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inline-word-links
Guidelines for adding inline cross-reference links to example sentences and notes. Includes common word reference table.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Guidelines for adding inline cross-reference links to example sentences and notes. Includes common word reference table.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
General quality standards for all je-dict-1 dictionary entries. Use when creating or revising any entry type.
Requirements for creating or revising adjective entries in je-dict-1. Covers forms, conjugations, predicate vs modifier usage, and similar word distinctions.
Requirements for creating and revising example sentences in je-dict-1. Covers minimum counts, progressive length, vocabulary restrictions by tier, and quality standards.
Requirements for creating nouns, counters, adverbs, and expressions in je-dict-1. Covers collocations, counting patterns, register, and cultural notes.
Requirements for creating or revising verb entries in je-dict-1. Covers transitivity, aspect/ている behavior, particle patterns, and collocations.
Requirements for formatting and structuring the notes field in je-dict-1 entries. Covers formatting, content organization, and readability standards.
| name | inline-word-links |
| description | Guidelines for adding inline cross-reference links to example sentences and notes. Includes common word reference table. |
This skill covers how to add cross-reference links within example sentences and notes. These links allow users to click on any word in an example to navigate to its dictionary entry.
Comprehensive polish (prompts/comprehensive_polish.md) requires full inline link coverage on every Japanese word in both example sentences AND the notes field, with noentry markers for words that lack entries. The rules below apply equally to Japanese text wherever it appears in an entry — there is no "examples only" carve-out anymore. The headword itself is not self-linked.
The link format uses special Unicode delimiters:
⟦{surface|reading}→baseform:entry_id⟧
Components:
⟦ (U+27E6) - Opening bracketsurface - The word as it appears in the sentence (may include furigana notation)→ (U+2192) - Arrow separatorbaseform - The dictionary form of the word (for tooltip display): (U+FF1A) - Fullwidth colon separatorentry_id - The dictionary entry ID (e.g., 00111_hon)⟧ (U+27E7) - Closing bracketLinks MUST be verified semantically, not programmatically.
Before adding any link, you MUST:
| Error Type | Example | Problem |
|---|---|---|
| Wrong homograph | の → 野 (field) | の in most contexts is the particle, not the noun 野 |
| Wrong word boundary | ものです → もの + です | May need to link as a single grammatical pattern |
| Conjugated form mismatch | 食べました → 食べる | Correct, but verify the verb is the intended one |
| Compound splitting | 日本語 → 日本 + 語 | Should link as single compound if entry exists |
For each word you intend to link:
〜, …) — but the surrounding Japanese in a pattern like 〜に対して should be linkedFor words that should be linked but lack an entry, use noentry (see below) and add the word to candidate_words.json with a note like "seen in entry XXXXX".
noentryFor words without dictionary entries:
⟦{矍鑠|かくしゃく}→矍鑠:noentry⟧
This preserves the markup for future linking but renders as plain text.
Always pair noentry with a candidate: when you mark a word noentry, also add it to candidate_words.json so the new-entry workflow can pick it up:
python3 build/manage_candidates.py add "矍鑠" "かくしゃく" "vigorous (despite age); seen in entry XXXXX"
This is what closes the dictionary in on itself: words that already appear get prioritized for entry creation.
Original:
"japanese": "{本|ほん}を{読|よ}む。"
With links:
"japanese": "⟦{本|ほん}→本:00111_hon⟧⟦を→を:00422_wo⟧⟦{読|よ}む→読む:00426_yomu⟧。"
Original:
"japanese": "{私|わたし}は{日本語|にほんご}が{分|わ}かります。"
With links:
"japanese": "⟦{私|わたし}→私:02988_watashi⟧⟦は→は:00079_ha⟧⟦{日本語|にほんご}→日本語:00614_nihongo⟧⟦が→が:00051_ga⟧⟦{分|わ}かります→分かる:00463_wakaru⟧。"
Original:
"japanese": "{彼|かれ}は{来|き}ませんでした。"
With links (note: conjugated forms link to dictionary form):
"japanese": "⟦{彼|かれ}→彼:01292_kare⟧⟦は→は:00079_ha⟧⟦{来|き}ませんでした→来る:00254_kuru⟧。"
Use this table for quick reference. For words not listed, search the dictionary.
