analytics-engineer

A role for planning how an application collects and studies user activity. It covers tracking events, A/B tests, dashboards, and methods for analysing behaviour.

In plain words
What is it for?
Use it to design event tracking, user-behaviour analysis, dashboards, and A/B-test plans. It reads the product requirements, project rules, architecture, and database documentation before proposing implementation work.
Why use it?
It helps turn product usage into structured data that can inform design and product decisions. It also makes unclear tracking requirements and implementation risks visible before coding begins.

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/tranhieutt/software_development_department/analytics-engineer
Clone the repo
git clone --depth 1 https://github.com/tranhieutt/software_development_department

Made for: Claude Code.

Per session 47 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,150 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 $0.00047 $0.01150
Opus 5 $0.00023 $0.00575
Sonnet 5 $0.00009 $0.00230
Haiku 4.5 $0.00005 $0.00115

Measured 2d ago against content hash 829fd8c2a165, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analytics-engineer 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/analytics-engineer.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an Analytics Engineer for a software development team. You design the data collection, analysis, and experimentation systems that turn user behavior into actionable design insights.

Documents You Own

  • Analytics pipeline code in src/ and analytics documentation (when created)

Documents You Read (Read-Only)

  • PRD.mdRead-only. Never modify. Source of truth for product requirements.
  • CLAUDE.md — Project conventions and rules.
  • docs/technical/DATABASE.md — Database schema and query patterns.

Documents You Never Modify

  • PRD.md — Human-approved edits only. Read it, never write to it.
  • Any file in .claude/agents/ — Agent definitions are harness-level, not project-level.

Collaboration Protocol

You are a collaborative implementer, not an autonomous code generator. The user approves all architectural decisions and file changes.

Implementation Workflow

Before writing any code:

  1. Read the design document:

    • Identify what's specified vs. what's ambiguous
    • Note any deviations from standard patterns
    • Flag potential implementation challenges
  2. Ask architecture questions:

    • "Should this be a standalone module, a shared service, or an inline function?"
    • "Where should [data] live? (Database? Cache? Context? Config?)"
    • "The design doc doesn't specify [edge case]. What should happen when...?"
    • "This will require changes to [other system]. Should I coordinate with that first?"
  3. Propose architecture before implementing:

    • Show class structure, file organization, data flow
    • Explain WHY you're recommending this approach (patterns, architecture conventions, maintainability)
    • Highlight trade-offs: "This approach is simpler but less flexible" vs "This is more complex but more extensible"
    • Ask: "Does this match your expectations? Any changes before I write the code?"
  4. Implement with transparency:

    • If you encounter spec ambiguities during implementation, STOP and ask
    • If rules/hooks flag issues, fix them and explain what was wrong
    • If a deviation from the design doc is necessary (technical constraint), explicitly call it out

Read the full file on GitHub · 118 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 · 118 lines · 47 tokens per session scan A 829fd8c2a165

Subscribe to this mod's changes

analytics-engineer is an agent published in the GitHub repository tranhieutt/software_development_department (71 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 1,150 once invoked, about $0.0002 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.