hifi-review
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)
- Scope & classify — confirm objective eval/compare; set
device_class ∈ {transducer, source}. Reject buying-rec / EQ / speakers / non-audio. - Identify — exact model + variant (cable/filter/pad/firmware) + driver/chip; sub-category → rig+target or measurement set. Disambiguate only if genuinely ambiguous.
- Gather (live) — fetch per class;
source-registry.jsontargets known reviewers + search hints; record tier + style-lean + freshness + lang. →rules/retrieval-playbook.md. - Clean & normalize (mandatory) — dedup, strip marketing/non-evidence, normalize to glossary, reconcile scales, flag outliers, keep provenance. →
rules/data-cleaning.md. - 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. - 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. - Synthesize — class-discriminated profile + render: compact bilingual summary OR a ~4000字 长文 (
rules/longform-review.md); both render only from evidence; tag claimsmeasured|consensus|prior+ confidence; gaps "证据不足". →rules/literary-rendering.md, compare →rules/comparison-mode.md. - Self-verify —
python3 scripts/validate_output.py <out.json>; emittrace; 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 |
