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 ekinciio/saas-growth-marketing-skills --skill competitor-intelgit clone --depth 1 https://github.com/ekinciio/saas-growth-marketing-skillsWrote 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/ekinciio/saas-growth-marketing-skills/competitor-intel)<a href="https://agentmods.dev/skills/ekinciio/saas-growth-marketing-skills/competitor-intel"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/competitor-intel/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/ekinciio/saas-growth-marketing-skills/competitor-intel"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/competitor-intel.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 | $0.00060 | $0.02289 |
| Opus 5.5 | $0.00024 | $0.00916 |
| Sonnet 5.5 | $0.00012 | $0.00458 |
| Haiku 4.5 | $0.00006 | $0.00229 |
Grade A, and why
competitor-intel scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
If the user provides their own API keys, use them for richer competitive intelligence. Enrichment is model-driven: the scanner script does not call these APIs. When a key is set, Claude calls the service's API directly ( How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Intel
Conduct structured competitive analysis for SaaS products, generate sales battle cards, map market landscapes, and build competitive positioning strategies.
First Run
When a user runs /competitor-intel analyze <url> for the first time,
display this intro before starting:
""" 📡 Competitor Intel
What I'll do: Fetch the competitor URL and extract publicly visible signals - positioning, features, CTAs, social channels, and trust elements.
What you'll get: → Competitor profile (value prop, audience, platform) → Strengths and weaknesses → Opportunities against them
Note: I scan only the provided URL (single page). For deeper analysis, provide specific pages (pricing, features, about) separately.
Output: Saved to COMPETITOR-ANALYSIS-REPORT.md Time: ~60 seconds.
Starting... """
Then proceed immediately.
Commands
/competitor-intel analyze <competitor-url>
Run a full competitor analysis by scanning the provided URL and combining it with the user's knowledge.
Steps:
- Accept the competitor URL from the user
- Run
python3 scripts/competitor_scanner.py <url>to extract publicly available page data (title, meta description, headers, CTAs, social links, tech signals). Add--jsonfor machine-readable output. - Ask the user to supplement with any known information about pricing, funding, team size, and market positioning
- Analyze the extracted data against the framework in
references/analysis-framework.md - Generate a structured competitor profile
Output format:
Competitor Analysis: [Company Name]
====================================
Overview:
URL: [url]
Value Proposition: [extracted from meta/headers]
Target Audience: [inferred from messaging]
Product:
Key Features: [from page analysis]
Platform: [web, mobile, desktop]
Integrations: [detected integration page: yes/no]
Marketing:
Social Channels: [detected links]
Blog/Content: [detected: yes/no]
Trust Signals: [count of testimonials, logos, badges]
Primary CTA: [extracted CTA text]
Strengths:
- [Strength 1]
- [Strength 2]
Weaknesses:
- [Weakness 1]
- [Weakness 2]
Opportunities Against:
- [Opportunity 1]
- [Opportunity 2]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 255 lines · 60 tokens per session scan A 3ac4a7e83024
competitor-intel is a skill published in the GitHub repository ekinciio/saas-growth-marketing-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 2,289 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agent-reach
An internet-search and platform-access router for finding information across websites and services such as Reddit, X, GitHub, YouTube, and job sites.
apple-search-ads
When the user wants to set up, optimize, or scale Apple Search Ads (ASA) campaigns — including keyword bidding, match types, campaign structure, Creative Product Sets, CPP routing, and ROAS optimization. Use when the user mentions "Apple Search Ads", "ASA", "Search Ads", "Search tab ads", "Today tab ads", "CPT"…
creator-ugc-marketing
When the user wants to plan, brief, source, or measure organic creator / influencer / UGC marketing for their app — including TikTok creators, Instagram Reels, YouTube Shorts, micro-influencers, paid creator briefs, UGC ad creative for Meta/TikTok, affiliate programs, and seeding strategy. Use when the user mentions…
subscription-lifecycle
When the user wants to optimize their subscription business end-to-end — from trial start through renewal, cancellation, and win-back. Use when the user mentions "subscription lifecycle", "trial conversion", "churn", "cancellation", "win-back", "lapsed subscribers", "dunning", "billing retry", "grace period", "renewal…
web-to-app-funnel
When the user wants to design or optimize the funnel that takes web visitors into installing and onboarding the app — including smart app banners, web-to-app deep links, deferred deep links, web onboarding (Stripe-paid web flow before app install), QR codes, "open in app" CTAs, and the trade-off between paying on web…
app-analytics
When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For…