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00051_ga | が | が | subject marker |
| 00079_ha | は | は | topic marker |
| 00314_ni | に | に | location/direction/time marker |
| 00422_wo | を | を | direct object marker |
| 00484_mo | も | も | also, too, even |
| 00490_made | まで | まで | until, to, as far as |
| 00502_de | で | で | at, by, with (location/means) |
| 00504_kara | から | から | from, because |
| 00512_to | と | と | and, with, quotation |
| 02473_he | へ | へ | direction marker (toward) |
| 09472_no | の | の | possessive marker, nominalizer |
| 09473_ka | か | か | question marker, or |
| 09474_ne | ね | ね | confirmation particle |
| 09475_yo | よ | よ | emphasis particle |
| 09476_yori | より | より | than, from (comparison) |
| 09477_kedo | けど | けど | but, although |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00534_dare | 誰 | だれ | who |
| 00536_itsu | いつ | いつ | when |
| 00539_doko | どこ | どこ | where |
| 00543_dou | どう | どう | how, in what way |
| 00547_dore | どれ | どれ | which one |
| 00551_dono | どの | どの | which (+ noun) |
| 00498_nani | 何 | なに | what |
| 02988_watashi | 私 | わたし | I, me |
| 00915_ano | あの | あの | that (over there) |
| 00919_asoko | あそこ | あそこ | over there |
| 00961_koko | ここ | ここ | here |
| 00962_kono | この | この | this |
| 00991_soko | そこ | そこ | there |
| 00993_sono | その | その | that |
| 00994_sore | それ | それ | that (thing) |
| 01292_kare | 彼 | かれ | he, him |
| 01286_kanojo | 彼女 | かのじょ | she, her |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00006_aru | ある | ある | to exist (inanimate) |
| 00119_iku | 行く | いく | to go |
| 00254_kuru | 来る | くる | to come |
| 00283_miru | 見る | みる | to see, look, watch |
| 00322_nomu | 飲む | のむ | to drink |
| 00392_suru | する | する | to do, to make |
| 00396_taberu | 食べる | たべる | to eat |
| 00426_yomu | 読む | よむ | to read |
| 00458_shiru | 知る | しる | to know, to learn |
| 00463_wakaru | 分かる | わかる | to understand |
| 00467_hanasu | 話す | はなす | to speak, talk |
| 00469_matsu | 待つ | まつ | to wait |
| 00470_neru | 寝る | ねる | to sleep |
| 00473_tsukau | 使う | つかう | to use |
| 00474_wasureru | 忘れる | わすれる | to forget |
| 00477_kaku | 書く | かく | to write |
| 00478_motsu | 持つ | もつ | to hold, have |
| 00481_tsukuru | 作る | つくる | to make, create |
| 00482_au | 会う | あう | to meet |
| 00483_kiku | 聞く | きく | to hear, listen, ask |
| 00489_kau | 買う | かう | to buy |
| 00492_omou | 思う | おもう | to think, feel |
| 00494_hairu | 入る | はいる | to enter |
| 00495_iru | いる | いる | to exist (animate) |
| 00503_hataraku | 働く | はたらく | to work |
| 00508_kaeru | 帰る | かえる | to return home |
| 00511_noru | 乗る | のる | to ride, get on |
| 00513_deru | 出る | でる | to go out, leave |
| 00515_iu | 言う | いう | to say, tell |
| 00519_hashiru | 走る | はしる | to run |
| 00520_morau | もらう | もらう | to receive |
| 00521_okiru | 起きる | おきる | to wake up |
| 00526_oshieru | 教える | おしえる | to teach |
| 00527_benkyousuru | 勉強する | べんきょうする | to study |
| 00531_hajimeru | 始める | はじめる | to begin |
