# 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.

- Skill: `lazyisefficient/content-ops` (Agent Skill, multi-file: 19 files)
- Install (CLI): `npx skillmds@latest add lazyisefficient/content-ops`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lazyisefficient/content-ops/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: LazyIsEfficient (https://skillmd.com/u/lazyisefficient)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lazyisefficient/content-ops

---


# 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](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](references/expert-assembly.md) — domain-expert examples for auto-assembly of unfamiliar panels
- [references/patterns.md](references/patterns.md) — learned rejection patterns; read every run
- [experts/humanizer.md](experts/humanizer.md) — AI writing detection rubric (24 patterns); always run
- [experts/](experts/) — pre-built panels: humanizer, instagram, linkedin, newsletter, podcast-quotes, recruiting, seo-strategy, x-articles, youtube-shorts
- [scoring-rubrics/](scoring-rubrics/) — content-quality, conversion-quality, evaluation-quality, strategic-quality, visual-quality

## Related skills

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