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Socialseal content adjustment recommendations

Skill OpenSealAI/agent-skills/skills/socialseal-content-adjustment-recommendations

Open-source Agent Skills for SocialSeal: social-search strategy, the vNext creator/UGC production engine (reference videos -> Video DNA -> blueprint -> brief -> Asset Studio), and discoverability measurement.

Install
npx -y skills add OpenSealAI/agent-skills --skill socialseal-content-adjustment-recommendations

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What its author says it does

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Use this skill when converting a SocialSeal readout into concrete content changes: what to continue, adjust, add, pause, retest, or track differently based on discoverability evidence.

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SKILL.md

2.6 KB, 477 tokens by cl100k_base, as published. Nobody here has run it

SocialSeal Content Adjustment Recommendations

Overview

This skill turns measurement into decisions. It should not summarize the readout again. It should say exactly what content, tracking, or briefing changes should happen next and why.

Inputs

  • performance readout
  • opportunity matrix
  • content plan or posted content list
  • production capacity
  • next-period goals

Workflow

  1. Group findings by action type. Continue, adjust, add, pause, investigate, or change tracking.
  2. Diagnose likely issue. Topic, format, hook, first frame, useful detail, market/language, cadence, asset quality, or measurement gap.
  3. Write concrete changes. Use owner-ready language: “Add two utility walkthroughs for X,” not “do more useful content.”
  4. Prioritize. Impact, confidence, effort, and speed to learn.
  5. Define measurement. State the tracking group, keyword/topic, and signal for each change.
  6. Create brief queue. Convert high-priority changes into creator briefs or video concepts.

Output

A recommendation table:

  • action type
  • finding/evidence (cite by video title/URL, @handle, "keyword" [market, platform])
  • recommended change
  • owner/workstream
  • expected signal
  • tracking group/keyword
  • priority
  • caveat / confidence basis (measured / scoped statistic / indicative pattern; see references/evidence-and-confidence.md)

Do / Don't

Do:

  • tie every recommendation to evidence
  • separate content changes from tracking fixes
  • keep changes small enough to measure
  • define review timing

Don't:

  • recommend vague quality improvements
  • change too many variables at once
  • ignore production constraints
  • invent certainty where evidence is partial

Troubleshooting

  • If every recommendation is “create more content,” return to content jobs and specify format/detail.
  • If evidence conflicts, propose a small test rather than a major pivot.
  • If tracking is incomplete, make tracking repair the first recommendation.

Verification Checklist

  • Each recommendation has evidence.
  • Each recommendation is actionable.
  • Measurement signal is defined.
  • Priorities reflect impact and feasibility.
  • Tracking fixes are not disguised as content strategy.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.