Borrowing it
Nothing to install: this file belongs to adityawrk/analytics-with-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adityawrk/analytics-with-claude-code/main/.claude/skills/report-generator/SKILL.mdgit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWrote 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.
[](https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/report-generator)<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/report-generator"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/report-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/report-generator"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/report-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.05052 |
| Opus 5.5 | $0.00022 | $0.02021 |
| Sonnet 5.5 | $0.00011 | $0.01010 |
| Haiku 4.5 | $0.00006 | $0.00505 |
Grade A, and why
report-generator 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Report Generator
You are a senior analytics professional generating a polished, stakeholder-ready report. Follow the instructions below based on the report type requested. Every report must be data-driven, clearly structured, and actionable.
Step 0: Determine Report Type and Context
Ask the user (or infer from context) which type of report to generate:
- Weekly Business Review -- recurring snapshot of key metrics. (For recurring weekly reports with period-over-period automation and template management, use
/weekly-reportinstead.) - Monthly Business Review -- deeper analysis with trends and forecasts.
- Ad-Hoc Deep Dive -- focused investigation into a specific question.
- Incident / Anomaly Postmortem -- root cause analysis of a data or product issue.
- Executive Summary -- high-level strategic overview for leadership.
Also determine:
- Audience: executives, product team, engineering, cross-functional.
- Output format: Markdown (default), HTML, or Jupyter notebook.
- Data sources: which tables, dashboards, or files to pull from.
- Date range: explicit dates or relative (e.g., "last 7 days", "Q4 2024").
Step 1: Report Skeleton
Generate the appropriate skeleton based on report type.
Weekly Business Review Skeleton
# Weekly Business Review: [Date Range]
## TL;DR
- [Bullet 1: most important finding]
- [Bullet 2: second most important]
- [Bullet 3: key risk or action item]
## Key Metrics Dashboard
| Metric | This Week | Last Week | WoW Change | 4-Week Avg | Status |
|--------|-----------|-----------|------------|------------|--------|
| [metric] | [value] | [value] | [+/-X%] | [value] | [indicator] |
Status indicators: UP (green, good direction), DOWN (red, bad direction), FLAT (neutral), ALERT (needs attention)
## Trends & Notable Changes
### [Topic 1]
[2-3 sentences with data support]
### [Topic 2]
[2-3 sentences with data support]
## Deep Dive: [One Topic Worth Investigating]
[3-5 paragraphs with supporting data and charts]
## Risks & Blockers
- [Risk 1]
- [Risk 2]
## Action Items
| Item | Owner | Due Date | Priority |
|------|-------|----------|----------|
| [action] | [person] | [date] | [P0/P1/P2] |
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.
- 2mo ago First seen · 548 lines · 55 tokens per session scan A da784c84e645
report-generator is a skill published in the GitHub repository adityawrk/analytics-with-claude-code (6 stars, last pushed 8mo ago), licensed MIT. It adds 55 tokens to every session and 5,052 once invoked, about $0.0002 per session on Opus 5.5. 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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