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autoresearch

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".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os

설치 방법 보기

포함된 파일(7)

SKILL.md4.5 KB
README.md3.7 KB
references/content-types.md1.6 KB
references/execution-protocol.md2.6 KB
references/round-structure.md1.9 KB
requirements.txt18 B
scripts/autoresearch.py17.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Autoresearch Skill

Karpathy-style optimization loops for any conversion-focused content. No traffic needed. Simulated expert panel. Minutes, not weeks.

The sequence: Run autoresearch FIRST to hit 85+ simulated score. Then deploy. Then validate with real traffic.

What You'll Produce

Every run outputs 3 files:

File	Purpose
`{name}-optimized.{ext}`	The winning optimized content
`data/{name}-experiments.json`	Full experiment log — all variants + all scores
`data/{name}-optimization-report.md`	Human-readable summary with winner rationale

Core rules

Intake: parse content type from file extension (HTML → landing/form page; markdown → email/ad copy). List all optimizable elements and confirm with user before starting.
Score every variant against the 5-expert panel in a single batched API call per round — never one call per variant.
Run up to 3 rounds per element; stop early when top variant hits the minimum score threshold (default: 80).
Cross-breed only after each element has its own winner — premature cross-breeding creates incoherent combinations.
Write 3 output files: optimized content, experiments JSON, optimization report.
Report back: winning score, biggest score jump, top 2 runner-ups, file paths, next step.

User Options

Option	Default	Description
`elements`	all	Which elements to optimize
`variants_per_round`	10	How many variants per round
`min_score`	80	Stop when this score is hit
`rounds`	3	Max rounds before stopping
`auto_apply`	false	Overwrite source file with winners
`content_type`	auto-detect	Force a content type if auto-detect is wrong

Quality Gates

< 70: Don't ship. Something fundamental is broken.
70-79: Marginal. One more round targeting the lowest-scoring dimension.
80-84: Good. Shippable. Validate with real traffic.
85-89: Strong. Ship with confidence.
90+: Rare. Ship immediately.

Anti-Patterns

Never call the API once per variant. Always batch. A 10-variant round = 1 call.
Don't over-optimize for one dimension — a 95 clarity + 45 trust is a misleading overall score.
Don't run more than 5 rounds. If not hitting 80 after 3, the problem is strategic, not tactical.

References

references/round-structure.md — round-by-round procedure and 5-expert panel definitions
references/content-types.md — elements and score dimensions per content type (landing pages, email, ads, forms)
references/execution-protocol.md — 6-step execution protocol with JSON schema

Related skills

content-ops — quality scoring gate; run after autoresearch optimization
conversion-ops — audits a LIVE landing-page URL across CRO dimensions; use it instead when scoring a live page rather than optimizing copy variants pre-launch

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

adversarial-claims-reviewer

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

browser-testing-with-devtools

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

code-review-and-quality

무료

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".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

codebase-cost-estimator

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

content-ops

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

content-pipeline

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LazyIsEfficient/agentic-os172026년 7월 17일 업데이트

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설치

명령으로 설치

npx skills add https://github.com/LazyIsEfficient/agentic-os --skill autoresearch

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/LazyIsEfficient/agentic-os/tree/main/.claude/skills/autoresearch

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/autoresearch/, Codex는 ~/.agents/skills/autoresearch/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: LazyIsEfficient/agentic-os
스타: 17
포크: 4
카테고리: 리서치·학습
라이선스: MIT
원문 언어: 영어
최종 업데이트: 2026년 7월 17일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.