Results for “tprm”

Generates synthetic body MRI volumes using NVIDIA's NV-Generate-CTMR rflow-mr model. Wraps the upstream diffusion inference pipeline with config staging, output validation, and NIfTI volume summarization.

Verify TPM measured boot integrity and remote attestation using tpm2-tools, including PCR reading, event log replay, quote generation and verification, and golden baseline comparison.

pnpm package manager. Fast, disk-efficient with excellent monorepo support. Use when managing dependencies or setting up monorepos. USE WHEN: user mentions "pnpm", "pnpm workspace", "pnpm-workspace.yaml", asks about "pnpm commands", "pnpm install", "workspace protocol" DO NOT USE FOR: npm (use standard npm commands), yarn (use yarn commands), bun package manager

Automate multi-turn adversarial conversations against LLM agents using Microsoft PyRIT, including Crescendo and Tree-of-Attacks-with-Pruning (TAP) attack chains with scorer feedback loops.

Evaluates speaker verification models by computing true positive rate at fixed false positive rate thresholds, probing embedding space separation of same-speaker versus different-speaker pairs.

Total productive maintenance tracker

Generates synthetic brain MRI volumes using NVIDIA's NV-Generate-CTMR workflow, with configurable modality and random seed.

Submit and manage TAO training, evaluation, and inference jobs on SLURM GPU clusters over SSH with sbatch/srun, Pyxis/Enroot containers, and Lustre-backed storage.

Choose pricing models, set freemium thresholds, and structure enterprise pricing conversations for technical products.

Routes project management requests to one of eight specialized skills: senior PM, scrum master, Jira, Confluence, Atlassian admin, templates, meeting analyzer, or team communications.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

TypeORM for TypeScript/JavaScript. Covers entities, repositories, and relations. Use with SQL databases. USE WHEN: user mentions "typeorm", "@Entity", "Repository", "DataSource", "QueryBuilder", "typeorm migration", asks about "decorators for database", "active record pattern", "entity relationships", "typeorm relations" DO NOT USE FOR: Prisma projects - use `prisma` skill; Drizzle - use `drizzle` skill; SQLAlchemy (Python) - use `sqlalchemy` skill; raw SQL - use `database-query` MCP; NoSQL - use `mongodb` skill; Sequelize - not supported

Run a pre-mortem risk analysis on a PRD or launch plan, categorizing risks as Tigers (real problems), Paper Tigers (overblown concerns), and Elephants (unspoken worries), then classifying by urgency and creating action plans.

Reads BA and TechLead artifacts to produce a sprint plan, task breakdown, and story point estimates, creating one tracked todo per sprint task for live pipeline tracking.

Plans sprints, breaks user stories into tasks, and estimates story points from BA and TechLead artifacts, producing sprint plan, task breakdown, and story point files.

Trigger machine account authentication with PetitPotam (MS-EFSR) and Coercer across MS-RPRN, MS-DFSNM, and MS-FSRVP to feed NTLM relay into AD CS Web Enrollment (ESC8) and other relay targets.

Configure privileged session monitoring and recording using CyberArk PSM or open-source alternatives like Teleport, with keystroke logging, real-time alerts, and compliance audit trails.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

Build type-safe APIs with tRPC, covering procedures, routers, middleware, React integration, subscriptions, and error handling.

Comprehensive PM resume review and tailoring against 10 best practices including XYZ+S formula, keyword optimization, job-specific tailoring, and structure. Use when reviewing a PM resume, preparing for job applications, or improving resume impact.

Map adversary TTPs against organizational assets using the MITRE ATT&CK framework, assess detection coverage gaps, and prioritize defensive investments.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

Agent skill for swarm-pr - invoke with $agent-swarm-pr

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency on NVIDIA GPUs (A100/H100).

Provides production patterns and non-obvious traps for Prisma ORM in TypeScript backends, covering schema design, query optimization, transactions, pagination, and common pitfalls.

Executes Atomic Red Team tests mapped to MITRE ATT&CK techniques, performs coverage gap analysis, and runs detection validation loops to measure blue team visibility.

Train reinforcement learning agents at millions of steps per second using optimized PPO, vectorized environments, and multi-agent support.

Worktree-first dev environment manager for issues, PRs, and features with optional tmux sessions

PR Review Expert

Expert guidance for implementing GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.

Review GitHub PRs and GitLab MRs with structured analysis including blast radius, security scanning, test coverage delta, breaking change detection, and performance impact.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.