docs: add generated SKILL.md discovery view

SKILL.md

@@ -0,0 +1,73 @@
+---
+name: diffusion-pusht
+description: >-
+  S1 Vision-Language-Action policy. Capabilities: push on t_shape. Diffusion Policy (~263M-param U-Net with 100-step DDPM denoiser) for the PushT 2-DoF pushing benchmark. Action chunks of length 8 within a horizon of 16. The chunk inference cost is dominated by the denoising loop, so cached pops are essentially free — this is the extreme test of the queue-drain contract. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.
+metadata:
+  openral_rskill: true            # generated discovery view of an rSkill
+  schema_version: 0.1
+  rskill_id: OpenRAL/rskill-diffusion-pusht
+  manifest: ./rskill.yaml
+  role: s1
+  kind: vla
+  model_family: diffusion
+  embodiment_tags: [pusht]
+  actions: [push]
+  objects: [t_shape]
+  scenes: [tabletop_2d]
+  sensors_required: ['rgb:observation.image']
+  state_dim: 2
+  action_dim: 2
+  runtime: pytorch
+  quantization: fp32/pytorch
+  chunk_size: 8
+  latency_budget: {per_chunk_ms: 1250.0}
+  license_code: Apache-2.0
+  license_weights: apache-2.0
+  weights_uri: hf://lerobot/diffusion_pusht
+  source_repo: hf://lerobot/diffusion_pusht
+  paper_url: https://arxiv.org/abs/2303.04137
+---
+
+# diffusion-pusht — rSkill discovery view
+
+> **Generated view, not a hand-written skill.** This `SKILL.md` is a discovery-only
+> mirror of [`rskill.yaml`](./rskill.yaml), produced by `tools/generate_rskill_skillmd.py`.
+> It lets tools that read the standard agent-skill format find and reason about this
+> OpenRAL rSkill. The `rskill.yaml` manifest is the single source of truth
+> (CLAUDE.md §1.3). Do not edit by hand — edit the manifest and regenerate.
+
+## What it is
+
+An OpenRAL **Vision-Language-Action policy** (`role: s1`, `kind: vla`). Diffusion Policy (~263M-param U-Net with 100-step DDPM denoiser) for the PushT 2-DoF pushing benchmark. Action chunks of length 8 within a horizon of 16. The chunk inference cost is dominated by the denoising loop, so cached pops are essentially free — this is the extreme test of the queue-drain contract.
+
+## Capabilities
+
+- **Verbs:** push
+- **Objects:** t_shape
+- **Scenes:** tabletop_2d
+- **Embodiments:** pusht
+
+## Why this is discovery-only
+
+An agent skill is natural-language instructions loaded into an LLM's context. An rSkill
+is an executable artifact: it carries a typed capability/embodiment contract, model weights,
+a runtime, and a license/provenance gate — none of which fit in freeform markdown. So an
+agent can use this view to *select* the right skill, but cannot *execute* it by loading
+this file. Execution always goes through the OpenRAL loader and the robot HAL.
+
+## License
+
+- **Code:** Apache-2.0.
+- **Weights:** `apache-2.0` — permissive / commercial-use OK
+
+## How to actually run it (not via an agent harness)
+
+```python
+from openral_rskill import rSkill
+
+skill = rSkill.from_pretrained("OpenRAL/rskill-diffusion-pusht")
+# the loader validates embodiment / sensors / runtime / quantization against the target
+# RobotDescription and enforces the weight-license gate before any weights load.
+```
+
+See [`rskill.yaml`](./rskill.yaml) for the authoritative, validated manifest.