agentleFS
Sophon-KrSophon-Kr/thai-native/skills/thai-config/SKILL.md

thai-config

Skill0 starsChanged 22 days agoMITView on GitHub

Show or change thai-native settings, such as default register, your particle (ครับ/ค่ะ), tech-term policy, numerals, slang, lint hooks and per-term overrides. Saves to your user config or to the project's .thai-native/config.json for the whole team.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Add --project to save in .thai-native/config.json at the project root (commit it to share with the team).

    line 35

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

thai-config

Script: python3 "${CLAUDE_SKILL_DIR}/../thai-writing/scripts/thai_native.py" --project-dir "${CLAUDE_PROJECT_DIR}" config

  • With arguments ($ARGUMENTS): run the script with them and report the result in one or two Thai lines.
  • Without arguments: run config show, then offer a quick setup. Ask, in one round, (1) particle: ครับ / ค่ะ / ไม่ใช้คำลงท้าย / ดูจากข้อความ, (2) default register: auto / team / business / formal / casual / social, (3) tech terms: ตามคลังคำ (auto) / คงอังกฤษ / ทับศัพท์ / ไทย (อังกฤษ) / แปลไทย, (4) lint after writing files: warn / off. Save all answers with one command, config set particle=… register=… term_policy=… file_hook=…, and show the final config. Until these four are set, the plugin's prompt hook asks the same questions once per session before a Thai task (setup_prompt off stops it).
keyvaluesmeaning
registerauto, formal, business, team, casual, socialdefault register (auto = by genre)
particleauto, krap, ka, noneครับ / ค่ะ / none; auto mirrors the user
term_policyauto, keep_en, translit, th_en, translateoverride every glossary term at once
numeralsarabic, thaidigits
yearauto, be, ceพ.ศ. or ค.ศ. in dates
slangoff, light, onslang in casual/social registers
emojiauto, off, onemoji in chat
self_checkauto, lint, checklistwhen to run the lint
file_hookwarn, offlint Markdown/text files after Write/Edit
prompt_hookauto, always, offadd how the glossary writes the tech terms in your message (auto = only when the message is about Thai)
prompt_cardsoff, onalso push short reference cards for the task with the glossary note (genre rules once per session, register, translation patterns). Off by default: in the 0.2.0 A/B, answers with pushed cards lost more often than without
stop_hookauto, offif Claude's final reply has a certain Thai error (ครับ/ค่ะ with a slash, misspelled particle), ask once for a corrected reply
setup_promptauto, offuntil particle, register, term_policy and file_hook are set, ask for them once per session before a Thai task
post_hookoff, warn, block-oncelint Thai text before Jira/Confluence/Slack/Linear/Notion MCP tools post it
learnoff, onthai-learn: keep local copies of Thai files Claude writes so it can learn from your edits (user config only; a project file cannot turn it on)
term"<term> <policy> [thai]"personal override for one term

Add --project to save in .thai-native/config.json at the project root (commit it to share with the team). Claude Code users can also set register, particle, term policy and the file hook in /plugin configure thai-native@thai-native; the config file wins when both are set.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.