Results for “toml”

Initialize team config for a project. Creates .agenteam/config.yaml (or legacy agenteam.yaml) and generates .codex/agents/*.toml.

Records common pitfalls and assumptions for Python module execution and GitHub Actions sparse checkout setups.

Configure Python tooling, dependency surfaces, static analysis, and verification gates. Use when editing `pyproject.toml`, `requirements.txt`, `pytest.ini`, `ruff`, `pyright`, CI, or Python release checks.

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Create and install Codex custom agent roles in ~/.codex/config.toml, generate role config files, enforce supported keys, and guide users through required role inputs (model, reasoning effort, developer_instructions).

Trains, evaluates, and runs inference for Mask Auto-Label (MAL) weakly-supervised segmentation models using ViT-MAE backbones with minimal point or box annotations.

Run automated hyperparameter optimization for NVIDIA TAO models using AutoMLRunner, supporting multiple search algorithms and experiment tracking.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Use when formatting, linting, or type-checking standalone Python files that are not governed by a repository toolchain. Do not use for pyproject.toml or repository-level tooling work.

Executes Atomic Red Team tests for MITRE ATT&CK technique validation using the atomic-operator Python framework. Loads test definitions from YAML atomics, runs attack simulations, and validates detection coverage.

Trains, evaluates, exports, quantizes, and runs inference for a Mask Grounding DINO model for open-set instance segmentation guided by text prompts.

Train, evaluate, export, quantize, and run inference for a TAO OneFormer model that performs panoptic, instance, and semantic segmentation using task-conditioned queries.

Train, evaluate, export, quantize, and run inference on Mask2Former models for panoptic, instance, and semantic segmentation using NVIDIA TAO.

Master Test Architect and Quality Advisor. Use when the user asks to talk to Murat or requests the Test Architect.

Computes the InfoLM metric from torchmetrics for evaluating text generation against ground truth, with configurable information measures and sentence-level scoring.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

YAML configuration for CI/CD, Docker Compose, and Kubernetes.

Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

Volume-based PUMP token reward system with day-indexed epochs, pro-rata distribution, accumulator tracking, and cross-program claiming on Solana.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Author/validate/export Google's DESIGN.md token spec files.

Full token lifecycle from creation through bonding curve trading, graduation detection, AMM migration, fee collection, and volume tracking on Solana using PumpSdk and OnlinePumpSdk.

Builds and governs design systems with a three-layer token architecture, component specs, and design-to-code handoff, including slide generation.

Author/validate/export Google's DESIGN.md token spec files.

CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).