Use ai-usage-report

Generate a per-user or team-level report on how AI tools are being used

Generate a per-user or team-level report on how AI tools are being used — volume, breadth, and effectiveness signals (e.g. whether AI outputs landed in shipped artifacts vs were generated and discarded). Consumes usage-event logs exported from an MCP server or other telemetry source.

Reach for it when a manager or team lead needs to understand AI adoption beyond raw counts — including spotting "AI theater" where people invoke AI to claim usage but don't act on the outputs.

Install it

npx skilldrop-cli install ai-usage-report                 # Claude Code, your user folder
npx skilldrop-cli install ai-usage-report --project       # Claude Code, this repo
npx skilldrop-cli install ai-usage-report --ide cursor    # or kiro, codex, antigravity, copilot

It is part of the AI engineering pack: --pack ai-engineering installs it with its neighbours.

It ships scripts, so it needs Python 3 (standard library only, nothing to install). What they may touch is declared in its manifest.

Ask for it

Say it the way you would to a colleague. Requests like these route to it:

"Build me a monthly AI adoption report from this usage-event CSV"
"Which teams are generating AI output but never shipping it? Here's the telemetry export"
"Turn this JSONL of MCP usage events into a team rollup I can show execs"
"I suspect some people are invoking Copilot just to hit a usage target — can the logs show that?"

Requests like these belong to a different skill, so it shouldn't answer them:

"Design a golden set and regression gate for our support-bot prompt"
"Define the product success metrics for our new AI summarization feature"
"Write the policy on which AI tools staff may use with customer data"

If another skill answers instead, ask for this one by name: "use ai-usage-report".

Check what you get

A good result:

Numbers are computed from the input, not estimated. If the script cannot compute a metric (missing field), the column is omitted with a note. Never fill with ~ or est. placeholders.
Per-user reports include only users present in the input. Don't infer missing users from elsewhere.
The "AI theater" framing lives in section headers, not in per-user judgments. Say "Single-shot rate: 87%" — never "Alice is doing AI theater."
Privacy posture matches the view. per-user names individuals; team-rollup never names anyone, even in passing.
Recommendations target patterns, not people. "Consider a deeper-engagement workshop for the team" — never "Coach Alice to engage more deeply."
Period is explicit on the report header. Every report shows the dates it covers.
Sample size is honored. If n_users < 5 in a team-rollup, the report is no longer anonymous — refuse to generate the team-rollup view and tell the user why.

See a worked example

weekly engineering team

Use something else when

You don't have usage telemetry yet — the skill consumes data, doesn't collect it. The collection-side spec (event schema, MCP server) lives in the skilldrop repo at docs/designs/ai-usage-telemetry-design.md.
You want to judge specific named people. The skill surfaces patterns; performance judgments based on AI usage are a different (and harder) decision.
The input lacks every effectiveness field. You can still produce a volume report, but call out the limitation explicitly — don't ship a report that looks like effectiveness analysis when it isn't.
Your team is < 5 people and the view is team-rollup. Aggregate views aren't anonymous at that size.

SKILL.md, the instructions the agent reads
Its page on the site

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