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Study Coach · the `/ask-coach` command

ai-study-kit has many features — quiz app, courses, flashcards, wrong-question grilling, podcasts, deployment — which itself becomes a burden for a learner: what exactly should I do today? /ask-coach answers that. It’s the repo’s built-in main-entry skill: install it once, start every study session from it, and let it scan your state, recommend, and execute with you — no need to memorize the toolchain.

The command names are the menu — the ai-study-kit plugin (name is permanent) installs four commands:

Command What it does
/ask-coach Ask the coach: state snapshot + recommendation + guided execution (main entry; everything else routes from here)
/coach Sit down and study: direct tutoring entry (F10 open/resume; opening reports “what to practice today + why”)
/study-doctor One-stop health check: four quality gates + environment probes, pass/fail report + fix order
/study-recap Direct wrong-question deep-dive entry (F4, once prerequisites check out)

The skill sources live in the repo under skills/ (single source of truth: the ask-coach main entry + three thin commands coach / study-doctor / study-recap that share the main entry’s references/). Two install paths:

① Plugin marketplace (zcode / Claude Code, recommended): the repo ships its own marketplace manifest (.claude-plugin/marketplace.json; scripts/sync-plugin.mjs generates plugins/ai-study-kit/ from the source). Add the marketplace https://github.com/jerryjiao/ai-study-kit in your client and install the ai-study-kit plugin — skill updates arrive with marketplace refreshes, no manual reinstall (versions follow repo releases). After a plugin update, opening /ask-coach in an older project reports the version gap and guides you through the F13 upgrade (data-safe, fills the gaps — see the flow table; the kit snapshot self-reports its version via kit-version.json). The plugin name is ai-study-kit for life; the commands are the ask-coach family (renamed from /ai-study-kit in v0.13, Sept 2026 — marketplace names are permanent, so the plugin name stays).

② Manual install (any client honoring ~/.agents/skills/):

Terminal window
# from the ai-study-kit repo root (installs all four skills; thin commands rely on the main entry's references/)
pnpm run skill:install # copies to ~/.agents/skills/{ask-coach,coach,study-doctor,study-recap}
pnpm run skill:install -- --link # symlink variant (auto-updates with git pull)
# other clients: custom destination
bash scripts/install-skill.sh --dest ~/.claude/skills
# uninstall
pnpm run skill:uninstall

After installing, restart the CLI (or open a new session) and type /ask-coach. It also works uninstalled: just tell your agent to read skills/ask-coach/SKILL.md and follow it.


Every invocation runs the same three steps:

  1. Scan state (read-only, ≤1 min) — theme, question/card/course/deep-dive inventory, answering progress, ungraduated wrong questions, due flashcards, lessons completed, weak oral-recitation targets (derived from the attempts ledger), tutoring sessions and exam deadline, AI config, backend online or not, kit version drift (your project vs the plugin snapshot — lagging or unknown version leads to the F13 upgrade, see below); with a knowledge graph location provided it also carries graph signals (per-node mastery four-states, prerequisite relations — see F12).
  2. Report + recommend — one snapshot table + one recommended action with a reason + a numbered menu.
  3. Execute with you — once you pick, it follows the playbook in skills/ask-coach/references/flows.md step by step, then checks the “done” criteria.

Without an explicit intent, the recommendation takes the first hit in order (full version in skills/ask-coach/SKILL.md). The top three study entries run “flashcards → sprint → resume tutoring”: reviews are debt that accrues daily, the sprint is the harvest window within a week of the exam, and tutoring can resume anytime (version drift sits ahead of the study entries — align the feature layer first; your data is never at risk):

