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/rediumvex/ai-marketing-claudenpx agentmods add skills/rediumvex/ai-marketing-claude/market-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/skills/rediumvex/ai-marketing-claude/market-report)<a href="https://agentmods.dev/skills/rediumvex/ai-marketing-claude/market-report"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-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/skills/rediumvex/ai-marketing-claude/market-report"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-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 | $0.00077 | $0.01234 |
| Opus 5 | $0.00039 | $0.00617 |
| Sonnet 5 | $0.00015 | $0.00247 |
| Haiku 4.5 | $0.00008 | $0.00123 |
Grade A, and why
market-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 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Report — Consolidated Deliverable
Compile the outputs of previous /market skills into one cohesive client-ready report. Markdown by default; PDF with --format pdf.
Pre-flight
-
Scan the project root for any of these artifacts:
MARKETING-AUDIT.mdLANDING-CRO.mdSEO-AUDIT.mdBRAND-VOICE.mdCOMPETITOR-ANALYSIS.mdFUNNEL-ANALYSIS.mdLAUNCH-PLAN.md
-
If none exist: tell the user they should run
/market audit <url>first. Offer to run it. -
If some exist: use what's there; don't re-analyze unless data is stale or contradictory.
-
Extract scores, findings, and recommendations from each file. Normalize category names across files (they should already match, but verify).
-
Determine the format —
--format md(default) or--format pdf.
Report Assembly
Build a JSON data structure that feeds either the Markdown template or the PDF generator:
{
"url": "https://example.com",
"brand_name": "Example",
"date": "YYYY-MM-DD",
"business_type": "SaaS",
"overall_score": 62,
"grade": "D",
"executive_summary": "2–4 sentences.",
"categories": {
"Content & Messaging": {"score": 68, "weight": "25%", "top_finding": "..."},
"Conversion Optimization": {"score": 52, "weight": "20%", "top_finding": "..."},
"SEO & Discoverability": {"score": 74, "weight": "20%", "top_finding": "..."},
"Competitive Positioning": {"score": 48, "weight": "15%", "top_finding": "..."},
"Brand & Trust": {"score": 70, "weight": "10%", "top_finding": "..."},
"Growth & Strategy": {"score": 55, "weight": "10%", "top_finding": "..."}
},
"findings": [
{"severity": "Critical", "title": "...", "evidence": "...", "fix": "...", "impact": "..."}
],
"quick_wins": ["..."],
"medium_term": ["..."],
"strategic": ["..."],
"competitors": [
{"name": "...", "positioning": "...", "pricing": "...", "social_proof": "...", "content": "..."}
]
}
Save to /tmp/report_data.json.
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 · 151 lines · 77 tokens per session scan A 33df398bb28b
market-report is a skill published in the GitHub repository rediumvex/ai-marketing-claude (38 stars, last pushed 5mo ago), licensed MIT. It adds 77 tokens to every session and 1,234 once invoked, about $0.0004 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.
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