feat(agent): add CSV benchmark recorder + summary/embedding test scripts and skills

New files:
- scripts/model_benchmark.py — shared CSV recorder for all model tests
- scripts/test_summary_model.py — summary model quality benchmark (coherence, coverage, brevity, hallucination)
- scripts/test_embed_model.py — embedding model benchmark (dimension, cosine accuracy, speed)
- .agents/skills/test-summary-model/SKILL.md — skill for testing summary models
- .agents/skills/test-embed-model/SKILL.md — skill for testing embedding models
- benchmarks/README.md — schema documentation

Updated:
- .agents/skills/test-chat-model/SKILL.md — now also records to CSV

All three scripts write to benchmarks/model_benchmarks.csv with one row
per run per check. The CSV accumulates results across runs for comparison.

.agents/skills/test-embed-model/SKILL.md

@@ -0,0 +1,98 @@
+---
+name: test-embed-model
+description: Tests an embedding model (BOR_LLM_EMBED_MODEL in .env) across three dimensions — dimension consistency, cosine accuracy on semantic pairs, and embedding speed — then records results in benchmarks/model_benchmarks.csv. Use when the user asks to test or benchmark an embedding model, check embedding quality, or compare embedding models (e.g. "test the embed model", "check embedding dimension", "benchmark embed speed").
+---
+
+# Test an Embedding Model (dimension + cosine + speed)
+
+Tests the configured `BOR_LLM_EMBED_MODEL` (default `embed`) against
+three independent checks:
+
+1. **Dimension check** — output vector length matches `BOR_EMBEDDING_DIM`
+2. **Cosine accuracy** — semantically similar text pairs have higher
+   cosine similarity than dissimilar pairs (5 pairs tested)
+3. **Speed** — vectors produced per second (50 vectors)
+
+## Rules (non-negotiable)
+
+- **Do not touch** the semantic pair texts — they are the controlled
+  benchmark corpus. Changing them is a methodology change: flag it.
+- **Do not edit app code** (`app/`, `tests/`). This skill tests a model,
+  not the app. If the model exposes an app defect, report it — don't fix it.
+- `.env` is gitignored and is **not committed** — the model switch stays
+  a live dev setting, and the summary must say which model `.env` is left
+  on (default: the tested model).
+
+## Procedure
+
+All commands run from the repo root with `uv run`.
+
+### 1. Preconditions
+
+```bash
+podman compose up -d db
+grep -E "BOR_LLM_(BASE_URL|API_KEY|EMBED_MODEL|EMBEDDING_DIM)" .env
+```
+
+### 2. Switch the model (optional)
+
+Edit only `BOR_LLM_EMBED_MODEL` in `.env` (leave chat and summary alone):
+
+```
+BOR_LLM_EMBED_MODEL=<model>
+```
+
+### 3. Run the benchmark
+
+```bash
+# Single run (all three checks)
+uv run python -m scripts.test_embed_model
+
+# Specify a model explicitly
+uv run python -m scripts.test_embed_model --model embed-v2
+
+# Multiple runs for variance
+uv run python -m scripts.test_embed_model --runs 3
+```
+
+Wall time: ~0.5–2 s per run (fast — the embedding endpoint is lightweight).
+
+### 4. Read the verdicts
+
+Each check prints `✓` (PASS) or `✗` (FAIL):
+
+- **dimension**: `actual_dim == expected_dim` (from `BOR_EMBEDDING_DIM`, default 768)
+- **cosine**: similar-pair margin ≥ 0.05 (similar > dissimilar by at least 0.05 cosine)
+- **speed**: no NaN/Inf vectors in output; rate reported as vec/s
+
+Gate: PASS if all three checks pass.
+
+### 5. Record + commit
+
+Results are automatically appended to `benchmarks/model_benchmarks.csv`
+(one row per check per run). Commit:
+
+```bash
+git add benchmarks/model_benchmarks.csv
+git commit --no-gpg-sign \
+  -m "docs(agent): record the <model> embedding benchmark" \
+  -m "<one-paragraph body: dimension match, cosine margin, speed, any failures>"
+```
+
+### 6. Summary
+
+Table of dimension / cosine margin / speed per run, the one-line
+conclusion, and a note that `.env` is now on `<model>`.
+
+## Troubleshooting
+
+- **dimension mismatch** — the model's output vectors are a different
+  size than expected. This is a configuration error: check the model's
+  docs for its embedding dimension and update `BOR_EMBEDDING_DIM`.
+- **cosine margin < 0.05** — the embeddings don't separate similar from
+  dissimilar texts well. The model may be a poor embedding model or
+  the semantic pairs are too generic. Report the margin.
+- **NaN/Inf vectors** — the model's embedding endpoint is broken or
+  the input text is malformed. Check the endpoint directly.
+- **very slow** — >100 vec/s is typical for a local endpoint; remote
+  endpoints may be slower due to network latency. Report the rate.