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meta-performance-analyst

meta-performance-analyst is a skill for Codex from vibemarketer94/vibemarketer-skills. It costs 110 tokens per session (2,697 once invoked), scanned A, original, MIT.

A skill for analysing advertising results from Meta, the company behind Facebook and Instagram. It examines campaign data and turns measures such as return on ad spend, cost per acquisition, clicks, and reach into decisions.

In plain words
What is it for?
Use it to review campaigns, ad sets, and ads; compare spending and returns; investigate unusual results or creative fatigue; assess budget allocation; and analyse data from Meta tools, exports, or attribution platforms.
Why use it?
It helps separate useful performance signals from incomplete or inconsistent advertising data. It also makes the time period, currency, attribution rules, and data source explicit before drawing conclusions.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex.

not rated 41repo 4mo ago A scan 110 tokens original MIT

Good fit Use it to review campaigns, ad sets, and ads; compare spending and returns; investigate unusual results or creative fatigue; assess budget allocation; and analyse data from Meta tools, exports, or attribution platforms.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst
View source ↗ vibemarketer94/vibemarketer-skills x.com
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add vibemarketer94/vibemarketer-skills --skill meta-performance-analyst
Clone the repo
git clone --depth 1 https://github.com/vibemarketer94/vibemarketer-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for meta-performance-analyst

README.md
[![agentmods](https://agentmods.dev/badge/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst/github.svg)](https://agentmods.dev/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst)
Your own site
<a href="https://agentmods.dev/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst"><img src="https://agentmods.dev/badge/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for meta-performance-analyst

Your own site · 80×15
<a href="https://agentmods.dev/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst"><img src="https://agentmods.dev/badge/skills/vibemarketer94/vibemarketer-skills/meta-performance-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 110 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,697 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00110 $0.02697
Opus 5.5 $0.00044 $0.01079
Sonnet 5.5 $0.00022 $0.00539
Haiku 4.5 $0.00011 $0.00270

Measured 2mo ago against content hash 57170ede1b9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

meta-performance-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

The scan reads SKILL.md. This mod also ships 11 executable files (scripts/analysis_pack.py, scripts/build_analysis_pack.py, scripts/generate_operator_read.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/meta-performance-analyst/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Meta Performance Analyst

You are a performance marketer analyzing Meta Ads for a DTC brand. Your job is to turn account data into decisions.

Before Analysis

  1. Read .agents/vibemarketer-context.md if it exists. Check .codex/, .claude/, and .agents/product-marketing.md as fallbacks.
  2. Identify the data source:
    • Meta Ads CLI data
    • Meta MCP or Marketing API fallback data
    • Meta Ads Manager CSV export
    • Triple Whale Summary, Metrics, or Attribution API data
    • Northbeam Data Export API or CSV data
    • Shopify/backend revenue and order data
    • Spreadsheet copied into chat
    • Screenshots or summary only
  3. If using MCP/API, request the smallest dataset that answers the question. Prefer account, campaign, ad set, and ad level insights over broad raw pulls.
  4. If using CSV/spreadsheet data, inspect columns and normalize metric names before analysis.
  5. State attribution window, date range, and currency. If unknown, call it out.

Reference Files

Load only what is needed:

  • references/meta-metrics.md for field mapping, calculated metrics, and common Meta Insights fields.
  • references/anomaly-rules.md for diagnostic rules and interpretation patterns.
  • references/meta-mcp-workflow.md for Meta Ads CLI-first workflow, MCP fallback, and safe API query shapes.
  • references/attribution-platforms.md for Triple Whale, Northbeam, Shopify/backend, and blended-goal reconciliation.
  • references/llm-analysis-providers.md when deciding which LLM/API should analyze text, image, or video inputs.

Scripts

Use these when the user wants executable data pulls:

  • scripts/pull_meta_ads_cli.py: preferred wrapper for Meta Ads CLI performance pulls.
  • scripts/pull_meta_active_hierarchy.py: active campaign, ad set, and ad drilldown that only descends into entities with spend in the analysis window. It is slow/resumable by default, writes a manifest, caches entity lists, can continue after Meta rate limits, and can fall back to Marketing API entity listing when Ads CLI list commands fail.
  • scripts/pull_meta_insights.py: fallback Marketing API helper when Ads CLI is unavailable.
  • scripts/pull_triplewhale.py: call Triple Whale endpoints with explicit JSON payloads.
  • scripts/normalize_triplewhale_summary.py: flatten Triple Whale Summary Page JSON into long metrics CSV and a one-row KPI target CSV.
  • scripts/generate_operator_read.py: combine normalized Meta hierarchy CSVs with Triple Whale KPIs into an operator pack with coverage proof, campaign drilldowns, ad set analysis, and ad-level CPMr diagnostics.
  • scripts/build_analysis_pack.py: orchestrate live or sample Meta hierarchy pulls, Triple Whale normalization from API or existing JSON, KPI normalization, the operator pack, or a Triple Whale-only smoke test.
  • scripts/pull_northbeam_export.py: fetch Northbeam models/metrics/breakdowns or create a Data Export API job.

Read the full file on GitHub · 243 lines

Files

What ships with it

19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 243 lines · 110 tokens per session scan A 57170ede1b9b

Subscribe to this mod's changes

meta-performance-analyst is a skill published in the GitHub repository vibemarketer94/vibemarketer-skills (41 stars, last pushed 4mo ago), licensed MIT. It adds 110 tokens to every session and 2,697 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metricsClaude CodeMarketing and SEOai-agentscodexcreative-strategydtc
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