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 Ootto-AI/claude-content-skills --skill competitor-teardowngit clone --depth 1 https://github.com/Ootto-AI/claude-content-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/ootto-ai/claude-content-skills/competitor-teardown)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/competitor-teardown"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/competitor-teardown/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/ootto-ai/claude-content-skills/competitor-teardown"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/competitor-teardown.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00499 |
| Opus 5 | $0.00030 | $0.00249 |
| Sonnet 5 | $0.00012 | $0.00100 |
| Haiku 4.5 | $0.00006 | $0.00050 |
Grade A, and why
competitor-teardown 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.
What it actually says
Competitor Teardown — the pattern behind a rival's best posts
Reverse-engineers a rival account's best posts into the patterns, hooks, and posting cadence actually driving their growth.
When to use
A rival is growing and you want the mechanism, not a vibe.
What you'll need
Their handle, or 5-10 of their posts with engagement numbers. [agent-reach](../agent- reach/SKILL.md) can pull them.
Instructions
Give Claude the input and run this.
You are my competitive analyst. Account: [HANDLE]. Posts: [paste 5-10 with views/likes/comments].
1. WINNERS vs REST: which posts beat their median, and the trait the winners share that the others lack.
2. HOOK PATTERN: write their reusable opening as a fill-in template.
3. STRUCTURE: the beat shape they repeat.
4. CADENCE: how often they post, the format mix, what they do after something lands.
5. THE GAP: what their comments keep asking for that they are NOT making. That gap is my lane.
6. MY THREE: three reels I could make this week in that gap, in my voice.
Give me the pattern, never their copy.
Honesty: This produces a pattern to model. Reproducing their actual words gets you demoted by the platform and makes you a worse version of them.
Next: comment-mining → viral-hook-writer
Built by Ootto — the AI autopilot that connects your tools once and runs the busywork for you, automatically. Book a demo →
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 · 53 lines · 60 tokens per session scan A f4fe0b1aab06
competitor-teardown is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 20d ago), licensed MIT. It adds 60 tokens to every session and 499 once invoked, about $0.0003 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.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.