Jp Romaji

jp-romaji

Convert Japanese to speakable ASCII Hepburn romaji. House style: house-style.md. Reader prompts: prompts/generic.md, prompts/media.md.

Classify (required first)

Default: generic.

Flip to media when the user/caller says it is media (lyrics, anime line, VN, song, …) or names a work (“Idol by YOASOBI”, “Evangelion title”). Do not infer media from “this text looks like lyrics.”

If media and the subtype is not given: infer lyrics | title/name | dialogue | mixed, state it, allow override. mixed is per-line.

If generic and there is no Japanese and no named work: ask for text. Stop.

Mechanical CLI

This skill lives in the directory that contains this SKILL.md. Follow the symlink if you reached it via ~/.agents/skills/jp-romaji. Resolve that directory, then:

uv run --project <that-dir>/scripts/romaji romaji --engine both

Feed source lines on stdin (or a temp file). Parse JSONL. Each line is orig plus pykakasi and cutlet objects (hira, kana, romaji, tokens).

Never invoke the CLI with a cwd-relative jp-romaji/scripts/romaji path.

Pipeline

1. Classify and state the mode.
2. Has source lines (user pasted Japanese):
   1. Run the CLI on those lines.
   2. Spawn one child (reader). Attach house-style.md plus the matching prompt. Give it mode, lines, and both engine JSON objects.
      • generic: prompts/generic.md. No web/search tools.
      • media: prompts/media.md. Child may use the web. Do not research in the main agent.
   3. Fuse the three voters. Emit.
3. No source lines, media (named work):
   1. Spawn the media child with the work identifier, mode, house-style.md, and prompts/media.md. No engine JSON.
   2. Child returns fetched_lines + per-line votes + sources.
   3. Run the CLI on fetched_lines.
   4. Fuse the three voters. Emit.
4. Main agent never browses on a media run. Generic child never browses.

If a child does not return parseable JSON, ask it once to resend the object only, then proceed or report the failure. Do not invent readings to fill a broken blob.

Fusion

Vote on hira / intended reading. Then apply house-style.md to the winning reading.

Carve-out: well-attested official Latin names/works/characters stay official.

Voters: pykakasi | cutlet | reader. The reader is the generic child or the media child. A voter that did not run does not vote and does not block +15. There is no separate research ballot; media sources and variant are appendix fields.

Treat two hira strings as the same reading if they match after removing spaces and converting katakana long-vowel ー to the previous vowel’s hiragana length. Toukyou / Tōkyō / Tokyo as romaji are not a conflict when hira matches.

Tie-break when two candidates are within 10 confidence points:

1. Reading shared by more voters
2. reader
3. cutlet
4. pykakasi

Then house-normalize (except official Latin names/works).

Confidence

Integer 0–100, per line and overall. Number + one-line reason.

Path ∈ {pykakasi, cutlet, reader}. Prior from the winning path:

• Mechanical winner (pykakasi or cutlet) on kanji-heavy text: cap 55
• Mechanical winner on non-kanji text: 70
• Reader winner on media: use the cited-tier prior in prompts/media.md. Do not copy those numbers here.
• Reader winner on generic: 40–65 (no source tier)

Then one agreement bonus, not stacked, over voters that ran:

• +10 if exactly 2 of 3 share the winning reading
• +15 if every voter that ran shares the winning reading
• −15 if the pick is reader-only (neither engine shares that reading)

If any line is below 60, overall cannot be above 75. Report the min line score in the appendix.

User-visible output

1. Stated mode (and inferred subtype if any).
2. Copy-paste block: winning house-style romaji, one line per source line.
3. Appendix:
   • columns (both modes): pykakasi | cutlet | reader | pick
   • all columns romaji
   • per-line confidence + reason; overall; min line
   • media only: sources and variant / official line from the child (not extra vote columns)

Internal kana only appears if it is the reason.

Out of scope

• Translation / gloss, audio/PV transcription, Chinese/Korean romanization
• User-facing kana/furigana, silent rewrite of the user’s Japanese, Anki export
• Dumping a whole series/game/VN
• Inferring media from “this looks like lyrics” without a mention or a named work
• Main-agent web research on a media run