Use Slack with an AI Browser for Candidate Sourcing

If you use Slack and you regularly need to source candidates, the bottleneck is usually the same: Slack 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 Slack 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 Slack is one of the inputs. It names the Slack 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 Slack needs to contribute to the workflow.

What signals candidate sourcing actually needs

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

• Current role and tenure - Slack does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Recent role changes (often visible on LinkedIn) - Slack does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• GitHub or content output for technical roles - Slack 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) - Slack does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Geo match for hybrid roles - Slack 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) - Slack does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Slack

Strawberry can read recent channel activity, summarize a thread, and post approved updates back to a channel.

Slack surfaces Strawberry uses for this workflow: channels, DMs, threads, saved items, user list.

How Strawberry runs candidate sourcing with Slack

1. Strawberry opens the Slack channels that contains the relevant context.
2. The companion pulls related context from Slack (DMs, history, attached files) where it exists.
3. For the parts Slack 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 Slack or your system of record.

Example Strawberry prompt

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

Read this Slack channels and any linked context.
Then run a full candidate sourcing workflow on it. Use the browser to fill any gaps not in Slack.
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 Slack for this, and where to use a different tool

Slack is strong for this workflow because Strawberry can read recent channel activity, summarize a thread, and post approved updates back to a channel.

Where Slack falls short Sending in Slack requires explicit approval; private channels need explicit invitation; search retention depends on plan.

Consider also a CRM or project tool for tracked follow-up.

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 Slack to Strawberry

Native OAuth, read + write scopes are separate. 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.

When Slack + Strawberry is NOT the right fit for candidate sourcing

Skip this setup if any of the following is true:

• You don't actually need Slack signals. If everything you need lives on the public web, drop the Slack step and let Strawberry run on URLs alone - it's faster.
• A known Slack constraint blocks the speed gain: Sending in Slack requires explicit approval.
• The buyer (recruiter, founder hiring, hiring manager) doesn't own the decision. If the brief gets handed to someone who'll redo the research, the audit-trail-in-Strawberry advantage is wasted.

3 mistakes that kill this workflow

1. Spray-and-pray DMs that mention nothing specific. Slack is one input. Strawberry's edge is combining it with everything else. Stop at Slack-only signals and you'd have been faster with native Slack reports.
2. Missing the obvious signals (someone just posted 'thinking about a change'). Pre-check Slack for a recent touch or duplicate before Strawberry acts on the output. A duplicate hit burns the relationship.
3. No quality bar - putting 200 names on the list to look productive. Strawberry is built so a human reviews before any external action. Skipping that review to save time is how you ship a wrong fact to a real person.

Honest tradeoff vs alternatives

You could source candidates inside Slack alone using its native features, or with a dedicated candidate sourcing tool. Slack alone gives you tighter data fidelity but misses every signal that lives off-platform. A specialised candidate sourcing tool gives you better dashboards but its scope ends where its integrations end, and most of the real signal still lives on the open web.

Strawberry's edge with Slack: Strawberry can read recent channel activity, summarize a thread, and post approved updates back to a channel. The price you pay: an agent run takes 30-90 seconds; a native Slack action loads in 2. For a one-off question you already know the answer to, use Slack directly. For an output you'll redo every week or every account, route it through Strawberry as a saved routine so the synthesis happens once and re-runs automatically.

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