HiFi Review

Objective, source-traceable evaluation of HiFi gear — transducers (IEM/HP/TWS) and source gear (DAC/amp/DAP) — as a bilingual, evidence-traced verdict. Use f...

Objective, evidence-traceable evaluation of a HiFi device. Evidence hierarchy: ① measurement/curve data (anchor) → ② reviews (what measurement can't show) → ③ specs/family (priors). Literary phrasing may color but never exceed the evidence. Output bilingual (中文 + English). Read-only. Accuracy ≫ speed.

Two classes: transducer (IEM/HP/TWS) → 量感 + 风格 from FR-vs-target, technicalities from review consensus only (never measured). source (DAC/amp/DAP) → measured competence + system matching, chip/topology as priors — resist 玄学: if it measures transparent, say so.

Protocol — 8 steps (branch on device class)

1. Scope & classify — confirm objective eval/compare; set device_class ∈ {transducer, source}. Reject buying-rec / EQ / speakers / non-audio.
2. Identify — exact model + variant (cable/filter/pad/firmware) + driver/chip; sub-category → rig+target or measurement set. Disambiguate only if genuinely ambiguous.
3. Gather (live) — fetch per class; source-registry.json targets known reviewers + search hints; record tier + style-lean + freshness + lang. → rules/retrieval-playbook.md.
4. Clean & normalize (mandatory) — dedup, strip marketing/non-evidence, normalize to glossary, reconcile scales, flag outliers, keep provenance. → rules/data-cleaning.md.
5. Measure & quantize — transducer: python3 scripts/fr_analyze.py <fr> --target <id>; source: python3 scripts/source_analyze.py --sinad … --zout … [--target-z …]. Screenshot-only FR → qualitative. → rules/tonal-mapping.md, rules/source-gear-eval.md.
6. Corroborate — transducer: technicalities from consensus, style-weighted (measurement-backed high-trust regardless of source; impression-led bias-corrected), N/M agreement, flag conflicts → rules/technicalities-from-reviews.md. source: engineering + transparency verdict.
7. Synthesize — class-discriminated profile + render: compact bilingual summary OR a ~4000字 长文 (rules/longform-review.md); both render only from evidence; tag claims measured|consensus|prior + confidence; gaps "证据不足". → rules/literary-rendering.md, compare → rules/comparison-mode.md.
8. Self-verify — python3 scripts/validate_output.py <out.json>; emit trace; never pass a FAIL.

Always obey rules/accuracy-guardrails.md: never invent a dB/curve; flag incompatible rig/target comparisons; record dissent.

Modules

File	When to load
rules/retrieval-playbook.md	Step 3 — find curves/reviews/specs per class; squig/screenshot; stop rule
rules/data-cleaning.md	Step 4 — dedup / de-market / normalize / reconcile / flag / provenance
rules/tonal-mapping.md	Step 5 transducer — bands, dB→量感, 风格, tilt, peaks, target/rig select
rules/technicalities-from-reviews.md	Step 6 transducer — review-only attrs, consensus + style weight
rules/source-gear-eval.md	Step 5–6 source — SINAD/THD/Zout/power tiers, transparency, matching
rules/accuracy-guardrails.md	Always — rig/target match, never-invent, conflicts, EOL
rules/literary-rendering.md	Step 7 — anchored 文学化, provenance, bilingual, no over-claims
rules/comparison-mode.md	Compare — target/rig alignment, per-band delta, not-comparable
rules/longform-review.md	Step 7 — ~4000字 长文: structure / length / anchoring
references/*.json + signature-glossary.md	single sources of truth + glossary

Scripts

File	Usage
fr_analyze.py <fr.csv> --target <id> --rig <r>	transducer → 量感 / 风格 / tilt / peak-dip features
source_analyze.py --sinad N --zout N [--power --target-z --target-sens]	source → tier + drive/damping matching
compare.py <a> <b> --target <id>	two devices → band + tilt deltas, rig guard
infer_target.py <fr> --rig <r>	guess intended target (ranks same-rig targets)
validate_output.py <eval.json>	schema + traceability gate (exit 1)
check_longform.py <review.md> --class <c> [--backing json]	长文 QA: 字 + sections + backing gate