docs: add generated SKILL.md discovery view

SKILL.md

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+---
+name: rtdetr-coco-r18
+description: >-
+  S1 object detector. Capabilities: detect on person, cup, bottle, bowl, chair, table. RT-DETR-L (Real-Time DEtection TRansformer, large variant) trained on COCO and exported to ONNX. Runs on the camera tee and publishes ObjectsMetadata to /openral/perception/objects. 80 COCO categories. Apache-2.0 weights. Reference latency ~20 ms on GPU, ~45 ms on CPU. See ADR-0037 for the detector rSkill kind 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-rtdetr-coco-r18
+  manifest: ./rskill.yaml
+  role: s1
+  kind: detector
+  actions: [detect]
+  objects: [person, cup, bottle, bowl, chair, table]
+  scenes: [tabletop, kitchen, indoor]
+  sensors_required: [rgb]
+  runtime: onnx
+  quantization: fp32/onnx
+  chunk_size: 1
+  latency_budget: {per_chunk_ms: 50.0}
+  license_code: Apache-2.0
+  license_weights: apache-2.0
+  weights_uri: local://rskills/rtdetr-coco-r18
+  source_repo: hf://PekingU/rtdetr_r18vd_coco_o365
+  paper_url: https://arxiv.org/abs/2304.08069
+---
+
+# rtdetr-coco-r18 — 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 **object detector** (`role: s1`, `kind: detector`). RT-DETR-L (Real-Time DEtection TRansformer, large variant) trained on COCO and exported to ONNX. Runs on the camera tee and publishes ObjectsMetadata to /openral/perception/objects. 80 COCO categories. Apache-2.0 weights. Reference latency ~20 ms on GPU, ~45 ms on CPU. See ADR-0037 for the detector rSkill kind contract.
+
+## Capabilities
+
+- **Verbs:** detect
+- **Objects:** person · cup · bottle · bowl · chair · table
+- **Scenes:** tabletop · kitchen · indoor
+
+## 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-rtdetr-coco-r18")
+# 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.