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cccskills
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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.

LazyIsEfficient/agentic-os
インストール方法を見る

含まれるファイル（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（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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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概要と使いどころ

adversarial-claims-reviewer

無料

Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

autoresearch

無料

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

browser-testing-with-devtools

無料

Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

code-review-and-quality

無料

Conducts multi-axis code review across correctness, readability, architecture, security, and performance. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Triggers on "review my PR", "review this diff", "code review", "review this changeset", "is this ready to merge", "pre-merge review".

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

codebase-cost-estimator

無料

Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

content-pipeline

無料

Non-interactive content-production toolkit: mine quotable moments from podcast RSS feeds and meeting notes, discover clip-worthy moments in video transcripts, repurpose long-form source into platform-native drafts (X, LinkedIn, YouTube Shorts, newsletter), and batch-score/gate those drafts before publish. Use when asked to "mine quotes from this podcast", "find clips in this video", "repurpose this into a thread / LinkedIn post / Short", "turn this transcript into posts", "extract viral moments", or "gate this batch of drafts". Runs Python scripts end to end. For interactive expert-panel scoring of a single artifact see content-ops.

日本語の概要は準備中です。原文の説明を表示しています。

LazyIsEfficient/agentic-os172026年7月17日 更新

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