Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pronpx agentmods add commands/indranilbanerjee/digital-marketing-pro/performance-reportWrote 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/commands/indranilbanerjee/digital-marketing-pro/performance-report)<a href="https://agentmods.dev/commands/indranilbanerjee/digital-marketing-pro/performance-report"><img src="https://agentmods.dev/badge/commands/indranilbanerjee/digital-marketing-pro/performance-report/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/commands/indranilbanerjee/digital-marketing-pro/performance-report"><img src="https://agentmods.dev/badge/commands/indranilbanerjee/digital-marketing-pro/performance-report.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 | $0.00017 | $0.01020 |
| Opus 5 | $0.00009 | $0.00510 |
| Sonnet 5 | $0.00003 | $0.00204 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
performance-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 10d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Report
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate a structured marketing performance report that transforms raw data into actionable insights. Covers KPI tracking, period-over-period trends, anomaly detection, channel benchmarking, and prioritized optimization recommendations.
Trigger
User runs /digital-marketing-pro:performance-report or asks for a marketing report, performance review, channel analysis, or campaign results summary.
Inputs
Gather the following from the user. If not provided, ask before proceeding:
-
Reporting period — date range for the report (e.g., "last 30 days", "Q1 2026", "Feb 1-28")
-
Channels to cover — which marketing channels to include:
- All channels (default)
- Specific: organic search, paid search, social media, email, direct, referral
-
Data source — raw performance data in any of these forms:
- Pasted directly into the conversation
- CSV or spreadsheet file
- Connected platform (if analytics or advertising connectors are available)
-
Comparison period — previous period, year-over-year, or custom benchmark
-
Report audience — who will read this:
- Executive — high-level summary with headline metrics and strategic recommendations
- Tactical — detailed breakdown with granular data and specific optimizations
-
KPIs of interest (optional) — specific metrics to focus on, or use defaults:
- Traffic: sessions, users, pageviews, bounce rate
- Conversions: conversion rate, leads, revenue, ROAS
- Engagement: time on site, pages per session, social engagement
- Email: open rate, click rate, unsubscribe rate
Process
-
Load brand context — apply brand goals, targets, and industry benchmarks from the active profile
-
Ingest data — validate and normalize the provided performance data
-
Calculate core KPIs — per channel: traffic, conversions, revenue, ROAS, CPA, engagement metrics, growth rates
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.
- 10d ago First seen · 114 lines · 17 tokens per session scan A 67ed5a65b610
performance-report is a command published in the GitHub repository indranilbanerjee/digital-marketing-pro (801 stars, last pushed 2d ago), licensed MIT. It adds 17 tokens to every session and 1,020 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 2026-08-30.
Other commands, from other repositories
brand-setup
Configure brand voice, terminology, compliance guardrails, and style guide for content production.
output-folder
Print the absolute path to the user-visible ContentForge output folder and open it in the OS file manager.
create-content
Run the full 10-phase content production pipeline — research, draft, fact-check, humanize, and publish.
audit-content
Audit your content library for freshness decay, coverage gaps, and optimization opportunities.
content-brief
Generate a research-backed content brief with keyword data, competitor analysis, search intent, and SEO strategy.
publish
Publish finished content to Webflow or WordPress with preview, verification, and HTML export fallback.