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Use when the user wants Karpathy-style autonomous experiment loops, overnight research runs, or iterative keep-or-discard testing.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/Sheshiyer/skill-clusters --skill autoresearch명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Arcplume runs Grok through the Grok Build CLI's own OAuth-authenticated session (grok login) for image generation, with strict preflight validation, secret-safe handling, and headless CLI-driven execution -- no separate XAI_API_KEY billing. Video falls back to the billed xAI API. USE WHEN a user wants to generate an image via a locally logged-in Grok Build CLI session, e.g. 'generate an image with grok', 'use grok build', or 'use my logged-in grok session'.
Shared reference for the Selemene cluster: the two report surfaces (deterministic Rust reports vs. narrative witness-pipeline readings), the @selemene/bridge CLI contract, the output manifest format, and non-prescriptive witnessing tone. USE WHEN deciding which Selemene surface to invoke or when routing between birth/compatibility/transit reports and solo/dyadic readings.
Route Selemene Engine tasks to the right surface: deterministic reports (birth/compatibility/transit) via selemene-core and the @selemene/bridge CLI, or narrative witness readings via selemene-report. USE WHEN the user wants anything under the Selemene/Noesis umbrella but has not named the exact surface.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | autoresearch |
| description | Use when the user wants Karpathy-style autonomous experiment loops, overnight research runs, or iterative keep-or-discard testing. |
| cluster | quality-eval |
| version | 1.0.0 |
| origin | folded from agent skills library (quality-eval overlap) |
Use this skill when the user wants an agent to run repeated experiments with a strict keep-or-discard loop instead of a one-shot answer.
Use this when the user wants the original Karpathy workflow.
autoresearch repo (set AUTORESEARCH_DIR or ask the user for the path).<autoresearch-repo>/README.md<autoresearch-repo>/program.mdtrain.pyprepare.pyresults.tsv untrackedautoresearch/<tag>Use this mode only if the machine and repo are actually ready for it. The upstream repo assumes a real training environment and is not a generic business-research framework by default.
Use this when the user wants the same experiment pattern applied to:
Read references/thoughtseed-business-loop.md before running this mode.
Always leave behind:
Resolve these per environment rather than hardcoding:
autoresearch repo (e.g. $AUTORESEARCH_DIR).