928 skills found Ā· Page 5 of 31
wanshuiyin / research-wikiPersistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle. Inspired by Karpathy's LLM Wiki pattern
wanshuiyin / experiment-planTurn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problā¦
wanshuiyin / integrity-forensicsRun the Anti-Autoresearch integrity-forensics sweep (span-anchored evidence ledger ā GPT auditors propose findings ā a rules-only reporter that lists every proposal with what the auditor said about it) against a paper via a SHA-pinned thin launcher ā then convert the verdict into a typed policy gateā¦
wanshuiyin / proof-orchestratorManage a stateful, run-directory-based proof project: continuation across runs, run-local source bookkeeping, manual GPT Pro handoff packages when a local attempt stalls, and an optional DeepSeek second opinion as additional evidence only
wanshuiyin / alphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback
wanshuiyin / auto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review ā implement fixes ā recompile, for 2 rounds
wanshuiyin / system-profileProfile a target (script, process, GPU, memory, interconnect) for performance analysis
larksuite / lark-openapi-exploreré£ä¹¦/Lark åē OpenAPI ę¢ē“¢ļ¼ä»å®ę¹ęę”£åŗäøęęęŖē» CLI å°č£ ēåē OpenAPI ę„å£ćå½ēØę·ēéę±ę ę³č¢«ē°ę lark-* skill ę lark-cli å·²ę³Øåå½ä»¤ę»”č¶³ļ¼éč¦ę„ę¾å¹¶č°ēØåēé£ä¹¦ OpenAPI ę¶ä½æēØć
MemoriLabs / MemoriMemori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems.
xbtlin / ai-berkshireAI ę¶ä»£ēä¼Æå åøå°ļ¼åŗäŗ Claude Code / Codex ēä»·å¼ęčµē ē©¶ę”ę¶ćå·“č²ē¹Ā·čę ¼Ā·ę®µę°øå¹³Ā·ęå½å大åøę¹ę³č®ŗ + å¤Agent并č”ē ē©¶ć| AI-era Berkshire: a value investing research framework built for Claude Code / Codex. 4 masters' methodologies + multi-agent adversarial analysis.
googleapis / mcp-toolboxMCP Toolbox for Databases is an open source MCP server for databases.
budtmo / docker-androidAndroid in docker solution with noVNC supported, video recording, mcp server and AI-agent
AgriciDaniel / claude-obsidianSelf-organizing AI second brain for Obsidian + Claude Code. Drop any source and Claude reads, links, and files it into one connected knowledge graph of plain Markdown you own. AI note-taking, personal knowledge management (PKM), and an open-source Notion alternative.
corsairdev / corsairConnect your users to their apps
NanmiCoder / cc-hahaę¬å°ä¼å ēč·Øå¹³å° Claude Code / AI Agent ę”é¢å·„ä½å°ļ¼å¤ Agent åä½ćGit Worktreeć代ē Diffćęč½åøåŗćå¤ęØ”åćComputer Usećę”é¢å® ē©ļ¼ęÆęå¾®äæ”ćé£ä¹¦ćééå H5 ēčæēØč®æé®ć | Local-first Claude Code / AI Agent desktop workspace: multi-agent teams, Git worktrees, diffs, skills marketplace, multi-model, Computer Use, task-aware desktop pets; remote via Wā¦
miuuyy / codex-chatgpt-webUse ChatGPT Web (including Pro) as a native model in Codex ā with context, tools, streaming and images, without using Codex quota.
kyegomez / OpenMythosA theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.
casdoor / casdoorAn open-source Agent-first Identity and Access Management (IAM) /LLM MCP & agent gateway and auth server with web UI supporting OpenClaw, MCP, OAuth, OIDC, SAML, CAS, LDAP, SCIM, WebAuthn, TOTP, MFA, Face ID, Google Workspace, Azure AD
Orchestra-Research / AI-Research-SKILLsComprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. Maintained by Orchestra Research.
Orchestra-Research / huggingface-tokenizersFast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers
Orchestra-Research / nnsight-remote-interpretabilityProvides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution
Orchestra-Research / pyvene-interventionsProvides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework
Orchestra-Research / sparse-autoencoder-trainingProvides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features
Orchestra-Research / transformer-lens-interpretabilityProvides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching
Orchestra-Research / langchainFramework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval.
Orchestra-Research / outlinesGuarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
Orchestra-Research / implementing-llms-litgptImplements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral)
Orchestra-Research / mamba-architectureState-space model with O(n) complexity vs Transformers' O(nĀ²). 5Ć faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
Orchestra-Research / nanogptEducational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
Orchestra-Research / rwkv-architectureRNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
