---
name: test-summary-model
description: Tests a summary model (BOR_LLM_SUMMARY_MODEL in .env) against a fixed set of 8 source texts from the fixture KB — evaluates coherence, coverage, brevity, and hallucination detection — then records results in benchmarks/model_benchmarks.csv. Use when the user asks to test or benchmark a summary model, compare summary models, or evaluate summary quality (e.g. "test lite summary", "compare summary models", "how good is turbo at summarizing").
---

# Test a Summary Model (quality benchmark)

Tests the configured `BOR_LLM_SUMMARY_MODEL` (default `lite`) against
8 source texts extracted from the fixture KB. Each text is a realistic
documentation excerpt (~300–500 chars). The model is asked to summarize
each in 2–4 sentences.

## Rules (non-negotiable)

- **Do not touch** the fixture source 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|SUMMARY_MODEL)" .env
```

### 2. Switch the model (optional)

Edit only `BOR_LLM_SUMMARY_MODEL` in `.env` (leave chat and embed alone):

```
BOR_LLM_SUMMARY_MODEL=<model>
```

### 3. Run the benchmark

```bash
# Single run
uv run python -m scripts.test_summary_model

# Specify a model explicitly
uv run python -m scripts.test_summary_model --model turbo

# Multiple runs for variance
uv run python -m scripts.test_summary_model --runs 3
```

Wall time: ~3–5 s per text, ~25–40 s total for 8 texts.

### 4. Read the verdicts

The `gate:` line: `PASS|FAIL turns=N answered=N quality=Q hallucinations=H/N coherence=C/5 coverage=V/5 brevity=B/5 (wall Ts)`.

Scoring rubric:
- **coherence** (0–5): non-empty, multi-sentence, starts with capital
- **coverage** (0–5): captures ≥2 key numbers/facts from source
- **brevity** (0–5): summary length / source length ratio 0.10–0.25 = 5, 0.05–0.35 = 4, etc.
- **hallucination**: detected when summary contains >3 uncommon tokens not in source

Quality score = coherence×0.35 + coverage×0.40 + brevity×0.25, scaled 0–100,
with a 5-point penalty per hallucination.

Gate: PASS if quality ≥ 70 and hallucination rate < 25%.

### 5. Record + commit

Results are automatically appended to `benchmarks/model_benchmarks.csv`.
Also append a summary line to `benchmarks/README.md` if it exists, or
create it:

```bash
git add benchmarks/model_benchmarks.csv
git commit --no-gpg-sign \
  -m "docs(agent): record the <model> summary benchmark" \
  -m "<one-paragraph body: quality score, hallucination count, wall time, comparison to other models>"
```

### 6. Summary

Table of quality / hallucinations / coherence / coverage / brevity / wall
per run, the one-line conclusion, and a note that `.env` is now on
`<model>`.

## Troubleshooting

- **endpoint down / all turns error** — check
  `curl -s $BOR_LLM_BASE_URL/models` with the key; the gate will exit 1
  with `answered<8`. Report, don't retry-loop.
- **low brevity** — the model is restating rather than condensing.
  This is model-specific; try a more explicit prompt or a different model.
- **hallucinations** — the model is adding details not in the source.
  This is the most common failure mode; tighten the prompt or switch models.