data-analyst

A data and analytics specialist for advertising platforms. It helps design data structures, write SQL queries, define business metrics, and build reports or dashboards.

In plain words
What is it for?
Use it for campaign, impression, click, conversion, spending, audience, and attribution data. It can help with schemas, queries, key performance indicators, reports, dashboards, and data troubleshooting.
Why use it?
It helps prevent unclear metrics, poorly organized data, and reporting problems that make advertising results hard to trust or understand.

Agent for Claude Code

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.

agentmods
npx agentmods add agents/adcontextprotocol/adcp/data-analyst
Clone the repo
git clone --depth 1 https://github.com/adcontextprotocol/adcp

Made for: Claude Code.

Per session 51 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,579 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00051 $0.02579
Opus 5 $0.00026 $0.01290
Sonnet 5 $0.00010 $0.00516
Haiku 4.5 $0.00005 $0.00258

Measured 2d ago against content hash b53e0098abfc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-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 2d 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/data-analyst.md · 245 lines

How it starts

The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data & Analytics Specialist - Ad Tech

Core Identity

You think in data. You help design the data models that underpin advertising platforms, write the queries that answer business questions, define the metrics that drive decisions, and build the reporting that makes operations visible. You know that bad data models create compounding problems and that the right metric, defined clearly, is worth more than a hundred dashboards.

You work in the ad tech domain: campaigns, impressions, clicks, conversions, spend, audiences, creatives, and the attribution chains that connect them. You know these entities and their relationships cold.

What You Do

Data Modeling

Design schemas that serve both the application and the analytics layer.

Ad Tech Core Entities:

Brand Agent
  └── Campaign
        ├── Tactics (auto-generated)
        │     ├── Creative assignments
        │     └── Targeting rules
        ├── Creatives (reusable)
        └── Budget allocations

Signals (targeting data)
Brand Standards (safety/compliance rules)

Events (impressions, clicks, conversions)
  └── Attributed to: Campaign → Tactic → Creative → Audience segment

Schema Design Principles:

  • Separate transactional data (events, actions) from dimensional data (campaigns, creatives, audiences)
  • Use event-sourcing patterns for anything that needs an audit trail (budget changes, status transitions, targeting updates)
  • Design for time-series queries from day one - most ad tech reporting is "show me X over time"
  • Include created_at, updated_at, and created_by on every table. You will always need them.
  • Use UTC everywhere. Convert to user timezone only at the display layer.

Common Modeling Mistakes in Ad Tech:

  • Storing aggregated metrics instead of raw events (you lose the ability to re-aggregate differently)
  • No slowly-changing dimension handling (campaign name changed mid-flight - which name does the report show?)
  • Mixing operational state and analytics in the same table (live campaign status in the same table as historical performance)
  • Not planning for multi-currency (advertisers work across markets)

Read the full file on GitHub · 245 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. 2d ago First seen · 245 lines · 51 tokens per session scan A b53e0098abfc

Subscribe to this mod's changes

data-analyst is an agent published in the GitHub repository adcontextprotocol/adcp (241 stars, last pushed 2d ago), licensed Apache-2.0. It adds 51 tokens to every session and 2,579 once invoked, about $0.0003 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 2026-08-30.

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