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ai-usage-report

ai-usage-report is a skill for Claude Code from sananthanarayan/skilldrop. It costs 96 tokens per session (1,931 once invoked), scanned A, original, MIT.

A reporting skill that turns exported logs of AI-tool activity into summaries for individual users or teams. The logs record events such as AI work performed and whether its output reached a shipped result.

In plain words
What is it for?
Use it with a CSV or JSONL usage log to produce a per-user or team-level report for a week, month, or chosen date range.
Why use it?
It separates useful AI-assisted work from activity that was generated but discarded, so managers can read effectiveness signals instead of raw event counts alone.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_SKILL_DIR} variable. Also seen: mentions Claude Code.

Not installable on its own: it runs a file from its repository that does not travel with it. Clone the repository, or install whatever ships that file. The line is --view per-user --period week --out ./out/ai-usage-week.md.

Part of the skilldrop plugin — 51 skills, 4 agents shipped together

not rated 2 22d ago A scan 96 tokens original MIT
View the source file sananthanarayan/skilldrop sananthanarayan.github.io
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add sananthanarayan/skilldrop
Claude Code
/plugin install skilldrop

Made for: Claude Code.

Or install skilldrop, the plugin that ships this one along with the rest of its 51 skills, 4 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ai-usage-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-usage-report.svg)](https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-usage-report)
Your own site
<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-usage-report"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-usage-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,931 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 5.1 $0.00096 $0.01931
Opus 5 $0.00048 $0.00966
Sonnet 5 $0.00019 $0.00386
Haiku 4.5 $0.00010 $0.00193

Measured 5d ago against content hash ef2156acc932, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

ai-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 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/build_report.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/ai-usage-report/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ai-usage-report

You help the user turn a raw AI-usage log into a readable, useful report — one that distinguishes "people doing real work with AI" from "people pressing the button to be seen pressing the button."

This skill consumes telemetry that someone else collected (an MCP server, an IDE telemetry pipeline, a manual CSV export). It does not collect the data itself. Your job is to:

  1. Take the input file from the user.
  2. Run the report script to compute aggregates and effectiveness signals.
  3. Wrap the output with a short executive narrative tailored to the requesting audience.

How to respond

  1. Confirm three inputs before generating anything:

    • Input file. A CSV or JSONL of usage events. Schema lives in templates/usage-event-schema.md. If the user's file columns don't match the schema, map the columns with them before running — don't guess at field names.

    • Reporting period. week, month, or explicit --from <date> --to <date>. If the user gives a fuzzy period ("recently"), pick the last 7 calendar days and call out your choice.

    • View. Exactly one of:

      • per-user — named breakdown, manager view, includes effectiveness scores
      • team-rollup — aggregate-only, no individual names, safe for staff/exec audiences
      • effectiveness — focused on AI-theater flags and outputs-not-consumed signals

      If the user hasn't picked a view, ask. Defaults aren't safe here — the wrong view in the wrong audience's hands creates a workplace problem.

  2. Call out what the data does NOT support before running. If the events have no output_consumed field, you cannot compute effectiveness — say so. If session_id is missing, you cannot compute breadth-per-session. The report should not invent numbers it doesn't have evidence for. Read the input's header (CSV) or first object (JSONL) and list which optional fields are present.

  3. Run the build script.

    # Claude Code
python3 "${CLAUDE_SKILL_DIR}/scripts/build_report.py" /path/to/events.csv \
  --view per-user --period week --out ./out/ai-usage-week.md

# Other IDEs (from the skill folder)
cd path/to/ai-usage-report && python3 scripts/build_report.py /path/to/events.csv \
  --view per-user --period week --out ./out/ai-usage-week.md

Read the full file on GitHub · 112 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 5d ago First seen · 112 lines · 96 tokens per session scan A ef2156acc932

Subscribe to this mod's changes

ai-usage-report is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 22d ago), licensed MIT. It adds 96 tokens to every session and 1,931 once invoked, about $0.0005 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.

Analytics and metricsadragent-skillsai-agentsanthropicclaudeclaude-code
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