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content-ops

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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LazyIsEfficient/agentic-os
See how to install

Files included (19)

  • SKILL.md4.3 KB
  • experts/humanizer.md7.3 KB
  • experts/instagram.md2.5 KB
  • experts/linkedin.md1.9 KB
  • experts/newsletter.md1.6 KB
  • experts/podcast-quotes.md2.9 KB
  • experts/recruiting.md1.5 KB
  • experts/seo-strategy.md1.4 KB
  • experts/x-articles.md2.4 KB
  • experts/youtube-shorts.md1.6 KB
  • README.md2.4 KB
  • references/expert-assembly.md3.2 KB
  • references/patterns.md503 B
  • references/procedure-steps.md6.0 KB
  • scoring-rubrics/content-quality.md823 B
  • scoring-rubrics/conversion-quality.md1.1 KB
  • scoring-rubrics/evaluation-quality.md1.0 KB
  • scoring-rubrics/strategic-quality.md452 B
  • scoring-rubrics/visual-quality.md980 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Expert Panel

General-purpose scoring and iterative improvement engine. Auto-assembles the right experts for whatever is being evaluated, scores it, and loops until 90+.

Core rules

  1. Intake: collect content, content type, offer context, variants, and source skill — full procedure in references/procedure-steps.md Step 1.
  2. Auto-assemble 7–10 experts: start from experts/ pre-built panels, add 1–3 domain experts, always include AI Writing Detector (1.5x weight) and Brand Voice Match.
  3. Select scoring rubric from scoring-rubrics/ by content type; read the file for criteria.
  4. Score recursively until 90+ aggregate (max 3 rounds). Humanizer weighted 1.5x. Show all rounds in output — the iteration trail is the value.
  5. Check references/patterns.md at every round start and dock points for known-bad patterns before expert scoring.
  6. When scoring another skill's output, generate a Source Improvement Brief (Step 6).
  7. On user rejection of 90+ content, capture the reason and append to references/patterns.md.

References

  • references/procedure-steps.md — full 7-step procedure: intake, panel assembly, rubric selection, scoring loop, output format, feedback-to-source, pattern learning
  • references/expert-assembly.md — domain-expert examples for auto-assembly of unfamiliar panels
  • references/patterns.md — learned rejection patterns; read every run
  • experts/humanizer.md — AI writing detection rubric (24 patterns); always run
  • experts/ — pre-built panels: humanizer, instagram, linkedin, newsletter, podcast-quotes, recruiting, seo-strategy, x-articles, youtube-shorts
  • scoring-rubrics/ — content-quality, conversion-quality, evaluation-quality, strategic-quality, visual-quality

Related skills

  • autoresearch — pre-launch variant generation + multi-round optimization of conversion copy; run before content-ops's final gate
  • conversion-ops — post-publish conversion layer; run after content-ops quality gate
  • adversarial-claims-reviewer — judges whether formal/technical claims are true; content-ops judges whether the prose is good
  • content-pipeline — script-driven content production (RSS quote mining, video-clip discovery, repurposing, batch draft gating); reuses this skill's experts/ panels in its transform stage

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