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.
Expert in PerforatedAI library for adding artificial dendrites to PyTorch neural networks. Triggers: 'Perforate my model' (start interactive setup), 'debug my perforated model' (debug/optimize existing integration), 'load my perforated model for inference' (deploy trained models). Also use when: debugging dendrite…
Scaffold a brand-new Comet example in this repo from the canonical template under templates/integration-example/. Use whenever the user wants to create, add, or start a new example, demo, or integration on Comet — phrasings like "add a Comet example for X", "scaffold an integration example", "create a new example for…
Fix CI failures and performance regressions for a Julia package PR by iterating - triage the latest CI results, fix one root cause, verify locally, push. Use when a PR's GitHub Actions or Buildkite CI is failing, or when CI jobs run slower than they do on the main branch. Verifies CPU and GPU compilation locally…
Fixes tool environment setup failures in proto-tools, either just for the current machine (eject the tool's standalone dir, patch it, and point PROTO STANDALONEDIR at it; works for any install, including a non-editable pip install) or as a cross-platform fix contributed back to the repo. Same diagnosis for both…
Implements a new bioinformatics tool wrapper in proto-tools using a parallelized agent pipeline. Orchestrates phases: Research, Contract (core tool file), Fan-out (5 parallel subagents), Verify, then the decisive gates — Config Field Audit, Temp Integration & Stress, Docs Verification, Self-Audit & Full PR Review …
Use for LeWorldModel, GR00T, LeRobot, PushT robotics showcase, real-checkpoint smoke scripts, checkpoint building, and host-owned optional runtime dependencies. Keeps real-runtime validation explicit without adding heavy ML/robotics packages or artifacts to the base project.
Use for WorldForge README, docs, changelog, generated provider docs, MkDocs navigation, version/release metadata, public positioning, and release or publish readiness checks. Keeps public surfaces synchronized without hype or generated-doc drift.
Use when selecting, running, or fixing WorldForge validation: pytest, coverage, ruff, generated provider docs, MkDocs strict build, package contract, CI failures, and release gates. Produces the smallest credible command set first, then escalates to full validation when public behavior changes.
Use durable, explainable, self-improving memory across a coding session via the velesdb-memory MCP server. Trigger whenever the velesdb-memory MCP tools (remember/recall/recallfused/relate/why/feedback/forget/rememberextracted/extractionstatus/entity/memorystatus) are available and the work would benefit from…
Compress an agent's working context deterministically with VelesDB's context compiler before sending it to a model — fewer tokens, same facts, every decision auditable and reversible. Use when a prompt is bloated with repeated context, long logs, or accumulated conversation turns; when token costs need to drop…
Turn every velesdb design / implement / verify cycle into durable, connected memory so the SAME mistake is never repeated and later decisions build on what was already learned, instead of re-deriving it or silently colliding with it. Trigger BEFORE starting a design or implementation on velesdb / velesdb-core /…
Autonomously reproduces quantum computing arXiv papers using TensorCircuit-NG. It creates standardized repository structures, generates meta.yaml, writes and runs JAX-accelerated code, and strictly enforces code quality (black/pylint) before saving final figures.
Tune tensor-network contraction path search and slicing for TensorCircuit-NG workloads, especially OMECO and cotengra hyperparameters, memory targets, total FLOPs/write, slice counts, and large-circuit amplitude or expectation contractions.
Analyzes and refactors TensorCircuit-NG code to achieve peak time and memory performance. It enforces advanced JAX vectorization, intelligent JIT staging, optimal tensor network contraction, and memory-efficient autodiff strategies.
🤖 Data Science & AI/ML skill suite derived from VoltAgent/awesome-agent-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
🤖 Data Science & AI/ML skill suite derived from BehiSecc/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
Generate a comprehensive retrospective for a (model, lane) pair that maps the explored search space, identifies coverage gaps from the topic taxonomy (model-optimization-index.md) and Pallas kernel catalog, surfaces premature-exhaustion signals (single-axis-probe vs kernel-port imbalance, frontier-stuck duration), and…
Generate a structured optimization hypothesis for the autoresearch loop. Walks three layers — (1) the generic wiki/model-optimization-index.md for TPU envelope data + topic pointers + cross-model refuted-pattern principles, (2) the generic wiki/model-optimization-blueprint.md for ladder-keyed phase ordering +…
Start the autoresearch optimization loop for a specific model + lane. Resolves the hierarchical program.md (root → model → lane), asks the user for hardware (local TPU VM or GKE cluster of a specified TPU type + topology), discovers available clusters from .env/, checks occupancy with USERPREFIX-aware attribution…
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originalMIT
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: