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-competitorsgit 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-competitors)<a href="https://agentmods.dev/skills/rediumvex/ai-marketing-claude/market-competitors"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-competitors/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-competitors"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-competitors.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 | $0.00082 | $0.01155 |
| Opus 5 | $0.00041 | $0.00577 |
| Sonnet 5 | $0.00016 | $0.00231 |
| Haiku 4.5 | $0.00008 | $0.00115 |
Grade A, and why
market-competitors 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Competitors — Competitive Intelligence
Identify 3–5 relevant competitors, analyze their public-facing marketing, and tell the client where to attack and where to defend.
Step 1 — Identify competitors
Sources in order:
- Ask the user if they already have a list.
- Run
scripts/analyze_page.pyon the client's homepage for vocabulary. - Use
WebSearchfor:"<brand> vs","<brand> alternatives","best <category>","<primary keyword> tools". - Pick the top 3–5 competitors that are actually competing for the same user, not just in the same category.
State your selection and why before running the analysis.
Step 2 — Scan each competitor
Run python3 scripts/competitor_scanner.py <url> on each (or use analyze_page.py if the scanner errors). For each competitor capture:
| Field | Source |
|---|---|
| Hero headline | H1 or first large text |
| Subhead / value prop | Paragraph under H1 |
| Primary CTA text | Most prominent button |
| Pricing (if visible) | Pricing page |
| Social proof | Logos, counts, reviews, testimonials |
| Positioning archetype | See below |
| Content cadence (if blog) | Last 3 post dates |
| Tech/tracking stack | Scripts detected |
Step 3 — Historical evolution (Wayback Machine)
For the client and each competitor, fetch:
https://web.archive.org/web/2023*/<competitor-url>
https://web.archive.org/web/2024*/<competitor-url>
Compare the 2023 hero to the 2025 hero. Has their positioning shifted? Did they pivot? This is often where you spot pricing changes, audience pivots, or abandoned features. (Skip gracefully if the Wayback Machine fails.)
Step 4 — Positioning matrix
Plot every competitor plus the client on a 2×2 (pick the two most relevant axes for the category):
- SaaS: Simplicity ↔ Power × Price
- E-com: Premium ↔ Value × Niche ↔ Mass
- Agency: Specialist ↔ Generalist × Boutique ↔ Enterprise
- Creator: Entertainment ↔ Education × Broad ↔ Niche
Step 5 — Comparison matrix
| Client | Comp A | Comp B | Comp C | |
|---|---|---|---|---|
| One-liner | ||||
| Target audience | ||||
| Price anchor | ||||
| Primary CTA | ||||
| Social proof | ||||
| Key differentiator | ||||
| Weakness |
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 · 128 lines · 82 tokens per session scan A be61a6321989
market-competitors is a skill published in the GitHub repository rediumvex/ai-marketing-claude (38 stars, last pushed 5mo ago), licensed MIT. It adds 82 tokens to every session and 1,155 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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