tutor

name	tutor
description	Drill your recorded pains (friction phrasings, grammar fixes, irregular verbs, learned vocab) with spaced repetition. Usage: /tutor [size]
disable-model-invocation	true
allowed-tools	Bash(python3 */tutor.py*) Bash(python3 */config.py*)

You run an interactive tutoring session IN THIS conversation (you deal a card, the user answers, you judge, repeat). This skill's entrypoint is ${CLAUDE_SKILL_DIR}/tutor.py (in this skill dir); the shared config.py is at ${CLAUDE_PLUGIN_ROOT}/config.py. Invoke each directly so the command starts with python3.

Steps:

1. Run python3 "${CLAUDE_PLUGIN_ROOT}/config.py" show (it prints <config><row><first_language>…</first_language><learning_language>…</learning_language><explanation_language>…</explanation_language></row></config>). Write all feedback in explanation_language.
2. Run python3 "${CLAUDE_SKILL_DIR}/tutor.py" deck (add --size <N> if the user passed a number). It prints <deck><row>…one card per <row>…</row></deck>. If it prints <deck></deck> (empty): say there is nothing due and nothing new — come back after a /debrief — and STOP.
3. For EACH card, one at a time: print the exercise (number it [i/N]), WAIT for the user's reply, judge it, then record with ONE call. Put the user's reply between the <answer> tags VERBATIM (do not re-quote, trim, or fix it; never escape anything — the quoted <<'SL_IN' passes every char literally). The body and the closing SL_IN MUST start at column 0:

python3 "${CLAUDE_SKILL_DIR}/tutor.py" record <item_kind> <item_key> <exercise> <verdict> <<'SL_IN'
<answer>
their reply, verbatim
</answer>
SL_IN

The call prints <result><row><box>N</box></row></result> (the card's new Leitner box). Exercises and verdicts:

• friction/production — ask: how would you say (learner_wrote, mention the zone)? PASS = conveys the meaning of native_phrase in a natural register (word-for-word identity NOT required); PARTIAL = understandable but unnatural; FAIL = bailed, empty, or wrong meaning. Always show native_phrase after judging.
• grammar/fix — ask to correct original. PASS = the error class from rule is fixed (other phrasing variance is fine); PARTIAL = fixed but a new error introduced; FAIL = the error remains. Show fixed + rule.
• verbs/forms — if the card has a context excerpt, show it as the setup, then ask for the past + past participle of item_key. PASS = both exact; PARTIAL = one correct. Show the forms (and correction) from the card.
• vocab/reverse — ask: what is the learning-language word for translation? PASS = the word; PARTIAL = minor misspelling. Show the word. (On FAIL the script auto-resets the word into glossing — mention it relearns.) Give ONE line of feedback per card, then move on. If the user asks to stop, stop immediately (skip the remaining cards, no records for unseen cards).

4. After the last card: print a summary — pass/partial/fail counts, items that fell back to box 1, then run python3 "${CLAUDE_SKILL_DIR}/tutor.py" stats (it prints <stats><row><due_today>…</due_today><due_tomorrow>…</due_tomorrow><tracked>…</tracked></row></stats>) and report due_today / due_tomorrow. Nothing else.