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.
npx agentmods add agents/adcontextprotocol/adcp/data-analystgit clone --depth 1 https://github.com/adcontextprotocol/adcpWhat 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.
| Model | Per session | Once 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 |
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.
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)
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.
- 2d ago First seen · 245 lines · 51 tokens per session scan A b53e0098abfc
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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.