mcpField
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audience-builder

audience-builder

github.com/kelpi-aiMarketing & growth553 installsUpdated 17w ago
Repository

About audience-builder

A marketing skill for researching and planning Meta ad audiences, aimed at advertisers evaluating targeting before launch. It considers first-party audiences, lookalikes, and broad targeting, and creates audiences only after approval.

Key features

  1. 01Research related interests, behaviors, and sizes
  2. 02Propose custom audiences and 1% lookalikes
  3. 03Assess broad targeting against the budget
  4. 04Flag targeting options that have been retired
  5. 05Create audiences only with explicit approval

Use cases

  1. 01Assess a customer list as a seed audience before launch
  2. 02Investigate why an ad reached the wrong audience
  3. 03Compare interest, lookalike, and broad targeting options

MCP support

Requires a Meta Ads MCP. Research is read-only; audience creation requires explicit approval.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/kelpi-ai/meta-ads-skills --skill audience-builder

After installing, type/audience-builderin Claude Code to confirm it loaded.

Bundled filesskill.meta.json

Details

From the project README

Audience Builder

Doctrine

Targeting is mostly the algorithm's job now: Meta removed exclusion options in 2025, retired interest categories in January 2026, and Andromeda aims by reading the ad. Audience work in 2026 is (a) first-party assets (customer lists, lookalikes) that give the system a seed, and (b) knowing when broad is simply better. An honest audience skill often ends with "go broad and fix the angle instead."

When to use

  • Before launch, if you have a customer list worth seeding.
  • When delivery lands in the wrong crowd ("my ad for young women went to older men"): usually an angle problem wearing a targeting costume.
  • Needs a Meta Ads MCP (read for research, write only for creating audiences you approve).

Run it

Audience research for my offer: [ONE-LINE OFFER + WHO from my angles]

1. Search interests and behaviors related to my category. For each, give the estimated audience size.
2. If I have a customer list or purchasers: propose a custom audience plus a 1% lookalike, with estimated sizes. Draft only, do not create yet.
3. Then be honest with me: given my budget of [$X/day], would broad targeting likely beat these? Explain in two sentences using what my ad creative already signals about who it is for.
4. Wait for my pick before creating anything.

Guardrails

  • Research is read-only; audience creation happens only on explicit approval.
  • Never stack interests to feel in control. If the recommendation is broad, say broad.
  • If an ad set was built on since-retired targeting options, flag it: those stopped delivering after January 15, 2026.

Good output looks like

A short table of real options with sizes, one honest recommendation, and no audience created that you did not ask for by name.

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