{"skill":{"slug":"gaasher-karpathy","name":"karpathy","description":"Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps changes that lower a single scalar metric (e.g. val_bpb). One agent proposes one change at a time, runs training in the user's env, keeps it only if the metric improves (advancing a git branch) else reverts, and loops forever until the human interrupts. A faithful adaptation of Karpathy's autoresearch. Not for the analysis-first variant that profiles before editing (that is ml-autoresearch), and not for a budgeted, plateau-stopping refactor.","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/karpathy"},"recommended_command":"npx skills add gaasher/Agent-Loop-Skills --skill karpathy","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add gaasher-karpathy","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"karpathy\" agent skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/karpathy. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps changes that lower a single scalar metric (e.g. val_bpb). One agent proposes one change at a time, runs training in the user's env, keeps it only if the metric improves (advancing a git branch) else reverts, and loops forever until the human interrupts. A faithful adaptation of Karpathy's autoresearch. Not for the analysis-first variant that profiles before editing (that is ml-autoresearch), and not for a budgeted, plateau-stopping refactor. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"gaasher-karpathy\",\"task\":\"Install karpathy\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"karpathy\" as a Claude Code skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/karpathy. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps changes that lower a single scalar metric (e.g. val_bpb). One agent proposes one change at a time, runs training in the user's env, keeps it only if the metric improves (advancing a git branch) else reverts, and loops forever until the human interrupts. A faithful adaptation of Karpathy's autoresearch. Not for the analysis-first variant that profiles before editing (that is ml-autoresearch), and not for a budgeted, plateau-stopping refactor. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"gaasher-karpathy\",\"task\":\"Install karpathy\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"karpathy\" from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/karpathy into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps changes that lower a single scalar metric (e.g. val_bpb). One agent proposes one change at a time, runs training in the user's env, keeps it only if the metric improves (advancing a git branch) else reverts, and loops forever until the human interrupts. A faithful adaptation of Karpathy's autoresearch. Not for the analysis-first variant that profiles before editing (that is ml-autoresearch), and not for a budgeted, plateau-stopping refactor. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"gaasher-karpathy\",\"task\":\"Install karpathy\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"install_receipt":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","idempotency":"event_id is unique; retries update the same receipt","count_rule":"verified installs require install_used=true and outcome=success","example":{"event_id":"install_<unique-id>","skill_slug":"gaasher-karpathy","task":"Install karpathy","agent":"codex","outcome":"success","install_used":true}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"agent_prompt":"Install the \"karpathy\" agent skill only after reviewing the OpenAgentSkill profile and source repository. Safety gate: Blocked for auto-install (block). Start with https://www.openagentskill.com/skills/gaasher-karpathy, inspect the trust and audit notes, then use the recommended install handoff: npx skills add gaasher/Agent-Loop-Skills --skill karpathy. After installation, summarize changed files, required setup, and a minimal verification result before using the skill for real work. Report the verified result to https://www.openagentskill.com/api/agent/outcome using a unique event_id, skill_slug=gaasher-karpathy, install_used=true, and outcome=success or failed.","safety_checklist":["Safety gate: Blocked for auto-install. Policy: block.","Do not auto-install. Inspect the source, dependencies, and permission surface first.","Review the repository and license before running third-party code.","Prefer a sandbox or isolated project when testing a new skill.","Start with the recommended command, then inspect generated files before committing changes.","Do not execute external side effects, payments, account changes, or credentialed actions without explicit user approval."],"verification_steps":["Open the skill documentation or SKILL.md and identify required setup.","Run the smallest safe example for the target task.","Confirm outputs match the task before allowing broader agent use.","Record any missing credentials, policy risks, or manual approvals needed."],"do_not_auto_install_when":["The repository or license cannot be reviewed.","The skill requires broad credentials or production account access.","The task involves regulated, private, or high-impact data without user approval."],"urls":{"web":"https://www.openagentskill.com/skills/gaasher-karpathy","api":"https://www.openagentskill.com/api/agent/skills/gaasher-karpathy","install_api":"https://www.openagentskill.com/api/skills/gaasher-karpathy/install","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/karpathy"},"meta":{"agent_friendly":true,"api_version":"1.0","generated_at":"2026-09-08T13:09:25.484Z"}}