Reasoning models burn thousands of completion tokens before the
answer. Small per-test caps caused silent truncation and zero scores.
The runner now floors max_tokens at the config default (12000). The
client parses finish_reason. Reports mark truncated cells and the
JSON format carries response text for debugging. A stderr progress
reporter prints one line per completed request; --quiet disables it.
Add 8 macOS tests, 8 Linux tests, and 9 Kubernetes tests to
internal/tests. Each operations subcategory now holds 10 deterministic
tests, up from 2, 2, and 1.
Tests cover BSD-versus-GNU tooling, launchd, Proxmox pct exec, systemd
timers, cgroup v2 diagnosis, and ArgoCD GitOps scenarios. Evaluators use
ContainsAll, ContainsAny, Regex, JSONField, Numeric, and weighted
composites, matching the existing catalog style.
Every evaluator unit test asserts three correct phrasings and two wrong
ones, per the expansion contract. Ground truth for numeric and ordered
answers carries a derivation comment.
catalog_test.go still expects 17 tests and fails as documented. This
commit does not modify that file.
Bring agents/tool-routing and agents/planning up to 10 tests each.
Add a new agents/delegation subcategory with 10 tests. Split
agents.go into agents_toolrouting.go, agents_planning.go, and
agents_delegation.go, each with a sibling _test.go file, and reduce
agents.go to a thin registration aggregator.
Every new prompt is self-contained and forces a JSON or number-only
answer. Set-valued answers use JSONStringSet or a custom stage-set
evaluator so no arbitrary order is required. Dependency-graph fixtures
that force a single valid order are verified unique in the unit tests
by brute-force enumeration over every permutation. Numeric ground
truth is derived by running the same algorithm the fixture describes,
then cross-checked against an independently hand-derived value.
MaxTokens is omitted on all 28 new tests per the expansion rules; the
2 existing tests keep their prior value unchanged.
Framework runs a category/subcategory test catalog against multiple
LLMs through an OpenAI-compatible endpoint. Deterministic evaluators
score each response. Reports render as table, markdown, or JSON.
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