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.
npx skills add taoma888/awesome-ai-skills --skill revenue-dashboardgit clone --depth 1 https://github.com/taoma888/awesome-ai-skillsWrote 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/taoma888/awesome-ai-skills/revenue-dashboard)<a href="https://agentmods.dev/skills/taoma888/awesome-ai-skills/revenue-dashboard"><img src="https://agentmods.dev/badge/skills/taoma888/awesome-ai-skills/revenue-dashboard/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/taoma888/awesome-ai-skills/revenue-dashboard"><img src="https://agentmods.dev/badge/skills/taoma888/awesome-ai-skills/revenue-dashboard.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 | $0.00063 | $0.02247 |
| Opus 5 | $0.00032 | $0.01123 |
| Sonnet 5 | $0.00013 | $0.00449 |
| Haiku 4.5 | $0.00006 | $0.00225 |
Grade A, and why
revenue-dashboard 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 12d 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📊 Revenue Dashboard
Mission: Know your number — every morning, automatically.
Aggregate all revenue streams → Calculate key metrics → Report to WeChat daily.
🎯 When to Use
Trigger when:
- "收入" / "赚了多少" / "收益" / "账单"
- "今天赚了多少"
- "收入报表" / "财务报告"
- "佣金到账了吗"
- "帮我看下收入情况"
- Any financial status or monetization health check
⚡ Hard Gate
Before pulling data, confirm scope:
╔══════════════════════════════════════════════════════╗
║ REVENUE BRIEF ║
╠══════════════════════════════════════════════════════╣
║ Income Streams Active: ║
║ ☑ Affiliate Commissions ║
║ ☑ Ad Revenue (display/sponsored) ║
□ ☐ Subscription/Membership ║
□ ☐ Digital Product Sales ║
□ ☐ Services/Consulting ║
□ ☐ Other: <specify> ║
║ ║
║ Reporting Cadence: DAILY / WEEKLY / MONTHLY ║
║ Alert Threshold: ¥X (notify if daily < X) ║
╚══════════════════════════════════════════════════════╝
🔁 The Revenue Intelligence Loop
STEP 1: COLLECT
→ Pull data from all income sources (APIs, manual entry)
→ Normalize currencies and time zones
↓
STEP 2: CALCULATE
→ Compute MRR, ARPU, LTV, ROAS, conversion rates
→ Compare vs. previous period
↓
STEP 3: ANALYZE
→ Identify top performers and underperformers
→ Detect anomalies (sudden drops or spikes)
→ Calculate channel attribution
↓
STEP 4: REPORT
→ Format daily WeChat digest
→ Flag issues requiring attention
→ Recommend specific actions
↓
STEP 5: ALERT
→ If revenue < alert threshold → immediate notification
→ If anomaly detected → investigate + notify
Step 1: COLLECT — Data Sources
Affiliate Networks (API)
| Network | Data Available | Update Frequency |
|---|---|---|
| 阿里云百炼 | Clicks, Conversions, Commission | Daily |
| Amazon Associates | Earnings, Orders | Daily |
| Awin | Impressions, Clicks, Commission | Daily |
| ShareASale | Sales, Commission | Daily |
| CJ Affiliate | Performance | Daily |
What ships with it
1 file 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.
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.
- 12d ago First seen · 307 lines · 63 tokens per session scan A 051f863df247
revenue-dashboard is a skill published in the GitHub repository taoma888/awesome-ai-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 2,247 once invoked, about $0.0003 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.
Other skills, from other repositories
afrexai-startup-metrics-engine
Complete startup metrics command center — from raw data to investor-ready dashboards. Covers every stage (pre-seed to Series B+), every model (SaaS, marketplace, consumer, hardware), with diagnostic frameworks, benchmark databases, and board-ready reporting.
telegram-alerts-hardened
Send formatted trading alerts, portfolio updates, and market signals via Telegram. Supports price alerts, stop-loss notifications, win/loss reporting, and scheduled summaries. Use when you need Telegram notifications for trades, price alerts, portfolio updates, or automated trading reports.
files
A reusable skill for performing deep quantitative analysis on any equity option position. Combines technical analysis, multi-regime Monte Carlo simulation, ARIMA time-series forecasting, ML regression models (Gradient Boosting + Random Forest), and historical pattern matching to produce a sell-vs-hold decision with…
optionscope-app
Drive the user's locally-running OptionScope trading app autonomously — navigate its pages (dashboard / trade replay / spot replay), take screenshots, and read live structured state through MCP tools or plain HTTP endpoints. Use when the user asks to inspect, automate, test, or control OptionScope, or wants an agent…
commission-calculator
Calculate realistic affiliate earnings projections before committing to a program. Use this skill when the user asks about affiliate earnings, projecting income, calculating commissions, estimating how much they can make, comparing program payouts, or says "how much can I make promoting X", "calculate my affiliate…
html-ppt-zhangzara-long-table
OpenDesign's unit-economics and BYOK cost model: the assumptions, the sensitivity, and why it scales. Built as a decision-grade data & finance deck for CFO, investors.