate.

4 Human

Approve

Human reviews before any external action; approved output is saved back.

FAQ - Google Sheets + AI browser for candidate sourcing

Can Strawberry do candidate sourcing entirely inside Google Sheets?

No, and that is the point. candidate sourcing needs signals Google Sheets does not store - public web, LinkedIn, news, other apps. Strawberry combines Google Sheets with the browser, which is where the real value comes from.

Does Google Sheets need to be the primary CRM or system of record?

Not necessarily. Google Sheets can be one input among several. Strawberry can read it as context even if your primary system of record is somewhere else.

What permissions do I need on Google Sheets?

Read access to the surfaces you want Strawberry to use (named ranges, tabs, filters). Write permissions are only needed if you want Strawberry to update Google Sheets after a human approves the change. Native Google Sheets integration with read+write scopes.

What is the realistic success metric for candidate sourcing?

30% reply rate to first outreach, 5+ first-call conversions per 30 sourced - that is the target Strawberry helps you hit, not the only thing it measures.

What is the biggest mistake to avoid?

Spray-and-pray DMs that mention nothing specific.

Run candidate sourcing in 10 minutes with Strawberry and Google Sheets

  1. Open Google Sheets

    Connect Google Sheets so Strawberry can read named ranges, tabs, filters, headers, cell formulas, data validation and combine them with the rest of the brief. Pin the specific records or views you want to start from so the agent does not drift.

  2. Tell Strawberry the brief

    Drop the prompt below. Replace the placeholder with the actual recruiter, founder hiring, hiring manager target - one name, one URL, or one Google Sheets reference is enough. Keep the goal explicit: build a shortlist of 10-30 candidates who match the role and have at least one signal of openness

  3. Let it gather signals

    Strawberry pulls current role and tenure and recent role changes (often visible on LinkedIn), then layers public web sources in parallel. You should see citations next to each fact - that is the audit trail. Watch the Google Sheets side: very large sheets (10k+ rows) need batching; complex formulas can be misread if cells contain HTML or markdown

  4. Review before write-back

    Output lands in the shape you asked for: A shortlist with one row per candidate: name, current role, target role fit (1-5), one personalised opening line, contact link. Read it once. Fix anything off. The success metric is 30% reply rate to first outreach, 5+ first-call conversions per 30 sourced - if the draft does not hit that bar, send it back with a one-line correction.

  5. Save it as a routine

    If you will source candidates this again next week, click Save as routine. Pick a cadence (daily, weekly, on-trigger). Strawberry re-runs the whole flow on schedule and pings you when the new output is ready.

Paste-ready prompt for candidate sourcing with Google Sheets

You are helping me source candidates candidate sourcing. Use Google Sheets as one input and the public web for the rest.

Target: [paste one recruiter, founder hiring, hiring manager target here - a Google Sheets reference, a name + company, or a URL]

Goal: build a shortlist of 10-30 candidates who match the role and have at least one signal of openness

Signals to gather:
- current role and tenure
- recent role changes (often visible on LinkedIn)
- GitHub or content output for technical roles
- company stage match (someone leaving a Series B is more likely to talk to a seed-stage co)
- geo match for hybrid roles
- openness signals (LinkedIn open-to-work, recent comments about job search)

Output shape: A shortlist with one row per candidate: name, current role, target role fit (1-5), one personalised opening line, contact link

Rules:
- Cite every fact with a link or a Google Sheets reference. If you cannot find a signal, say so explicitly rather than guessing.
- Do not invent specifics. Use real, dated signals from the last 90 days where possible.
- If a fact would change the outcome and is missing, pause and ask me before writing the final output.

When the output is ready, surface it in this chat. Do not write back to Google Sheets or send anything externally until I approve.

Paste this into Strawberry's chat field. Replace the target placeholder before running.

When Google Sheets + Strawberry is the right combo for candidate sourcing

Google Sheets is the structured table where a team tracks accounts, leads, candidates, tasks, or content. Strawberry can read a tab, enrich each row with web research, and write the enriched columns back in place; ideal for lead lists, candidate sourcing, and account research. For candidate sourcing specifically, that means the agent already has named ranges, tabs, filters, headers, cell formulas, data validation as starting context - you do not need to brief it from scratch.

When it is NOT a fit

  • You need a single number, not a synthesised brief. A SQL query against your warehouse is faster.
  • The decision is happening in the next 60 seconds. The agent is fast but it is not instant; for hard real-time use, do it manually.
  • The Google Sheets data you would feed in is stale or wrong. Garbage in, confident garbage out.

Three mistakes to avoid

  1. spray-and-pray DMs that mention nothing specific
  2. missing the obvious signals (someone just posted 'thinking about a change')
  3. no quality bar - putting 200 names on the list to look productive

Honest tradeoff

very large sheets (10k+ rows) need batching; complex formulas can be misread if cells contain HTML or markdown. If you are running this at scale (10+ briefs per day), batch the inputs and let Strawberry process them as a routine instead of one-by-one prompts - cheaper per brief and the output stays consistent.

What a real output looks like

Role: Founding Engineer (Stockholm or remote EU),Candidate: Marek Novak - Senior Engineer @ Klarna, 4 years,Fit: 5/5 (worked on payment systems, contributed to Rust open source, recent talk on type-safe APIs),Opening line: noticed his RustConf talk on type-safe API contracts and our backend lead's tweet about Marek's library,Contact: LinkedIn DM + GitHub email
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