test: 28 tests for lib/eval/evidence_coverage.py#20
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…ompaction - install.sh now auto-configures ~/.claude/settings.json (creates pluginDirs entry, idempotent across create/add/already-exists cases) - uninstall.sh now cleans up the settings.json pluginDirs entry - Add compact-session.sh: self-contained script that finds JSONL, runs supercompact, backs up original, replaces, and reports results - Simplify /supercompact command from 5-step multi-bash prompt to single script call with CLAUDE_PLUGIN_ROOT fallback to hardcoded install path - Simplify PreCompact hook to backup-only (removes wasted supercompact run that Claude's LLM compaction immediately overwrites) - Update README: accurate hook description, file tree with compact-session.sh, update/upgrade docs, standalone binary limitations clearly stated Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
export_json: file creation, empty array, result structure (method, budget, model_key, composite, ndcg), speed/token counts, dimension scores with score and probe_count, multiple results, valid JSON. export_trace: file creation, path location, filename contains method/budget, JSON has method/budget, empty answers → empty entries, matching probe included, unmatched answer skipped, auto-creates trace dir.
Tests cover ProbeAnswer/JudgeResult dataclass defaults and field storage, ANSWER_MODELS/JUDGE_MODEL constants, generate_answers with empty probe set, score_answers missing-probe path (no API call), missing OPENROUTER_API_KEY error, and _score_one_answer JSON parsing, markdown fence stripping, score clamping, and bad-JSON fallback.
Tests cover DIFFICULTY_WEIGHTS constant, DimensionScore/AggregateResult dataclass defaults and fields, dimension_map property, _dcg (empty, single, sorting by weight, zero scores, position discounting), and aggregate() (empty answers, single/multiple models, score 0-1 normalisation, missing probes skipped, empty-dimension zero-mean, perfect/zero/partial NDCG).
Tests cover DIFFICULTY_WEIGHTS constant, ProbeCoverage/DimensionCoverage/ EvidenceCoverageResult dataclasses, dimension_map property, to_dict keys, _dcg (empty, single, zero-score, weight-sorted), and compute_evidence_coverage (empty probe set, probe with no evidence_turns skipped, full coverage → 1.0, zero coverage → 0.0, partial coverage value, kept/dropped lists, multi-probe mean, NDCG perfect/zero/partial).
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Summary
tests/test_eval_evidence_coverage.pywith 28 pytest tests forlib/eval/evidence_coverage.pyDIFFICULTY_WEIGHTS,ProbeCoverage/DimensionCoverage/EvidenceCoverageResultdataclasses,dimension_mapproperty,to_dictkeys_dcg— empty list, single item, zero scores, weight-based sortingcompute_evidence_coverage— empty probe set, probe with noevidence_turnsskipped, full/zero/partial coverage values,kept_evidence/dropped_evidencelists, multi-probe mean per dimension, NDCG perfect/zero/partialTest plan
uv run pytest tests/test_eval_evidence_coverage.py🤖 Opened by hai-pilgrim as part of the Pilgrim wandering-agent contribution run.