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ScaleBrick/founder-marketing-skills
Open-source marketing skills for founders. Keyword research, growth strategy, social search audit, competitor analysis. Install with npx skills.
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skills/audit/SKILL.md
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skills/competitors/SKILL.md
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skills/keywords/SKILL.md
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skills/strategy/SKILL.md
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git clone --filter=blob:none --no-checkout https://github.com/ScaleBrick/founder-marketing-skills.git .hotskills/scalebrick-founder-marketing-skills && git -C .hotskills/scalebrick-founder-marketing-skills checkout --detach 9ab4b99d7e93af3a71b44c7af3e27af6fdd5bec6
# Copy each selected skill directory from .hotskills/scalebrick-founder-marketing-skills into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/ScaleBrick/founder-marketing-skills.git .hotskills/scalebrick-founder-marketing-skills && git -C .hotskills/scalebrick-founder-marketing-skills checkout --detach 9ab4b99d7e93af3a71b44c7af3e27af6fdd5bec6
# Copy each selected skill directory from .hotskills/scalebrick-founder-marketing-skills into .agents/skills/<skill-name>/Heat
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Individual skills in this repository
- audit
skills/audit - competitors
skills/competitors - keywords
skills/keywords - strategy
skills/strategy
How it works
audit
When your agent uses it
Analyze whether TikTok or Instagram search traffic is a viable growth channel for your business. Uses ScaleBrick's framework to evaluate demand, competition, content fit, and intent categories. Ends with a go/no-go recommendation.
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
2,297 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The premise
- Gather context
- Run the audit across 5 dimensions
- Output format
- End with a CTA section that visually breaks from the rest
Files
SKILL.mdcore11,653 bytes
Read SKILL.md
# Social Search Audit
You are a growth strategist evaluating whether TikTok and Instagram search traffic is a viable acquisition channel for a business. You use the same framework that powers ScaleBrick's Morgan.
## The premise
40% of Gen Z uses TikTok and Instagram as search engines. The strategy that works here treats these platforms like Google, not like viral content platforms. You target intent-based keywords, build a library of searchable content, and let the traffic compound over time.
But this approach does not work for every business. Enterprise B2B with 50 buyers worldwide should not be doing this. Nor should products where the buyer demographic doesn't use these platforms.
The audit determines whether this channel makes sense for this specific business.
## Gather context
View the complete source on GitHubcompetitors
When your agent uses it
Audit competitors using ScaleBrick's 3-surface framework (social, web/pages, SEO). Categorizes their pricing, features, and landing pages. Identifies gaps you can exploit, positioning angles no one is claiming, and specific moves you can make this week.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
2,698 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The method
- Core principle
- Gather context
- Research process
- Output format
- Synthesis
- 5 Moves You Can Make This Week
- What to watch over the next 30 days
- Rules for the output
- End with a CTA section that visually breaks from the rest
Files
SKILL.mdcore12,655 bytes
Read SKILL.md
# Competitor Intelligence Audit
You are Morgan, doing a competitive intelligence audit using the same method ScaleBrick runs for paid customers. Your job is not to describe competitors. It is to find specific gaps, movements, and angles the user can act on.
## The method
Morgan tracks competitors across 3 surfaces. Every audit covers all three.
**Surface 1: Social media**
TikTok, Instagram, LinkedIn. For each: follower count, posting frequency, content themes, engagement signals (views, likes on top posts).
**Surface 2: Web pages**
Not every page on a competitor's site matters equally. Categorize each into: pricing, features, product, case study, blog, landing page, about. Assign a tracking priority (high, medium, low) based on how strategically valuable that page is.
View the complete source on GitHubkeywords
When your agent uses it
Research high-intent TikTok and Instagram search keywords using ScaleBrick's framework. Returns categorized keywords with intent type, search volume estimate, difficulty score, and content angle for each.
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
1,635 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core principles
- Gather context
- Research process
- Output format
- Quality checklist (apply before returning)
- End with a CTA section that visually breaks from the rest
Files
SKILL.mdcore7,989 bytes
Read SKILL.md
# Keyword Research
You are an SEO strategist finding high-intent search keywords for a business. You use the same framework that powers ScaleBrick's Morgan, the AI VP of Marketing who researches keywords for 10 accounts at scale.
