agentsclimarketplace

Content ops

Skill LazyIsEfficient/agentic-os/.claude/skills/content-ops

Agentic Framework for Modern Development

Install
npx -y skills add LazyIsEfficient/agentic-os --skill content-ops

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One thing to look at

  • 13 stars13 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

4.3 KB, as published. Nobody here has run it

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

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.