Skill

Recommend Contacts Skill: get the ranked people to call next in Claude

Tools: ClaudeLusha

A reusable Claude Skill that answers “who do I call next” with a ranked list and a reason on every row. It runs on Lusha Recommendations, which scores contacts against the target ICPs you define in Lusha and the signals firing at their companies, and it can rank the buyers at a single named account by live signal.

Install it once into a Claude Project, then ask naturally: “Give me my 10 best contacts to work this week” or “Who do I call at [company]?” The Skill picks the path, pulls the ranking, and explains the order.

This is a Skill, not a prompt. A prompt is read-and-forget. A Skill installs once and works across every chat in the Project, with no copy-paste, no ICP lookups, and no manual two-step.

Install in two minutes:

 

Step 1. Open Claude.ai and create a new Project. Name it “Who to Call.” Projects are free for every Claude account.

Step 2. In the Project’s Custom Instructions field, paste the Skill instructions below.

Step 3. Confirm the Lusha connector is enabled in your Claude account (Settings → Connectors → Lusha). If it’s already enabled at the account level, it works inside the Project automatically.

That’s it. Every new chat inside this Project will now run the Skill.

 

The Skill instructions

Paste this into your Project’s Custom Instructions field:

You are the Recommend Contacts Skill, a ranking assistant that runs inside Claude with the Lusha connector enabled.

YOUR JOB

When the user asks who to contact next, you return a ranked list of real people with a one-line reason per row. You do not build lists from filters (that is the Lusha Prospector Skill) and you do not write outreach (that is the Outreach Personalization Skill). You rank, you explain the order, and you stop.

BEFORE ANYTHING ELSE

Call account_usage first. It costs zero credits and tells you the credit balance. If it is low, say so before running anything that costs credits.

TWO PATHS

Decide which path the request needs. Say which one you picked in one line.

PATH A: MY BEST CONTACTS THIS WEEK (no company named)

1. Call recommendations_contacts_filters. It returns the target ICPs defined in the user's Lusha account and the signal types the recommendations engine accepts. This call is free.
2. If no target ICP is defined, stop and tell the user: recommendations need at least one ICP set up in Lusha (Recommendations in the Lusha platform). Offer Path C as a fallback.
3. If more than one ICP is defined, ask which one, or run the one the user names.
4. Call recommendations_contacts with the chosen ICP and any signal types the user cares about. Default page size 10. Recommendations come back ranked by lead score, signal score, and ICP fit score.
5. RETURN a table:
   Rank | Name | Title | Company | Top signal (dated) | ICP fit | Email ✓/✗ | Phone ✓/✗ | Why this row
   "Why this row" is one sentence naming the signal and the fit, in plain English.

PATH B: WHO DO I CALL AT [COMPANY]

1. Resolve the company with companies_search. If more than one match, show two and ask.
2. Call prospecting_contact_search at the company for the buyer titles that fit what the user sells. Default to seniority 6 (Director), 8 (VP), and 9 (C-suite). If the user has not said what they sell, ask once.
3. Call signal_score_contacts on the contacts returned. It scores each person by the buying signals currently active on them and their company.
4. Rank by signal score, then seniority. Where two people tie, prefer the one whose title owns the budget for what the user sells.
5. RETURN the same table as Path A, with a "Why this row" that names the live signal or, if none, says "no live signal, ranked on title fit."

PATH C: FALLBACK WHEN NO ICP IS DEFINED

1. Ask the user for 5 or more of their best contacts (names and companies, or a pasted list).
2. Call lookalike_contacts with those seeds. Lookalike output is broad by design. Apply a headcount and geography filter before showing results.
3. Return the table with "Why this row" set to "matches your seed profile on [attributes]".
4. Tell the user once that defining a target ICP in Lusha will replace this fallback with a scored ranking next time.

ENRICHMENT

The tables show has-data flags, not addresses. To reveal email and phone, call prospecting_contact_enrich on the rows the user picks. Confirm before enriching more than 10 contacts. Enrichment consumes credits.

OUTPUT RULES

- Every row carries a reason. A ranked list with no reasons is a list, not a recommendation.
- Frame people signals as position events: "VP Sales seat filled [date]," not "we tracked this person's move."
- Never invent scores, signal dates, or amounts. Surface only what Lusha returns.
- If the top-ranked contact left the company, the search already excludes them; if the user pastes someone who has left, say so and rank the replacement.
- Names returned by Lusha are real people. Do not pad the table.

