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Found 71 Skills
When the user wants to turn content into revenue, build a content-led GTM motion, reverse engineer distribution, or repurpose content across platforms. Also use when the user mentions 'content marketing,' 'content-led growth,' 'content to pipeline,' 'distribution,' 'content repurposing,' 'content strategy,' 'thought leadership,' 'newsletter,' 'content flywheel,' 'organic growth.' This skill covers content-to-revenue systems from creation through pipeline attribution.
Performance attribution, trade analytics, and strategy optimization
Input the original caption, creator handle, and brand voice and receive 3 ready-to-post captions for reposting creator content to your brand's social channels with proper credit and attribution. This skill should be used when writing a repost caption for a creator's content, drafting captions to reshare UGC on your brand account, creating branded captions for reposting influencer content, writing credit captions for sharing creator videos on your brand feed, adapting a creator's post for your brand's Instagram or TikTok, generating repost captions with proper creator attribution, repurposing creator content captions for brand channels, or writing share captions for UGC reposts. For generating ad copy from creator content, see paid-ad-copy-adapter. For writing a paid social brief from whitelisted posts, see paid-social-creative-brief-from-creator-content. For building content briefs before production, see creator-content-concept-generator.
Systematic workflow for CodeRabbit reviews - local CLI, PR threads, and commit attribution
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
Remove AI generation traces from text. Suitable for editing or reviewing text to make it sound more natural and more like human writing. This is a comprehensive guide based on Wikipedia's "Signs of AI writing". It detects and fixes the following patterns: exaggerated symbolic meaning, promotional language, superficial analysis ending in -ing, vague attribution, overuse of em dashes, rule of three, AI vocabulary, negative parallelism, excessive connecting phrases.
Remove signs of AI-generated writing from text (formerly human-writing). Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
Remove AI-generated traces from text. Suitable for editing or reviewing text to make it sound more natural and human-written. A comprehensive guide based on Wikipedia's "Signs of AI Writing". Detect and fix the following patterns: exaggerated symbolism, promotional language, superficial analysis ending with -ing, vague attribution, overuse of dashes, rule of three, AI vocabulary, negative parallelism, excessive connective phrases.
Identifies and removes AI writing patterns from text. Use when editing drafts, reviewing content, or rewriting text that sounds artificial. Detects inflated symbolism, promotional language, vague attributions, AI vocabulary, and structural patterns like rule-of-three overuse.
One-click comprehensive analysis of a stock/company. Collect data from five dimensions - stock price, news sentiment, industry comparison, market environment, and official company website - simultaneously through parallel sub-agents, then conduct cross-analysis, causal attribution, and trend prediction in the main thread, and output a standardized analysis report. Trigger words: Analyze XX stock, analyze TICKER, How is XX, Is XX worth buying? Supports A-shares and U.S. stocks.
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.