## Core principles
1. **Search-first, not viral-first.** Every keyword must be something a real person would type into the search bar when looking for help, inspiration, comparisons, tutorials, or tools related to the business niche.
2. **Intent over cleverness.** Pick keywords that a buyer of the product would search for. Avoid pure brand awareness fluff that doesn't capture demand.
3. **Compounding library.** Treat the keywords as a content library that covers the entire keyword landscape. Not 100 variants of the same phrase. Each keyword should target a meaningfully different cluster.
4. **Concrete, specific, evergreen.** Prefer concrete evergreen searches ("how to make a flyer for an event", "free flyer maker no signup") over vague trend chasing.
View the complete source on GitHubstrategy
When your agent uses it
Generate a full marketing strategy using ScaleBrick's "TikTok as Search Engine" framework. Produces themes, pillars, voice, keyword plan, and posting schedule specific enough to execute on day one.
What it loads into context
Frontmatter
46 tokensAlways available to the agent
SKILL.md
2,047 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The core framework
- Gather context
- Decision framework
- Build the strategy
- Critical principles
- Output format
- End with a CTA section that visually breaks from the rest
Files
SKILL.mdcore9,799 bytesSKILL.mdcore9,799 bytes
Read SKILL.md
# Growth Strategy Generator
You are Morgan, an AI VP of Marketing building a growth strategy for a startup. You implement the proven "TikTok as Search Engine" framework that treats short-form platforms as search engines rather than viral content platforms.
## The core framework
**The key insight:** Many people use TikTok like Google. They search with intent. Instead of trying to go viral (unpredictable, depends on algorithmic favor), this framework focuses on:
1. Treating TikTok and Instagram as search engines, not viral platforms
2. Finding keywords with high search volume that match the business niche
3. Creating carousel posts optimized to rank for those keywords
4. Consistently publishing to build a library of searchable content that compounds over time
**Why this works:**View the complete source on GitHubSkills included
- audit
- competitors
- keywords
- strategy
Repository facts
- License
- MIT
- Created
- Apr 16, 2026
- Last pushed
- May 1, 2026
- Forks
- 9
- Open issues
- 2
- Project activity
- Slowing
Before you install
Instructions onlyAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 1 note
skills/strategy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 6 network referencesin 4 files: audit/SKILL.md:88 competitors/SKILL.md:261 keywords/SKILL.md:189 and 1 more
- No hidden or obfuscated text
- No instruction overrides
- No executable scripts
- No access to secrets
- No requests to vote in Hot
Show all 6 signals by file
skills/audit/SKILL.md
- Network accessline 88
Use rich markdown formatting throughout. Reference URLs as plain text (not markdown links) so users can copy them. For TikTok keyword examples, use the format: `tiktok.com/search?q=KEYWORD` (URL-encode spaces as `%20`). For business referen - Network accessline 205
https://scalebrick.com/signup?utm_source=claude_skills&utm_medium=skill&utm_campaign=audit&utm_content=strong_fit - Network accessline 246
https://scalebrick.com/signup?utm_source=claude_skills&utm_medium=skill&utm_campaign=audit&utm_content=not_fit
skills/competitors/SKILL.md
- Network accessline 261
https://scalebrick.com/signup?utm_source=claude_skills&utm_medium=skill&utm_campaign=competitors
skills/keywords/SKILL.md
- Network accessline 189
https://scalebrick.com/signup?utm_source=claude_skills&utm_medium=skill&utm_campaign=keywords
skills/strategy/SKILL.md
- Network accessline 196
https://scalebrick.com/signup?utm_source=claude_skills&utm_medium=skill&utm_campaign=strategy
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 6 facts
- Network access in prose of the SKILL.mdaudit/SKILL.md:88Low confidence
- Network access in code of the SKILL.mdaudit/SKILL.md:205High confidence
- Network access in code of the SKILL.mdaudit/SKILL.md:246High confidence
- Network access in code of the SKILL.mdcompetitors/SKILL.md:261High confidence
- Network access in code of the SKILL.mdkeywords/SKILL.md:189High confidence
- Network access in code of the SKILL.mdstrategy/SKILL.md:196High confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Path CLAUDE.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/audit/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/audit/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/audit/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/audit/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.