'autonomous research' 검색 결과

Run repeated reviews of research work, implement fixes, and request another assessment for up to four rounds by default. The workflow records progress and stops when both the review score and verdict meet its acceptance rules.

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• Revising research work through repeated reviews
• Keeping a record of review findings and fixes
• Resuming an interrupted review process

Build AI agents by connecting workflow blocks in AutoGPT's visual editor. This skill covers multi-step automation, scheduled runs, webhooks that start work after external events, execution monitoring, and custom agent development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI workflow in a visual editor
• Start an agent when an external event occurs
• Run a multi-step workflow on a schedule

Measure code patterns such as casts to any or disabled lint rules, then run repeated, targeted edits. Keep changes that improve the chosen metric, revert unsuccessful changes, and track results for each improvement loop.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding code patterns to prioritize for cleanup
• Preparing measurement files for an improvement loop
• Repeating targeted edits to reduce casts to any

Run a research task through five ordered stages: brainstorming, planning, implementation, review, and documentation. The workflow checks that brainstorming and planning files exist before proceeding, then reviews and documents what the implementation stage produced.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Taking a research task from initial ideas to documentation
• Planning and implementing an estimation problem
• Working through a methodological improvement

Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

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Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.

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Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

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Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

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Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

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Reference when designing autonomous agent systems or deciding agent freedom/safety/context management/collaboration infrastructure. 10 design principles distilled from Karpathy autoresearch (autonomous ML experiment loop) + AgentHub (agent-native Git DAG). Triggered by "autoresearch", "agenthub", "ratchet" ("래칫"), "context budget", "dumb platform", "program.md", "overnight", "autonomous experiment" ("자율 실험"), "branchless", "single-file constraint" ("단일 파일 제약"), "fixed time budget" ("고정 시간 예산") keywords.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Claude Code의 기억, 정기 실행, 작업 대기열을 결합해 지속적인 작업을 설계합니다. 외부 실행 환경과 화면 조작 연동 설정도 안내합니다.

• 풀 리퀘스트를 정기적으로 확인할 때
• 매일 조사 결과를 모아 요약할 때
• 기억한 맥락으로 회의를 준비할 때

Find autonomous AI agents, frameworks, and tools through AgentFolio. Compare their capabilities, intended users, integrations, and deployment options to shortlist existing products or gather examples and ideas before designing an agent of your own.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding existing agents before building your own
• Shortlisting agent vendors for evaluation
• Researching competitors and market gaps

Guide an AI agent through research, implementation, and verification with fewer interruptions. The skill emphasizes evidence, concise reporting, and saved progress notes, with three working modes that adjust how closely the user follows the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegating engineering tasks through verification
• Reducing interruptions during focused work
• Resuming long tasks from saved progress notes

Improve code through repeated changes, checks, and decisions to keep or discard each result. Start with a goal or measurable target, limit iteration counts, and review logs for progress, stalled improvements, or regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve code against a measurable target
• Investigate and fix recurring errors
• Check whether changes introduce regressions

Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run repeated, measured Git experiments toward a numeric target; keep improvements and revert failures. Use for autonomous optimization or managing an autoresearch run, not one-shot edits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Research-brief + claim-record discipline for delegated or AI-assisted research work. Use when starting any substantive research task or long autonomous run (write the brief first), and when reporting results that will support a claim in a paper or decision (produce the claim record). Keeps faster execution from being confused with credible evidence.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Start a safe, governed autonomous loop from a plain-language goal. Use when the user says /coco-loop, "run an autonomous loop", "keep my build green for N hours", "work on X autonomously for a while", or wants a bounded self-improving loop that proposes fixes and never commits on its own. Turns a vague goal into a readable charter the user confirms, then arms and runs the loop propose-only under the coco-loops governance framework.

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Autonomous cold email campaign launcher. Takes one target company domain, scrapes their website, generates an ICP with Claude, pulls matching leads via Prospeo, enriches emails + company descriptions, personalizes each lead with parallel Claude Code Task sub-agents (A/B/C variants), and uploads as a live Smartlead campaign. Uses local JSON files for state (no database required). Use for automated daily campaign launches after you have an initial `client-profile.yaml` from /icp-onboarding. Triggers on "auto-research", "launch an automated campaign", "daily campaign", "run the research loop".

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Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.

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Operate the Deep Researcher Agent repository for autonomous deep-learning experiments, execution backends, provider/tool dispatch, GPU resources, durable progress, and research-support integrations.

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Build autonomous financial research agents that analyze stocks, SEC filings, earnings calls, and market data to produce investment reports. Use when: automating investment research, building AI-powered stock analysis, creating financial due diligence agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AgentFolio workflow skill. Use this skill when the user needs Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.