refactor(halcon): single-skill plugin layout → invoke as /halcon (not /halcon:halcon)

Move SKILL.md + references/ scripts/ evals/ from skills/halcon/ up to the
plugin root. Per Claude Code plugins-reference, a plugin with SKILL.md at its
root and no skills/ subdir is auto-loaded as a single-skill plugin (v2.1.142+),
so the invocation name = frontmatter name = halcon → clean /halcon.
Bump plugin.json 2.0.0 → 2.0.1 so existing installs receive the update.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

plugins/halcon/references/mcp-server.md

@@ -0,0 +1,64 @@
+# halcon-mcp — the HALCON MCP server
+
+A local MCP server that exposes common HALCON algorithms + script runners as
+tools, for driving HALCON experiments without hand-writing Python each time. It
+lives at the **project root** (outside this skill): `halcon-mcp/`. Source of truth
+is `halcon-mcp/README.md`; this file is the pointer from the skill.
+
+## What it is
+
+- Python + official `mcp` SDK (FastMCP), **stdio** transport.
+- Runs **in-process** in the shared HALCON venv (`import halcon`), so algorithm
+  tools call operators directly and return structured JSON + the **equivalent
+  HDevelop code** + an **overlay PNG**.
+- Errors return `{ok:false, error:...}` (no crash) so the model can self-correct.
+
+## Tools (11)
+
+Run/experiment: `halcon_check_env`, `halcon_run_script` (inline HDevelop snippet),
+`halcon_run_program` (.hdev), `halcon_run_procedure` (.hdvp), `halcon_run_hrun`
+(headless, GBK-decoded), `halcon_open_in_hdevelop` (GUI), `halcon_teach_roi`
+(interactive pan/zoom/box-select ROI → `.hobj`, or headless via `box`; runs
+`halcon-mcp/roi_picker.py` in a subprocess and returns the saved bbox).
+
+Algorithms: `halcon_enhance_image`, `halcon_find_circles` (edge-fit + metrology,
+same chain as `examples/find_circles.hdvp`), `halcon_measure_metrology`
+(circle/line/rectangle2), `halcon_match_shape` (2D shape-based matching).
+
+`halcon_teach_roi`'s output feeds `halcon_find_circles` / `halcon_match_shape` /
+`halcon_measure_metrology` as their `roi_file` / `template_roi`.
+
+## Prerequisite (one-time)
+
+Add `mcp` to the shared venv (mvtec-halcon is already there):
+
+```bash
+/mnt/c/Users/NAURA/.local/bin/uv.exe pip install mcp
+```
+
+## Register in Claude Code (this WSL)
+
+A ready config is at the project root as `.mcp.json` (copied from
+`halcon-mcp/.mcp.json.example`). Or via CLI:
+
+```bash
+claude mcp add halcon -- /mnt/c/Users/NAURA/.local/bin/uv.exe run \
+  --directory C:/workspace/agent-studio/halcon-001 python halcon-mcp/server.py
+```
+
+Then `/mcp` lists `halcon`. The server is a Windows process launched from WSL;
+it speaks JSON-RPC over stdio across the interop boundary (verified working).
+
+## Test without a client
+
+```bash
+/mnt/c/Users/NAURA/.local/bin/uv.exe run python halcon-mcp/examples/smoke_test.py
+```
+
+## Relationship to this skill
+
+The skill is the **knowledge** (how to run HALCON here, the manuals, recipes);
+the MCP server is the **executable surface** built from that knowledge. When a
+task needs repeatable, parameterized calls (enhance / find circles / metrology /
+matching / run a script), prefer the `halcon_*` MCP tools; for one-off custom
+pipelines, use the skill's `scripts/` runners or write an `.hdvp`.