What makes Meta Ads Audience Analysis notable?

1K downloads. Trust evidence available

How should Meta Ads Audience Analysis be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Meta Ads Audience Analysis

[Didoo AI] Analyzes Meta Ads audience efficiency across audience types, overlap, demographics, and budget allocation. Use when reviewing targeting strategy,... Skill: Meta Ads Audience Analysis Owner: elias-didoo Summary: [Didoo AI] Analyzes Meta Ads audience efficiency across audience types, overlap, demographics, and budget allocation. Use when reviewing targeting strategy,... Tags: audience-analysis:1.0.0, didoo-ai:1.0.0, latest:1.1.0, meta-ads:1.0.0 Version history: v1.1.0 | 2026-04-16T02:36:27.264Z | user Added homepage, metadata.openclaw.requires.env, OAuth scope for

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.0release · observed Apr 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170zy2wssmkqheacrkh2syyv183vgqw:meta-ads-audience-analysis

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-elias-didoo-meta-ads-audience-analysis/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

14,037 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: meta-ads-audience-analysis
description: "[Didoo AI] Analyzes Meta Ads audience efficiency across audience types, overlap, demographics, and budget allocation. Use when reviewing targeting strategy, planning audience expansion or narrowing, or auditing budget distribution across audience segments."
homepage: https://didoo.ai/blog
metadata:
  {
    "openclaw":
      {
        "requires": { "env": ["META_ACCESS_TOKEN", "META_AD_ACCOUNT_ID"] },
      },
  }
---

## Required Credentials
| Credential | Where to Get | Used For | OAuth Scope |
|-----------|-------------|---------|-------------|
| META_ACCESS_TOKEN | Meta Developer Console → Graph API Explorer → Generate Token | Fetching audience breakdown and overlap data | `ads_read` (read-only) |
| META_AD_ACCOUNT_ID | Ads Manager URL: `adsmanager.facebook.com/act_XXXXXXXXX` | Identifying which account to query | — |

## When to Use
Before launching new campaigns, during quarterly planning, or when a campaign is underperforming and audience mismatch is suspected.
Run standalone or after `meta-ads-weekly-performance` identifies audience-related issues.

---

## Requirements
- account_id (required)
- campaign_id (optional — defaults to all campaigns)
- date_range: last_30d (default)

---

## Step 1: Fetch Audience Data
Pull data broken down by audience dimension:

### Audience Type Performance (via breakdown by audience_type)
- Lookalike, Custom, Broad, Interest, Retargeting
- Spend, CTR, CPA, ROAS for each

### Audience Overlap (via Meta's overlap analysis tool)
- Pairs of overlapping audiences
- Overlap percentage, combined spend, ROAS

### Demographic Breakdown (via breakdown by age and gender)
- Age × Gender cells
- Spend, conversions, CPA, ROAS

### Placement Breakdown
- Feed, Stories, Reels, Instant Articles, Audience Network, Messenger
- CTR, CVR, CPA, ROAS for each

---

## Step 2: Audience Type Efficiency
| Audience Type | Spend | % of Budget | CTR | CPA | ROAS | Budget Should Be |
|--------------|------|------------|-----|-----|------|------------------|
|              |      |            |     |     |      |                  |

Identify:
- Which audience type delivers best ROAS
- Which is over/underallocated relative to efficiency
- Lookalike quality (which LAL layers performing)

---

## Step 3: Audience Overlap Analysis
| Audience Name | Overlaps With | Overlap % | Combined Spend | ROAS | Action |
|---------------|---------------|-----------|-----------------|------|--------|
|               |               |           |                 |      | Consolidate / Exclude / Separate / Monitor |

Flag:
- Overlap > 30% with high combined spend → Consolidate
- Overlap > 50% → Exclude one
- Different audiences with same LAL source → Separate

---

## Step 4: Demographic Deep Dive
| Age and Gender | Spend | Conversions | CPA | ROAS | Action |
|-----------------|------|-------------|-----|------|--------|
|                 |      |             |     |      | Increase Bid / Decrease Bid / Excl

_meta.json

{
  "ownerId": "kn72xgwz1h6j5cfwscvt1wm10d83tj78",
  "slug": "meta-ads-audience-analysis",
  "version": "1.1.0",
  "publishedAt": 1776306987264
}

skill-card.md

## Description:

[Didoo AI] Analyzes Meta Ads audience efficiency across audience types, overlap, demographics, and budget allocation. Use when reviewing targeting strategy, planning audience expansion or narrowing, or auditing budget distribution across audience segments.

This skill is ready for commercial/non-commercial use.

## Publisher:

[elias-didoo](https://clawhub.ai/user/elias-didoo)

### License/Terms of Use:

MIT-0

## Use Case:

Marketing operators and analysts use this skill to review Meta Ads audience efficiency, overlap, demographic performance, placement results, and budget allocation before campaign planning or troubleshooting.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: A Meta access token with broader permissions than needed could expose campaign data beyond the skill's read-only analysis use case.

Mitigation: Use a token limited to the read-only ads_read scope before running the skill.

Risk: Campaign-derived audience and budget findings are written to session context for use by the related recommendation skill.

Mitigation: Run the skill only when that session-context handoff is acceptable for the account and campaign data being analyzed.

Risk: Audience segment conclusions can be misleading when based on too little data.

Mitigation: Follow the skill's minimum of 50 results per segment before drawing conclusions on an audience slice.

## Reference(s):

- [Didoo AI Blog](https://didoo.ai/blog)
- [ClawHub Skill Page](https://clawhub.ai/elias-didoo/skills/meta-ads-audience-analysis)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown analysis with structured sections and session-context findings]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Writes audience issues and budget reallocation findings to session context for the related recommendation skill.]

## Skill Version(s):

1.1.0 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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