'autonomous research' 검색 결과

Coordinate AI research from literature review and hypothesis testing to findings and paper drafts. The skill alternates focused experiments with broader reflection, maintains project records, and directs specialized skills to carry out research tasks.

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• Turning a research question into testable hypotheses
• Managing repeated experiments across multiple hypotheses
• Resuming research from recorded findings and project state

Coordinate AI research from literature review to paper drafting. The skill alternates focused experiments with broader reviews of findings, tracks hypotheses and decisions, and uses specialist skills to carry out research tasks and prepare progress reports.

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• Starting a research project from a question
• Testing and comparing multiple hypotheses
• Reassessing direction when experiments stall

Research a defined topic within agreed limits and produce a cited report that distinguishes evidence, inference, contradictions, and gaps. Public web access requires consent, and saving findings or merging them into existing Obsidian notes receives separate review.

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• Investigate a topic within agreed limits
• Compare conflicting claims and their sources
• Create a cited research report

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

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Coordinate a research project from a broad topic through literature review, idea selection, experiments, and iterative review. Produce a research summary for writing, with optional paper drafting and PDF compilation, while recording stage progress for later resumption.

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• Taking a broad research topic through experiments and review
• Coordinating experiment implementation and result collection
• Preparing an evidence-based handoff for paper writing

Automate setup, configuration, execution, monitoring, and troubleshooting of AutoResearchClaw — the 23-stage autonomous research pipeline that generates conference-grade papers. Use when the user mentions ResearchClaw, wants to write a research paper autonomously, needs to set up or debug the pipeline, or says research paper, autonomous research, or paper generation.

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Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.

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Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

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• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Run Gemini research tasks that plan searches, read sources, and combine findings into cited reports. Specify a report structure, monitor progress, and continue earlier research with follow-up questions for market, technical, historical, or comparative studies.

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• Research a market and its competitors
• Review technical literature
• Compare products or technologies

Improve research through repeated Gemini reviews, code changes, experiments, and analysis. The workflow records full feedback and actions, then stops when both the score and readiness criteria are met or the round limit is reached.

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• Improving research through repeated external reviews
• Implementing and checking fixes for research weaknesses
• Keeping a complete record of reviews and experiments

Improve research through up to four rounds of external language model review, fixes, and reassessment. Use an OpenAI-compatible API to identify weaknesses, prioritize changes, and keep a cumulative record of reviewer responses, actions, and experiment results.

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• Identify weaknesses before submitting research
• Revise research using an external model's feedback
• Track review scores, changes, and experiment results

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

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Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

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• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

Iteratively improve research by having Claude Code review code, results, and drafts while Codex implements fixes. Repeat until the assessment meets the stopping criteria or the round limit is reached, preserving feedback and unresolved issues.

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• Repeating research reviews and fixes before submission
• Checking whether claims match code and experiment results
• Tracking how reviewer concerns are addressed across rounds

Improve research code, results, and paper drafts through repeated review by a second Codex agent. The workflow implements fixes, checks them again, and records assessments and unresolved issues across up to four rounds.

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• Improve research before submitting a paper
• Repeat fixes and reviews of experimental results
• Track unresolved concerns across review rounds

Use MiniMax to review research, address weaknesses through code changes, experiments, or analysis, and request another assessment. The workflow runs up to four rounds and preserves reviewer responses, actions, results, and unresolved blockers.

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• Reviewing research before conference submission
• Fixing weaknesses identified in an external review
• Reassessing research after experiments or analysis

Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.

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• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results

Define a consistent process for unattended AI research using public sources. Limit clarification questions, show a search plan, distinguish facts from interpretations and assumptions, and keep reports structured so repeated investigations can be compared.

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• Defining a process for recurring competitive research
• Separating sourced facts from AI interpretations
• Comparing findings across quarterly investigations

Carry a research task from brainstorming and planning through code implementation, review, and documentation. Multiple agents handle independent implementation tasks in parallel, then report the resulting files, changes, and findings.

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• Take a research task from initial ideas through implementation
• Split independent implementation tasks among multiple agents
• Run review and knowledge documentation after implementation

Multi-step autonomous research methodology for deep investigation tasks with structured synthesis

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Scaffold and run Karpathy-style autoresearch loops in any git repo. This skill should be used when setting up autonomous code improvement, generating adversarial eval harnesses, running hypothesis-implement-eval-keep/discard loops, or checking autoresearch progress. Triggers on "autoresearch", "autonomous improvement", "eval loop", "hypothesis loop", "self-improvement loop".

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Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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Runs autonomous keep/discard experiments on a codebase to optimize a single metric for a fixed duration, in the style of karpathy/autoresearch. Use when the user says "autoresearch" (optionally with a focus, e.g. "autoresearch the optimizer"), asks to run experiments on a repo overnight, to hill-climb or optimize a metric autonomously, or points at a repo with a karpathy-style program.md.

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Design and build AI agents for business, research, operations, or creative work. Define essential actions and knowledge, start with a simple execution loop, and add progress tracking or separate child agents as needs emerge.

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• Designing an assistant for business tasks
• Building a research agent
• Choosing essential actions and knowledge