Use when reinforcement-learning environment, reward, data-regime, algorithm or evaluation decisions need implementation checks.
Plugin yzmir-deep-rl from marketplace foundryside-marketplace in tachyon-beep/skillpacks. Commands read from the repo's manifest — 1 month ago.
using-deep-rlUse when reinforcement-learning environment, reward, data-regime, algorithm or evaluation decisions need implementation checks.
/yzmir-deep-rl:diagnoseDiagnose a stalled or unstable RL run with task-specific source and runtime evidence.
/yzmir-deep-rl:scaffold-experimentScaffold a reproducible RL experiment with task-specific source and runtime evidence.
/yzmir-deep-rl:select-algorithmCompare feasible RL algorithms with task-specific source and runtime evidence.
reward-function-reviewerReview reward incentives with task-specific source and runtime evidence.
rl-training-diagnosticianDiagnose RL training with task-specific source and runtime evidence.
Use when drafting, revising, critiquing, or planning prose fiction or creative nonfiction, especially when author voice, continuity, genre expectations, or truth claims need deliberate treatment.
1 skill · 3 commands · 11 agents
Use when documentation accuracy, executable procedures, institutional register, sensitive information, or source-to-document traceability needs deliberate review.
2 skills · 4 commands · 5 agents
Use when reconstructing an existing codebase: source-linked architecture, dependency and risk findings, coverage limits and incremental refresh.
1 skill · 5 commands · 4 agents
Use for Rust-specific ownership/API, async cancellation, unsafe/FFI soundness, feature/toolchain compatibility, diagnostics, packaging or profiling problems in a real crate.
1 skill · 5 commands · 3 agents
Use when an organization explicitly selects a software governance profile requiring lifecycle evidence, traceability, risk decisions or measurement.
5 skills · 4 agents
Use when test reliability, isolation, coverage gaps, performance experiments, resilience, or release evidence needs investigation or a concrete verification strategy.
1 skill · 5 commands · 3 agents
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
Knowledge base compiled from OpenAI's Spinning Up in Deep RL (MIT, Joshua Achiam) by engineering/book-to-skill. A resident core carries the RL optimization problem, the model-free taxonomy, the policy-gradient template with its five valid weights, the safe-step family (VPG to TRPO to PPO) and the overestimation family (DDPG to TD3 and SAC); 20 on-demand chapters cover key concepts and MDPs, the algorithm taxonomy and model bias, the policy gradient derivation with the log-derivative trick and EGLP lemma, Achiam's researcher essay, the key-papers topic map, the exercises including the silent DDPG broadcasting bug, the benchmark parity disclosure, one chapter per algorithm, and the logger/MPI/ExperimentGrid utilities. Ships a glossary, a patterns file with 16 techniques, a decision cheatsheet with thresholds, a cs-spinning-up-deep-rl agent and /cs-spinning-up-deep-rl. Structured study notes, not a reproduction of the source.
1 skill · 1 command · 1 agent
Multi-agent orchestration with AI SDK v5 - handoffs, routing, and coordination for any AI provider (OpenAI, Anthropic, Google)
1 skill · 3 commands · 1 agent
Troubleshooter + machine-readable corpus for reinforcement-learning training, evaluation, and experiment design on LLM agents (single/multi-agent, multi-turn, tool-augmented). Distilled from the AgentsMeetRL awesome list (378 projects).
1 skill
Generates an executable, end-to-end VERL reinforcement learning quickstart runbook for Ascend/NPU (docker image, dataset preprocessing, model setup, main_ppo training, and examples/run_*.sh flow). Use when users mention verl, quickstart, PPO/GRPO, gsm8k, run_*.sh, Ascend, or NPU training.
1 skill
LLM application development, prompt engineering, and AI assistant optimization
5 skills · 3 commands · 2 agents
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