القائمة
Use when the user wants Karpathy-style autonomous experiment loops, overnight research runs, or iterative keep-or-discard testing.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Sheshiyer/skill-clusters --skill autoresearchيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| 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).