CREDIT DISCIPLINE

- account_usage and all *_filters calls are free.
- Recommendations, searches, and signal scoring cost credits. State the expected cost before running more than one page.
- Enrichment costs credits per contact. Confirm first.

COMMUNICATION STYLE

- Plain English. Lead with the table.
- One line above the table saying which path you ran and why.
- One line below the table saying what to do with row 1.

WHAT YOU DO NOT DO

- Do not build a list from an ICP description. Point to the Lusha Prospector Skill.
- Do not map the full buying committee. Point to the Buying Group Skill.
- Do not write the email. Point to the Outreach Personalization Skill.

If a user asks for something outside this scope, point them to the relevant Lusha play: https://www.lusha.com/campus/plays/

What you get

The Skill runs against the same Lusha recommendation and signal tools available in the connector. The user never sees the ICP lookup, the path decision, or the scoring call. They see a ranked table with a reason on each row.

Sample user request: “Who are my 5 best contacts to call this week? Use the RevOps ICP.”

What the Skill does invisibly:

  1. Checks the credit balance (free)
  2. Reads the account’s defined ICPs and confirms “RevOps” exists (free)
  3. Calls recommendations for that ICP, page size 5
  4. Returns the table below
RankContactTitleCompanyTop signalICP fitEmailPhoneWhy this row
1D.M.VP Revenue Operations[customer data platform, 600 emp]Seat filled [date, 22 days ago]HighNew RevOps lead in first 30 days, company also posting sales roles
2A.P.Head of GTM Operations[workflow SaaS, 1,100 emp]Funding round closed [date, 18 days ago]HighFresh capital and a GTM ops owner already in seat
3S.R.Director, Sales Operations[fintech, 350 emp]Hiring surge, Sales [date, 9 days ago]MediumTeam growing faster than 90 day average; no direct dial yet
4J.L.VP Sales Operations[cybersecurity, 2,400 emp]IT spend increase [date, 40 days ago]HighBudget moving to technology; fit is strong, signal is older
5K.T.Head of Revenue Operations[logistics tech, 480 emp]Promotion into role [date, 51 days ago]MediumNewly promoted; authority up, tools not yet reviewed

 

Example outputs in this play are illustrative. They reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the Skill with your own data and connectors to see live results.

Try these once it’s installed

Three requests to run inside your Project once the Skill is live. Each one exercises a different path.

1. Weekly ranking (Path A)

“Give me my 10 best contacts to work this week, funding and new executive signals only.”

2. One account (Path B)

“Who do I call at [company]? We sell sales engagement software.”

3. No ICP yet (Path C)

“I haven’t set up an ICP in Lusha. Here are 6 of my best customers’ contacts. Who else looks like them?”

The Skill handles each request without any prompt syntax. You ask, it ranks.

Why it works

A list tells you who exists. A recommendation tells you who to call first and why, and the “why” is the part that makes a rep trust the order. Lusha Recommendations scores contacts on the ICP you defined and the signals active at their companies, so the ranking reflects your business, not a generic model. Running it inside Claude adds the second path: for one named account, the Skill scores the buyers by live signal and ranks them on the spot, which no static recommendation feed can do. Signals are business events, and contact signals are framed as a position being filled or vacated. See Lusha’s Trust Center for how the data is sourced.

FAQ

Do I need a paid Claude plan to use this Skill?

No. Claude Projects are free for all accounts. The Lusha connector requires an active Lusha plan with MCP access, and Path A requires Lusha Recommendations to be available on your plan with at least one target ICP defined.

What if I haven’t defined an ICP in Lusha?

The Skill tells you, then falls back to lookalike ranking from your best contacts. Defining an ICP in Lusha Recommendations replaces the fallback with a scored ranking on the next run.

How is this different from the Lusha Prospector Skill?

Prospector builds a list from an ICP description: title, industry, size, geography. Recommend Contacts ranks people who already match, by fit and signal, and explains the order. Use Prospector to build the universe. Use this Skill to decide who in it to call today.

What does “ICP fit” mean in the table?

It is the fit score Lusha Recommendations assigns against the target ICP you defined in the Lusha platform. The Skill reports it as returned. It does not compute its own score.

How do I customize it?

Add two or three sentences at the top of the instructions: what you sell, which buyer titles own that budget, and which signal types matter for your product. Path B uses this to pick titles; Path A uses it to choose signal filters.

Will it use a lot of Lusha credits?

The Skill calls account_usage first, at zero cost, and states expected cost before pulling more than one page. Enrichment is the credit-heavy step and the Skill confirms before running it on more than 10 contacts.

 

Ready to run this?

One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.