ReddRadar
Topic: autonomous-agents
981 skills in this topic.
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triage-issue
aden-hive/hive 10,180
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browser-edge-cases
SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
aden-hive/hive 10,180
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hive.note-taking
Maintain structured working notes throughout execution to prevent information loss during context pruning.
aden-hive/hive 10,180
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test-reporting
aden-hive/hive 10,180
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hive.context-preservation
Proactively preserve critical information before automatic context pruning destroys it.
aden-hive/hive 10,180
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hive.task-decomposition
Decompose complex tasks into explicit subtasks before diving in.
aden-hive/hive 10,180
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hive.error-recovery
Follow a structured recovery protocol when tool calls fail instead of blindly retrying or giving up.
aden-hive/hive 10,180
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hive.quality-monitor
Periodically self-assess output quality to catch degradation before the judge does.
aden-hive/hive 10,180
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hive.batch-ledger
Track per-item status when processing collections to prevent skipped or duplicated items.
aden-hive/hive 10,180
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autogrind
Engage in 24x7 auto-work mode. Continuously grind through improvements, fixes, tests, and polish without stopping until you say so. For long-running sessions across code, ML/data, research, design, or writing. Not for single bounded tasks. Trigger phrases: /autogrind, /自己动, 'autogrind this', 'grind on this', 'keep working don't stop', 'work until I say stop', 'keep improving', 'keep going'.
ttttonyhe/autogrind 6
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autogrind
Engage in 24x7 auto-work mode. Continuously grind through improvements, fixes, tests, and polish without stopping until you say so. For long-running sessions across code, ML/data, research, design, or writing. Not for single bounded tasks. Trigger phrases: /autogrind, /自己动, 'autogrind this', 'grind on this', 'keep working don't stop', 'work until I say stop', 'keep improving', 'keep going'.
ttttonyhe/autogrind 6
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swe-af
Autonomous engineering team runtime — one API call spins up coordinated AI agents to scope, build, and ship software.
Agent-Field/SWE-AF 697
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ml-paper-writing
Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
OpenRaiser/NanoResearch 689
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ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
OpenRaiser/NanoResearch 689
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nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs
OpenRaiser/NanoResearch 689
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academic-plotting
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
OpenRaiser/NanoResearch 689
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nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint
OpenRaiser/NanoResearch 689
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brainstorming-research-ideas
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
OpenRaiser/NanoResearch 689
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nanoresearch-planning
Produce an experiment blueprint from a research hypothesis
OpenRaiser/NanoResearch 689
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autoresearch
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.
OpenRaiser/NanoResearch 689
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evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
OpenRaiser/NanoResearch 689
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unsloth
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
OpenRaiser/NanoResearch 689
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nanoresearch-ideation
Search academic literature and generate research hypotheses
OpenRaiser/NanoResearch 689
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peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
OpenRaiser/NanoResearch 689