| 00538_aruku | 歩く | あるく | to walk |
| 00546_ageru | あげる | あげる | to give, raise |
| 00550_asobu | 遊ぶ | あそぶ | to play |
| 00557_dekiru | できる | できる | to be able to |
| 00567_owaru | 終わる | おわる | to end |
| 01970_naru | なる | なる | to become |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00025_chiisai | 小さい | ちいさい | small, little |
| 00118_ii | いい | いい | good, fine |
| 00335_ookii | 大きい | おおきい | big, large |
| 00464_yasui | 安い | やすい | cheap |
| 00488_furui | 古い | ふるい | old |
| 00491_nagai | 長い | ながい | long |
| 00500_takai | 高い | たかい | high, expensive |
| 00506_atarashii | 新しい | あたらしい | new |
| 00510_mijikai | 短い | みじかい | short |
| 00514_hayai | 速い/早い | はやい | fast, early |
| 00517_muzukashii | 難しい | むずかしい | difficult |
| 00529_tooi | 遠い | とおい | far |
| 00530_chikai | 近い | ちかい | near |
| 00533_osoi | 遅い | おそい | slow, late |
| 00585_akai | 赤い | あかい | red |
| 00586_aoi | 青い | あおい | blue |
| 00587_shiroi | 白い | しろい | white |
| 00588_kuroi | 黒い | くろい | black |
| 00589_omoshiroi | 面白い | おもしろい | interesting |
| 00591_isogashii | 忙しい | いそがしい | busy |
| 01107_hoshii | 欲しい | ほしい | wanted, desired |
| 01118_nai | ない | ない | nonexistent |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00111_hon | 本 | ほん | book |
| 00468_jikan | 時間 | じかん | time, hour |
| 00476_hito | 人 | ひと | person |
| 00480_shigoto | 仕事 | しごと | work, job |
| 00486_toshi | 年 | とし | year, age |
| 00487_yoru | 夜 | よる | night |
| 00496_kyou | 今日 | きょう | today |
| 00497_mise | 店 | みせ | store, shop |
| 00499_sakana | 魚 | さかな | fish |
| 00501_ashita | 明日 | あした | tomorrow |
| 00505_michi | 道 | みち | road, way |
| 00507_heya | 部屋 | へや | room |
| 00516_kuruma | 車 | くるま | car |
| 00522_densha | 電車 | でんしゃ | train |
| 00523_hana | 花 | はな | flower |
| 00524_ki | 木 | き | tree |
| 00525_me | 目 | め | eye |
| 00558_kao | 顔 | かお | face |
| 00560_kuchi | 口 | くち | mouth |
| 00569_te | 手 | て | hand |
| 00574_atama | 頭 | あたま | head |
| 00575_ashi | 足 | あし | foot, leg |
| 00576_ocha | お茶 | おちゃ | tea |
| 00577_byouin | 病院 | びょういん | hospital |
| 00578_ginkou | 銀行 | ぎんこう | bank |
| 00580_toshokan | 図書館 | としょかん | library |
| 00581_kouen | 公園 | こうえん | park |
| 00582_tenki | 天気 | てんき | weather |
| 00583_ame | 雨 | あめ | rain |
| 00607_kaisha | 会社 | かいしゃ | company |
| 00608_daigaku | 大学 | だいがく | university |
| 00609_sensei | 先生 | せんせい | teacher |
| 00610_gakusei | 学生 | がくせい | student |
| 00611_okane | お金 | おかね | money |
| 00612_ie | 家 | いえ | house, home |
| 00614_nihongo | 日本語 | にほんご | Japanese language |
| 00682_ue | 上 | うえ | above, up |
| 00670_shita | 下 | した | below, down |
| 00710_kaigi | 会議 | かいぎ | meeting |
| 00762_tsukue | 机 | つくえ | desk |
| 00888_shitsumon | 質問 | しつもん | question |
| 02180_eki | 駅 | えき | station |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00593_totemo | とても | とても | very |
| 00595_chotto | ちょっと | ちょっと | a little |
| 00596_sukoshi | 少し | すこし | a little, few |
| 00597_takusan | たくさん | たくさん | a lot, many |
| 00598_zenzen | 全然 | ぜんぜん | not at all |
