usage-report

A command that creates a report about a team’s use of an AI coding tool during the current billing period. It covers members, spending, models, and accepted agent edits.

In plain words
What is it for?
Reviewing team membership and activity, breaking down spending, examining model adoption, measuring accepted AI edits, and finding cost or usage anomalies.
Why use it?
Usage and spending data is spread across several reports, making it hard to see the full picture. This command gathers the needed information into one team report with possible concerns and recommendations.

Command

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 commands/ofershap/cursor-usage/usage-report
Clone the repo
git clone --depth 1 https://github.com/ofershap/cursor-usage
Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 288 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 $0.00017 $0.00288
Opus 5 $0.00009 $0.00144
Sonnet 5 $0.00003 $0.00058
Haiku 4.5 $0.00002 $0.00029

Measured yesterday against content hash 9cdc5f4ef7e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

usage-report 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:

commands/usage-report.md · 46 lines

What it actually says

Usage Report

Generate a team usage report by following these steps:

  1. Call get_team_overview to get the high-level summary (members, spend, DAU, top models).
  2. Call get_spending with allPages: true to get the full spending breakdown.
  3. Call get_model_usage for the last 7 days to show recent model adoption trends.
  4. Call get_agent_edits for the last 7 days to show AI effectiveness metrics.

Present the report in this structure:

Team Summary

  • Total members, active members, DAU trend
  • Total spend this cycle, average per member
  • Billing cycle dates

Spending Breakdown

  • Top 10 spenders with amounts
  • Spend distribution (median, p75, p90, max)
  • Anyone exceeding 3x the median (flag as potential concern)

Model Adoption

  • Top 5 models by message volume
  • Premium vs standard model split
  • Any model concentration risks

AI Effectiveness

  • Agent edit acceptance rate
  • Tab completion acceptance rate
  • Lines of code accepted vs suggested

Recommendations

  • Cost optimization opportunities (reference the cursor-cost-optimization skill)
  • Users who might benefit from model guidance
  • Any anomalies worth investigating
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 · 46 lines · 17 tokens per session scan A 9cdc5f4ef7e5

Subscribe to this mod's changes

usage-report is a command published in the GitHub repository ofershap/cursor-usage (8 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 288 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.