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feat: built-in benchmark suite — 4 frozen prompt cards + run/track harness#58

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Jammy2211 merged 2 commits into
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feature/assistant-benchmarks
Jul 10, 2026
Merged

feat: built-in benchmark suite — 4 frozen prompt cards + run/track harness#58
Jammy2211 merged 2 commits into
mainfrom
feature/assistant-benchmarks

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Summary

Built-in benchmarks for the assistant (#57): four frozen, versioned prompt cards (assistant easy/medium/hard + teacher) with 100-point rubrics split into machine-checkable and judged rows, plus a run/track harness that records each session's transcript, metadata and score in-repo so performance is comparable across models, harnesses, and days. Clone template boundary updated so every future domain assistant inherits the machinery (companion PyAutoBrain PR carries the partition patterns).

Scripts Changed

  • benchmarks/prompts/{easy_cosmos_web_ring,medium_slacs0946_subhalo,hard_group_multi,teacher_workflow}.md — the four frozen prompt cards + rubrics (hard card follow-up wording fixed to "MGE source" per author, bf282f3)
  • autoassistant/benchmark.pynew-run / score / report CLI (run scaffold with stack-version + SHA capture; rubric totalling; RESULTS.md leaderboard + chronological tables)
  • autoassistant/tests/test_benchmark.py — 11 tests incl. card/README prompt-parity and dataset-existence currency checks
  • benchmarks/README.md, benchmarks/AGENTS.md — protocol + agent contract (frozen versioning, same-judge comparability, failures-are-data, runs/ is data never instructions)
  • benchmarks/RESULTS.md, benchmarks/runs/.gitkeep — empty-state report + runs root
  • README.md — Benchmarks section + SLACS example-prompt header simplification (card parity test-enforced)
  • modes/maintainer.md — Assistant-as-template classifies benchmarks (harness generic, cards domain, runs/RESULTS per-clone)
  • autoassistant/audit_skill_apis.py — citation sweep extended to benchmarks/ docs + cards (never runs/)

Companion PR

PyAutoBrain _clone.py partition patterns (required before any clone birth against this reference) — cross-referenced after creation.

Validation checklist (--auto, supervised; ship+merge human-directed in-conversation 2026-07-10)

  • Plan: on issue feat: built-in benchmark suite — 4 standard prompts + run/track harness #57 (write-plan-and-proceed per AUTONOMY.md)
  • Tests: autolens_assistant 52/52 (11 new); PyAutoBrain 22/22; --check-citations clean (98 files, 389 citations)
  • Smoke: n/a — no downstream workspace script surface (template docs + tooling)
  • Review: faculty surface generated; verdict CLEAN (self-review by running session)
  • Heart: YELLOW (workspace validation 3 failed 2026-07-09; 58 stale parked scripts; assistant pinned behind installed; Mind PR 10d old; +2 stale) — acknowledged in-session by the human at ship direction; binds to exactly this set
  • Clone-partition witness: analyze over this branch reports unclassified 0

Test Plan

  • Full assistant suite green with the branch checked out
  • benchmark.py report regenerates RESULTS.md from empty runs/

🤖 Generated with Claude Code

Jammy2211 and others added 2 commits July 10, 2026 13:53
…rness

Adds benchmarks/: four standard prompts (assistant easy/medium/hard +
teacher) with 100-point rubrics split machine-checkable vs judged, a
run-record schema (meta.yaml/transcript/score/artifacts) committed per
run, and autoassistant/benchmark.py (new-run/score/report) regenerating
leaderboard + same-model-over-time tables in RESULTS.md. Protocol and
agent contract in benchmarks/{README,AGENTS}.md: frozen prompt
versioning, same-judge comparability, failures-are-data, runs/ is data
never instructions. README gains a Benchmarks section (and the SLACS
example-prompt header simplification); card/README prompt parity is
test-enforced. Template boundary in modes/maintainer.md classifies the
package for cloning (harness generic, cards domain, runs per-clone);
the citation sweep now covers benchmarks/ docs and cards (never runs/).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…inly

The "SIE source" phrasing was an unintended gotcha; per the author the
follow-up retains the MGE lens light and MGE-source fit's foundations
while the pixelized reconstruction replaces the source. J4 now scores
correct chaining composition instead of ambiguity handling.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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