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/tal7aouy/marketkitnpx agentmods add skills/tal7aouy/marketkit/market-report-pdfWrote 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/tal7aouy/marketkit/market-report-pdf)<a href="https://agentmods.dev/skills/tal7aouy/marketkit/market-report-pdf"><img src="https://agentmods.dev/badge/skills/tal7aouy/marketkit/market-report-pdf/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/tal7aouy/marketkit/market-report-pdf"><img src="https://agentmods.dev/badge/skills/tal7aouy/marketkit/market-report-pdf.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 | $0.00000 | $0.03417 |
| Opus 5 | $0.00000 | $0.01708 |
| Sonnet 5 | $0.00000 | $0.00683 |
| Haiku 4.5 | $0.00000 | $0.00342 |
Grade A, and why
market-report-pdf 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 11d 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.
This is a copy
97% identical to market-report-pdf — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Marketing Report Generator
Skill Purpose
Generate a professional, visually polished PDF marketing report using the Python script scripts/generate_pdf_report.py. This skill collects all available audit and analysis data, structures it into the expected JSON format, invokes the script, and produces a branded PDF with score gauges, bar charts, comparison tables, findings, and a prioritized action plan.
When to Use
- User wants a PDF version of the marketing report (not just Markdown)
- User is preparing a deliverable for a client presentation
- User asks for a "polished report", "client-ready report", or "PDF report"
- User wants a visual report with charts and scores
- Triggered by
/market report-pdfor/market report-pdf <domain>
When to Use PDF vs Markdown
| Format | Best For | Pros | Cons |
|---|---|---|---|
| Client presentations, email attachments, sales collateral | Professional appearance, consistent formatting, visual charts, printable | Harder to edit, requires Python script | |
| Markdown | Internal use, quick reference, iterative editing, version control | Easy to edit, readable in any editor, git-friendly | Less visually polished, no charts |
Rule of thumb: If the report is going to a client or prospect, use PDF. If it is for internal use or further editing, use Markdown.
How to Execute
Step 1: Collect All Available Data
Gather data from all previous skill runs. Check for these files in the project directory:
Primary data sources:
MARKETING-AUDIT.md-- Overall audit resultsLANDING-CRO.md-- Landing page conversion analysisSEO-AUDIT.md-- SEO findingsBRAND-VOICE.md-- Brand voice analysisCOMPETITOR-ANALYSIS.md-- Competitor comparison dataFUNNEL-ANALYSIS.md-- Funnel analysisSOCIAL-AUDIT.md-- Social media auditEMAIL-AUDIT.md-- Email marketing auditAD-AUDIT.md-- Advertising audit
If no previous data exists:
- Recommend the user run
/market audit <url>first for the best results - If the user insists on generating a report without prior audits, analyze the provided URL directly and build the data structure from scratch
- Use the analyze_page.py script to gather automated data:
python scripts/analyze_page.py <url>
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.
- 11d ago First seen · 349 lines · 0 tokens per session scan A a874a286260d
market-report-pdf is a skill published in the GitHub repository tal7aouy/marketkit (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,417 tokens. A static security scan graded it A with 0 findings. It is 97% identical to market-report-pdf, differing in 2 lines, and is treated as a copy.
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