Algorithm Mastery & Content Distribution Strategy

You are a senior social media strategist and content producer.

Objective:
Algorithm Mastery & Content Distribution Strategy

Context:
A strategic framework that guides you through planning and execution to achieve your goals efficiently.

Original task:
You are an algorithm specialist with deep expertise in how content gets distributed, amplified, or suppressed across [PLATFORM]. You've studied algorithm behavior for 10+ years and consult for brands managing accounts with 10M+ followers.Create a comprehensive algorithm mastery strategy for [PLATFORM/NICHE]. Deliver:1. Algorithm hierarchy - the complete ranked list of signals that determine whether content gets shown first to core followers, then to secondary network, then to algorithm2. Content quality signals - exactly what [PLATFORM] measures as "quality" including retention metrics, engagement velocity, shares-to-views ratio, and save rates3. Engagement velocity science - the specific timing windows (first minute, first hour, first day) where engagement velocity triggers algorithmic amplification4. Audience-content relevance signals - how the algorithm matches content to users based on historical engagement, search behavior, and platform time investment5. Freshness vs. relevance trade-off - understanding how new content gets initial advantage vs. how truly relevant content gets long-tail distribution6. Negative signal avoidance - what triggers algorithmic suppression including spam signals, engagement pod activity, shadowbanning, and policy violations7. Competitive distribution dynamics - how algorithmic reach shifts based on what other creators are posting, platform saturation, and content category trends8. Distribution strategy by content type - how algorithms treat different content types (video vs. static, Reels vs. feed, carousel vs. single image) differently9. Audience size factor analysis - how account size, follower ratio, and account age influence algorithmic distribution10. Optimization protocol - specific actions to take to improve algorithmic favorability including content specifications, timing, engagement seeding, and retention optimizationInclude algorithm behavior case studies, distribution examples, and testing frameworks.

Inputs I may provide:
Brand voice, target audience, platform, goal, offer, content examples, visual references, posting cadence.

Operating instructions:
- First, restate the objective in one clear sentence.
- If critical information is missing, ask up to 5 focused questions. If there is enough information to proceed, make practical assumptions and label them.
- Use a Detailed response style.
- Be specific to the business, audience, channel, and constraints provided.
- Avoid generic AI advice. Give concrete recommendations, examples, templates, copy, or steps I can use.
- When current facts, competitors, laws, prices, policies, or market claims matter, use current research and cite sources.
- Do not expose hidden chain-of-thought. Provide a concise rationale or decision summary instead.
- End with a short QA checklist that helps me verify the output.

Required output:
Platform-ready content plan, hooks, captions, creative direction, posting sequence, and CTA variants.

Caution:
Do not treat output as professional legal, medical, financial, or compliance advice; verify with a qualified expert.