701 tagged machine-learning, measured the same way as everything else here.
Browse within: data-science 231bioinformatics 161deep-learning 98MLOps 73streamlit 53claude-code-plugin 50auto-sci-research 45bibliometrics 45codex-skills 45geospatial 45literature-review 45forecasting 43mlforecast 41neuralforecast 41
Skill Claude CodeCodex
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
Skill Claude CodeCodex
Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.
retentioneering/retentioneering-tools
Skill Claude CodeCodex
Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's…
retentioneering/retentioneering-tools
Skill Claude CodeCodex
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks…
Skill Claude CodeCodex
Benchmarking and performance auditing for the Wax repo. Use when running or interpreting Wax benchmarks, diagnosing CPU, memory, or I/O bottlenecks, or investigating Swift 6.2 concurrency issues such as Sendable, actor isolation, @unchecked Sendable, task-group fan-out, and data races.
Skill Claude CodeCodex
Swift framework guidance for Wax on-device memory/RAG. Use when writing Swift code with the public Memory facade, experimental PhotoMemory / VideoMemory, BuiltInMultimodalEmbeddings, embedding providers, retrieval modes, or hybrid search. For agent operators using the Wax MCP server tools, use the separate wax-mcp…
Skill Claude CodeCodex
../../../.opencode/skills/condition-setup/SKILL.md.
Skill Claude CodeCodex
../../../.opencode/skills/define-equations/SKILL.md.
Skill Claude CodeCodex
Audits SKILL.md files in this repo's skills directory for references to functions, classes, or modules (mentioned by name in prose, e.g. parseinvoice) that no longer exist in the codebase because they were renamed, moved, or deleted. Proposes a targeted fix to the one skill being checked, never a blanket rewrite. ONLY…
Skill Claude CodeCodex
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
Skill Claude CodeCodex
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
Skill Claude CodeCodex
Execute the implementation planning workflow using the plan template to generate design artifacts.
Skill Claude CodeCodex
GitHub issue triage workflow with contributor profile extraction. Analyze → clarify → file cells → tag → implement → credit. Captures Twitter handles for changeset acknowledgments.
Skill Claude CodeCodex
Implicit feedback scoring, confidence decay, and anti-pattern detection. Use when understanding how the swarm plugin learns from outcomes, implementing learning loops, or debugging why patterns are being promoted or deprecated. Unique to opencode-swarm-plugin.
Skill Claude CodeCodex
Guide for job queue patterns in multi-agent coordination. Use when deciding between background jobs vs inline execution, submitting long-running tasks, monitoring job progress, and handling failures. Covers when to queue, job priority, retry strategies, and monitoring patterns.
Skill Claude CodeCodex
Use for hipfire quant calibration, imatrix-driven experiments, KLD/PPL quality evaluation, k-map/format selection, MQ/HFQ/HFP/MFP tradeoff work, ParoQuant-style weight transform planning, and KV policy planning. Use when deciding whether a calibrated model candidate should be promoted, rejected, packaged, or sent…
Skill Claude CodeCodex
Use Kernel Atlas to collect phase-aware hipfire measurements and render ISA Fit View visualizations for AMD GPU kernels, quant formats, and architectures. Use when a user asks how MQ/HFQ/HFP/Q8 quants occupy hardware, asks for an ASCII ISA visualization, wants to compare gfx1010/gfx1030/gfx11/gfx12 kernel fit, or…
Skill Claude CodeCodex
Use when porting a hipfire feature/fix branch authored against pre-0.1.20 master onto post-modular master. Walks through the engine→hipfire-runtime + per-arch-crate split mechanically, then surfaces semantic conflicts that need human judgment.
Skill Claude CodeCodex
Use liter-llm api reference documentation.
Skill Claude CodeCodex
Use liter-llm cli reference documentation.
Skill Claude CodeCodex
Use liter-llm mcp reference documentation.
graphsignal/graphsignal-profiler
Skill Claude CodeCodex
Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about graphsignal-run or graphsignal.watch(), or asks about CUPTI / Prometheus / OTLP setup.
Skill Claude CodeCodex
Analyze PerforatedAI training results and provide optimization recommendations. Trigger: 'Analyze my perforated results' (after training completes). Reviews CSV outputs, identifies performance patterns, recommends configuration improvements. For initial setup or debugging, use the perforatedai skill instead.
Skill Claude CodeCodex
Multi-GPU setup for PerforatedAI with DataParallel or DistributedDataParallel (DDP). Invoked automatically by the perforatedai skill when multi-GPU training is detected. Handles initialization workflow, checkpoint loading, rank 0 handling, and shell script generation for DDP.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: