microduck_rl AGENTS.md

microduck_rl AGENTS.md is an instructions file for Codex, OpenCode from pollen-robotics/microduck_rl. It costs 4,000 tokens per session, scanned A, original, Apache-2.0.

Repository instructions for training movement software for Microduck, a small two-legged robot. The software trains control policies in a physics simulation, exports them to ONNX, and prepares them for the real robot.

In plain words
What is it for?
Use it when listing environments, running short training checks, launching training, testing policies, exporting ONNX files, or rehearsing deployment.
Why use it?
It records rules that keep simulated training consistent with the robot, reducing the risk that a policy works in simulation but fails on hardware.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/pollen-robotics/microduck_rl/agents-md
Clone the repo
git clone --depth 1 https://github.com/pollen-robotics/microduck_rl

Made for: Codex, OpenCode.

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README.md
[![agentmods](https://agentmods.dev/badge/instructions/pollen-robotics/microduck_rl/agents-md.svg)](https://agentmods.dev/instructions/pollen-robotics/microduck_rl/agents-md)
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<a href="https://agentmods.dev/instructions/pollen-robotics/microduck_rl/agents-md"><img src="https://agentmods.dev/badge/instructions/pollen-robotics/microduck_rl/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 4,000 This file is loaded in full into every session.
When invoked 4,000 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.04000 $0.04000
Opus 5 $0.02000 $0.02000
Sonnet 5 $0.00800 $0.00800
Haiku 4.5 $0.00400 $0.00400

Measured 4d ago against content hash aceb94476f03, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

microduck_rl AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 246 lines

How it starts

The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

RL training environments for Microduck — a ~800 g, ~25 cm tall bipedal robot with 14 Dynamixel XL330 servos — built on mjlab (MuJoCo Warp) with PPO (rsl_rl). Policies are trained here at 50 Hz, exported to ONNX, and deployed by the runtime in the pollen-robotics/microduck repo on the real robot. Sim2real transfer is the whole point: every convention below exists because breaking it produced a policy that worked in the viewer and failed on hardware.

Commands

uv run list-envs                                    # live task registry
uv run train <TASK_ID> --env.scene.num-envs 4096    # train (add --hf-jobs for Hugging Face Jobs)
uv run train <TASK_ID> --env.scene.num-envs 64 --agent.max_iterations 5   # SMOKE TEST — always run first
uv run play <TASK_ID> --wandb-run-path <entity/project/run_id>
uv run scripts/export.py <TASK_ID> --wandb-run-path <...>   # → ONNX (bakes obs normalizer — mandatory path)
uv run scripts/infer_policy.py --walking out.onnx   # CPU MuJoCo deployment rehearsal
uv run --with pytest pytest tests/

A 5-iteration smoke test at 64 envs catches ~95% of config errors for cents. Never launch a long run without one.

Repo map

  • src/mjlab_microduck/tasks/mdp.py — ALL custom MDP functions (rewards, events, observations, commands, curricula). Add new functions here, grouped by task.
  • src/mjlab_microduck/tasks/microduck_*_env_cfg.py — one cfg module per task family. microduck_velocity_env_cfg.py is the main walking recipe AND the shared base (robot, DR, obs, commands) other envs build on or mirror.
  • src/mjlab_microduck/tasks/__init__.py — task registration (base + -Backlash- variants).
  • src/mjlab_microduck/tasks/backlash.py — wraps any env cfg into its backlash twin.
  • src/mjlab_microduck/robot/microduck_constants.py — robot cfgs, HOME frame, BAM actuator cfg.
  • src/mjlab_microduck/robot/microduck/ — MJCF exports from Onshape (onshape-to-robot, one config_mjcf_*.json per model) + scenes + add_backlash.py.
  • src/mjlab_microduck/actuator/friction_dr_bam.py — BAM actuator + friction DR + backlash encoder.
  • scripts/ — export, infer, sim2real comparison, wandb helpers.
  • tests/ — cfg-invariant and mdp-function regression tests (CPU, no GPU needed).

Read the full file on GitHub · 246 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 246 lines · 4,000 tokens per session scan A aceb94476f03

Subscribe to this mod's changes

microduck_rl AGENTS.md is an instructions file published in the GitHub repository pollen-robotics/microduck_rl (724 stars, last pushed 4d ago), licensed Apache-2.0. It adds 4,000 tokens to every session, about $0.0200 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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