Validate direct state action observation and reward buffer contracts for Isaac Gym style pipelines.
Scanned 9/9/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill tensor_api_buffer_contract --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: tensor_api_buffer_contract
description: Validate direct state action observation and reward buffer contracts for Isaac Gym style pipelines.
---
# Tensor API Buffer Contract
Use this skill when checking whether a reduced or real recovery preserves Isaac Gym's direct tensor-buffer idea. The skill validates named state, action, observation, reward, and reset buffers, their shapes, and whether a claimed direct path includes forbidden CPU staging.
## Inputs
- Buffer descriptors with `name`, `role`, `shape`, `producer`, `consumer`, and `direct` fields.
- Expected environment count for the leading batch dimension.
## Outputs
- A report with `ok`, `errors`, `direct_flow_ok`, and `roles_present`.
## Workflow
1. Collect descriptors for physics state, policy action, observation, reward, and reset buffers.
2. Verify each descriptor has a leading batch dimension matching the environment count.
3. Reject missing producer/consumer ownership or `direct=False` when direct flow is claimed.
4. Save the report as recovery evidence or use it as a cross-check from a harness.
## Validation
Run `python tests/test_buffer_contract.py` or validate the skill tree with tests enabled.
## Limitations
This deterministic checker verifies the contract. It cannot prove CUDA pointer aliasing without a real GPU tensor runtime.
## Edge-Case Refinement
A negative check must fail when a descriptor uses the wrong leading environment dimension or marks a buffer as non-direct, because such staging would violate the paper Tensor API mechanism.
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