| 00599_itsumo | いつも | いつも | always |
| 00600_tokidoki | 時々 | ときどき | sometimes |
| 00601_yoku | よく | よく | often, well |
| 00602_mou | もう | もう | already |
| 00603_mada | まだ | まだ | still, not yet |
| 00604_amari | あまり | あまり | not very |
| 00814_sugu | すぐ | すぐ | immediately |
| 00881_saikin | 最近 | さいきん | recently, lately |
| 01284_moshi | もし | もし | if, in case |
These are frequently encountered words not in the categories above:
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00164_kamoshirenai | かもしれない | かもしれない | might, perhaps |
| 00554_kodomo | 子供 | こども | child |
| 00673_sumu | 住む | すむ | to live, reside |
| 00755_shizuka | 静か | しずか | quiet, calm |
| 00791_kowareru | 壊れる | こわれる | to break |
| 00823_wakai | 若い | わかい | young |
| 00846_hitsuyou | 必要 | ひつよう | necessary |
| 01098_ireru | 入れる | いれる | to put in |
| 01127_ooi | 多い | おおい | many, much |
| 01137_tokoro | ところ | ところ | place, point |
| 01165_mieru | 見える | みえる | to be visible |
| 01179_keiken | 経験 | けいけん | experience |
| 01430_juusho | 住所 | じゅうしょ | address |
| 01458_tatemono | 建物 | たてもの | building |
| 01636_shippai | 失敗 | しっぱい | failure |
| 01932_kiku | 効く | きく | to be effective |
| 01970_naru | なる | なる | to become |
| 02355_suki | 好き | すき | like, fond of |
| 02444_hiku | 弾く | ひく | to play (instrument) |
| 02514_kekkon | 結婚 | けっこん | marriage |
| 02848_issho | 一緒 | いっしょ | together |
| 02899_kudasai | ください | ください | please (do) |
| 03036_machigau | 間違う | まちがう | to make a mistake |
| 03093_dake | だけ | だけ | only, just |
| 03290_seichou | 成長 | せいちょう | growth |
| 03823_senshu | 選手 | せんしゅ | player, athlete |
| Entry ID | Word | Reading | Gloss |
|---|---|---|---|
| 00031_daga | だが | だが | but, however |
| 00033_dakedo | だけど | だけど | but, however |
| 00379_sokode | そこで | そこで | so, therefore |
| 00382_soredemo | それでも | それでも | but still |
A pre-built lookup table is available at build/word_id_lookup.json for fast word→ID resolution. It is regenerated automatically by make index (via update_indexes.py).
The table has two indexes:
by_reading — maps hiragana readings to entries (primary lookup method)by_headword — maps kanji/surface forms to entriesEach match includes id, headword/reading, gloss, and tier for disambiguation. For homophones (e.g., きく → 聞く/効く), check the gloss to select the correct entry.
# Example: look up a word by reading
python3 -c "
import json
with open('build/word_id_lookup.json') as f:
data = json.load(f)
for e in data['by_reading'].get('きく', []):
print(f\"{e['id']}: {e['headword']} - {e['gloss']}\")
"
python3 build/validate.py 2>&1 | grep -A5 "Word link"
noentry WordsWhen you mark a word with noentry, consider tracking it for potential future entry creation. In your session log, include:
### Words marked noentry (candidates for future entries)
- ランナー (runner)
- セーフ (safe - sports)
- 丁目 (district number)
This helps identify gaps in the dictionary that may need filling.
Before finalizing links: