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 rediumvex/ai-marketing-claude --skill market-auditgit clone --depth 1 https://github.com/rediumvex/ai-marketing-claudeWrote 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-audit)<a href="https://agentmods.dev/skills/rediumvex/ai-marketing-claude/market-audit"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-audit/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-audit"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-audit.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 | $0.00093 | $0.01221 |
| Opus 5 | $0.00046 | $0.00611 |
| Sonnet 5 | $0.00019 | $0.00244 |
| Haiku 4.5 | $0.00009 | $0.00122 |
Grade A, and why
market-audit 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Audit — 6-Dimension Site Audit
Your job is to produce a rigorous marketing audit. You coordinate 5 specialist subagents running in parallel, then synthesize their outputs into one decision-grade report.
Phase 1 — Discovery (before spawning agents)
- Fetch the page once with
python3 scripts/analyze_page.py <url>and capture the JSON. Pass this data to every subagent so none of them re-fetches unnecessarily. - Classify the business type (SaaS / E-commerce / Agency / Local / Creator / Marketplace). This adjusts which findings matter most.
- Extract the brand name from the title or logo alt text. Use it consistently in the report.
- Check for prior artifacts (
BRAND-VOICE.md,COMPETITOR-ANALYSIS.md, etc.) and feed relevant excerpts to the matching subagent.
Phase 2 — Parallel Analysis
Spawn these 5 subagents in the same message (one tool block with 5 Agent calls). Each gets: the URL, the analyze_page JSON, the detected business type, and the brand name.
| Subagent | File | Dimension | Weight |
|---|---|---|---|
| Content analyst | agents/market-content.md |
Content & Messaging | 25% |
| Conversion analyst | agents/market-conversion.md |
Conversion Optimization | 20% |
| Technical SEO analyst | agents/market-technical.md |
SEO & Discoverability | 20% |
| Competitive analyst | agents/market-competitive.md |
Competitive Positioning | 15% |
| Strategy analyst | agents/market-strategy.md |
Brand & Trust + Growth | 20% (split) |
Each subagent returns a 0–10 score per sub-dimension, specific findings with quoted evidence, and concrete fixes.
If a subagent fails or returns thin data, fall back to running the checks yourself from the analyze_page JSON — never leave a dimension blank.
Phase 3 — Synthesis
- Normalize scores to 0–100 and apply the weights from the catalog.
- Compute the composite and map to a letter grade.
- Deduplicate findings — if Content and Conversion both flag the weak CTA, mention it once under the higher-impact dimension.
- Rank every finding by revenue impact:
impact = monthly_traffic × expected_CR_lift × ARPU. If traffic and ARPU aren't known, estimate conservatively and flag the assumption. - Generate the action plan in three tiers:
- This week (quick wins) — under 1 day of work each
- This month (high-leverage) — 1–3 weeks of work
- This quarter (strategic) — 1–3 months of work
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 · 101 lines · 93 tokens per session scan A 54af17198769
market-audit is a skill published in the GitHub repository rediumvex/ai-marketing-claude (38 stars, last pushed 5mo ago), licensed MIT. It adds 93 tokens to every session and 1,221 once invoked, about $0.0005 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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