Virality And Retention

May 3, 2026 ยท View on GitHub

Content Machine treats virality as an editorial and learning system, not as a guarantee. The goal is to make better shorts by checking hooks, retention, platform fit, packaging, and real outcomes before repeating a format.

Skill Sequence

Use this order when a short needs serious social-native review:

  1. short-form-archetype-research chooses the right format and quality gates.
  2. hook-packaging generates hook, title, cover, opening-frame, and metadata variants.
  3. short-form-production-playbook keeps the script, visuals, captions, and review loop editorially coherent.
  4. virality-review checks hook, retention, clarity, payoff, risk, and platform fit.
  5. retention-pass catches dead air, weak first frames, repetitive visuals, caption overload, and payoff problems.
  6. platform-packaging prepares platform-specific titles, descriptions, hashtags, CTAs, disclosures, and upload checklists.
  7. publish-prep-review remains the final rendered-video readiness gate.
  8. metrics-feedback-loop turns publish receipts and performance metrics into style-profile or niche-profile updates.

Rules

  • Do not promise guaranteed views, reach, conversions, or algorithmic performance.
  • Treat reference winners as structure to learn from, not footage to copy into a new render.
  • Keep packaging truthful to the script and source evidence.
  • Optimize for retention, saves, shares, qualified comments, follows, clicks, or conversions instead of raw views alone.
  • Preserve artifacts so another agent can see why a hook, platform package, or style-profile update was chosen.

Minimal Artifacts

For a serious production run, expect:

  • packaging-variants.v1.json
  • virality-review.v1.json or a compact equivalent review note
  • retention-pass.v1.json or scene/timestamp-specific retention notes
  • platform_packaging.json
  • publish-prep/
  • metrics-feedback.v1.json after the short has real performance data

Practical Prompt

Use Content Machine's virality and retention workflow on this short.
Choose the archetype, generate hook/title/cover variants, review the
script or render for hook and retention problems, prepare platform
packaging, and do not call it publish-ready until publish-prep passes.
After publishing metrics exist, turn the result into repeat/kill/test
learning for the relevant style or niche profile.