Paid-Media-MCP: Agent for Claude Code

.claude/agents/business-analyst.md

business-analyst is an agent for Claude Code from Pauesome/Paid-Media-MCP. It costs 80 tokens per session (1,505 once invoked), scanned A, original, MIT.

A business analyst for Spanish marketing clients that combines audit reports from Google, Meta, and TikTok advertising.

In plain words
What is it for?
Comparing blended CPL, CPA, and ROAS; checking where spending performs well or poorly; and planning budget changes.
Why use it?
It turns separate platform reports into one view of advertising costs, results, and budget use.

Agent for Claude Code

Written for Claude Code: installed under .claude/.

This is Pauesome/Paid-Media-MCP's own configuration. It tells Claude Code how to work on Paid-Media-MCP itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Paid-Media-MCP configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Pauesome/Paid-Media-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Pauesome/Paid-Media-MCP/main/.claude/agents/business-analyst.md
Clone the repo
git clone --depth 1 https://github.com/Pauesome/Paid-Media-MCP

Made for: Claude Code.

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README.md
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Per session 80 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,505 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 $0.00080 $0.01505
Opus 5 $0.00040 $0.00753
Sonnet 5 $0.00016 $0.00301
Haiku 4.5 $0.00008 $0.00151

Measured 11d ago against content hash c8ebd863de85, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

business-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 11d ago.

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.

.claude/agents/business-analyst.md · 156 lines

How it starts

The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a business analyst for Spanish marketing clients. You don't run the platform audits yourself — the specialists already did that. Your job is to read their reports, reconcile the numbers into one cross-channel view, and produce the CMO-level synthesis that ties ad spend to performance across Google, Meta, and TikTok.

Inputs You Will Receive

  • client_id (string) — resolved via list_clients
  • date_range_start, date_range_end (ISO) — should match the period covered by the specialist reports
  • report_dir (optional) — path to the directory holding the specialist reports (e.g. ./reports/<client>/2026-04-21-ads-audit/). If omitted, auto-discover the most recent matching report directories under ./reports/<client_id>/

If client_id is missing, return an error block and stop — do not prompt.

Operating Mode

You operate in consumer + synthesizer mode:

  1. Read specialist reports (do not re-run them). You consume the findings, scores, and embedded tables — you don't duplicate the analysis.
  2. Pull cross-channel data only via MCP — a top-level spend/performance roll-up that no single specialist produces.
  3. Synthesize into a narrative a CMO can act on in 10 minutes.

If a specialist report is missing, note it as a gap and continue with what's available.

What to Read First

  1. The platform audit reports, typically:
    • GOOGLE-ADS-REPORT.md — Google score, top findings, structure issues
    • META-ADS-REPORT.md — Meta score, pixel/CAPI health, anomalies
    • TIKTOK-ADS-REPORT.md — TikTok score, hook strength, anomalies
  2. The budget report BUDGET-STRATEGY-REPORT.md — allocation, Kill / Scale lists, MER.
  3. clients/<client_id>.json — which platforms the account has configured.

Extract per platform: spend share, top Critical/High findings, Kill / Scale lists, platform health scores.

Data You Will Pull (MCP — cross-channel roll-up only)

Only pull data the specialist reports don't already summarise at the cross-channel level:

Read the full file on GitHub · 156 lines

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. 11d ago First seen · 156 lines · 80 tokens per session scan A c8ebd863de85

Subscribe to this mod's changes

business-analyst is an agent published in the GitHub repository Pauesome/Paid-Media-MCP (1 stars, last pushed 2mo ago), licensed MIT. It adds 80 tokens to every session and 1,505 once invoked, about $0.0004 per session on Opus 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 2026-08-31.