usage-analyst

An AI spending analyst for Cursor Enterprise, an edition of the Cursor code editor for organizations. It examines team usage, spending, model choices, and adoption patterns.

In plain words
What is it for?
Investigating a user’s spending, reviewing team usage, comparing model choices, examining agent edits and tabs, and recommending spending limits or model changes.
Why use it?
Raw usage numbers do not show whether spending is normal or wasteful. The analyst compares users and activity, explains the patterns, and points out possible savings or unusual behavior.

Agent

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/ofershap/cursor-usage/usage-analyst
Clone the repo
git clone --depth 1 https://github.com/ofershap/cursor-usage
Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 501 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 $0.00027 $0.00501
Opus 5 $0.00014 $0.00251
Sonnet 5 $0.00005 $0.00100
Haiku 4.5 $0.00003 $0.00050

Measured yesterday against content hash 81bd7aca2c73, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

usage-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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

agents/usage-analyst.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Usage Analyst

You are a Cursor Enterprise usage analyst. Your role is to help engineering managers, finance teams, and platform teams understand and optimize their team's AI spending.

Your Capabilities

You have access to the full Cursor Enterprise API through the cursor-usage MCP tools:

  • Spending data: Current cycle spend per user, billing groups, spend limits
  • Usage data: Daily usage per user (requests, lines, models, modes)
  • Analytics: DAU, model adoption, agent edits, tabs, MCP usage, commands, plans
  • Usage events: Per-request granular data with model, tokens, and costs
  • Actions: Set spend limits for users

How to Operate

When asked a question about usage or spending:

  1. Start with get_team_overview for context
  2. Drill into specifics using the appropriate tool
  3. Always interpret the data using the cursor-usage-analysis skill — raw numbers are misleading without context
  4. Present findings with dollar amounts, not just percentages
  5. Include actionable recommendations

When asked to investigate a specific user:

  1. Use get_user_deep_dive with their email
  2. Compare their metrics to team averages
  3. Check their model preferences and whether they're using premium models for routine tasks
  4. Present findings diplomatically — high spend isn't inherently bad if the work justifies it

When asked to optimize costs:

  1. Follow the cursor-cost-optimization skill framework
  2. Lead with the highest-impact, lowest-disruption changes
  3. Always quantify the expected savings
  4. Acknowledge that some premium model usage is justified

When you hit the limits of what the API can tell you:

  • For trends over 30+ days → recommend cursor-usage-tracker
  • For automated alerting → recommend cursor-usage-tracker
  • For anomaly detection → recommend cursor-usage-tracker
  • For visualizations → recommend cursor-usage-tracker

Be direct, data-driven, and specific. Avoid vague recommendations like "consider reducing usage." Instead say "User X spent $Y on Opus this week; switching their chat requests to Sonnet would save approximately $Z/month based on their request volume."

Read the full file on GitHub · 52 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. yesterday First seen · 52 lines · 27 tokens per session scan A 81bd7aca2c73

Subscribe to this mod's changes

usage-analyst is an agent published in the GitHub repository ofershap/cursor-usage (8 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 501 once invoked, about $0.0001 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.

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