Order Condition Recommendation
1 Repo doesn’t exist F1 bootstrap the project (get the quiz app running first)
2 Project kit version lagging or unknown F13 upgrade (align the feature layer first — new features are silently degraded while drifting; data-safe, a few minutes)
3 Active theme is the dev-intro demo and you have your own topic F2 new theme (the demo’s git/Linux questions aren’t your study material)
4 Due flashcards > 0 F3 daily study (clear reviews first — memory is decaying; new knowledge can wait)
5 ≤ 7 days to the MISSION.md deadline F11 pre-deadline sprint (the short-window intensive-repetition window is open; no deadline configured → this row never matches and the snapshot shows ⚠)
6 Tutoring session in progress F10 coached tutoring, resume (report session name + open todo count, runs only with your nod: resuming is a suggestion, not an order)
7 Ungraduated wrong questions ≥ 3 F4 wrong-question grilling (LLM-clustered deep-dive)
8 Unanswered questions & lessons not done F3 daily study (build concepts before drilling — read the day’s lesson; a lesson counts only after you click “✓ done”, opening doesn’t count)
9 Unanswered questions & lessons done F3 daily study (concepts are in place, drill to validate)
10 All questions answered & accuracy ≥ 80% F5 make a podcast (passive consolidation) or F2 new theme
11 All questions answered & accuracy < 80% F4 grilling; still short of the bar → F6 patch the course (lesson quality isn’t enough)
# Flow When Key commands
F1 Bootstrap Get the demo running from zero pnpm install && pnpm dev
F2 New theme Turn what you want to learn into a full loop syllabus + exam-point table → materials → teach-generate → author questions/cards per table → switch theme → verify
F3 Daily study “What do I study today” due flashcards → read lessons → drill → redo wrong
F4 Wrong-question grilling ≥3 wrong questions piled up pnpm run ai:grill -- --theme <t>
F5 Make a podcast Commute/workout consolidation pnpm run ai:podcast -- --input <file>
F6 Generate/extend course Add lesson explanations pnpm run ai:teach -- --theme <t>
F7 Edit content Change questions/lessons/cards/schedule four-alignment chain + checks
F8 Verify & release Pre-release quality gate pnpm run scan / test / build + scripts/bidirectional-check.py
F9 Deploy Put it on a cloud server pm2 (start from apps/quiz-app/)
F10 Coached tutoring Teach each exam point through dialogue + quiz on the spot + resume across days minimal exam-point set from the table → three-part explanation + anchor phrase → quiz by mode → persist per point into study/records/ (oral Q&As go into the oral-attempts.json ledger) → hand over to F3
F11 Pre-deadline sprint ≤ 7 days to the exam, or you say “sprint / pre-exam / cram” harvest records phrases + wrong-question archives → four-piece sprint package + print version into study/sprint/ → hand over to F3 mock exam
F12 Knowledge-graph projection You have a knowflow knowledge base (graph.json) and want mastery coloring and exam-point edges visible on the graph build/confirm the exam-point↔node mapping (study/records/graph-map.json, proposed by the agent, confirmed by you item by item) → pnpm run mastery -- --graph <graph.json> --write-projection writes the read-only projection; no graph / no mapping degrades silently and knowledge pages are never written back
F13 Upgrade The plugin updated and your project lags behind (version drift / unknown version) back up progress → re-copy the kit (progress preserved) → fill in missing file templates → walk each contract gap (exam-point table / examPoint tags / card mapping — guided, never ghost-written) → close with the health check

Plus two ops entries: health check (/study-doctor — one-stop orchestration of the four quality gates + environment probes, with a pass/fail report and fix order) and diagnostics (progress not syncing, course 404, CLI config errors, scan hits… a symptom → root cause → action lookup table).


  • A routing skill, not another CLI: it introduces no new runtime — it encodes “read state → recommend → run existing commands/flows” as agent-followable instructions. All underlying capabilities already exist in the repo (three AI CLIs, sync scripts, verification gates).
  • State before advice: the coach is forbidden from recommending on vibes — every snapshot field has a probe command (skills/ask-coach/references/state.md), and progress statistics match apps/quiz-app/src/lib/progress.ts exactly (tombstone filtering, random-sandbox exclusion, wrong-graduation thresholds, SRS due).
  • Methodology embedded: the recommendation order is methodology.en.md “syllabus → materials → quizzes” made executable; the F2 flow forces MISSION (with the exam-point table) / RESOURCES before any course or question generation — authoring isn’t free-form JSON writing, it’s point-by-point production against the table, closed out by three green gates (qa / scan / four-alignment).

To add a flow: add a playbook section (purpose / prerequisites / steps / done criteria) in skills/ask-coach/references/flows.md, plus a row in SKILL.md’s menu and intent-routing table. Then run pnpm run sync:plugin to regenerate the plugin artifacts (manual-install users additionally rerun pnpm run skill:install). To add a thin command: create a new directory under skills/ with a thin SKILL.md (~15 lines, sharing ../ask-coach/references/) — sync-plugin picks it up automatically.

Q: Is installing it mandatory? A: No — but then you have to figure out “what next” yourself every time. Installed, it’s a one-sentence entry point.

Q: Will it touch my data? A: Steps 1/2 are strictly read-only. Step 3 writes files / runs commands only for the flow you pick, and the playbooks flag the red lines (sync artifacts are hand-edit-forbidden, progress files are hand-edit-forbidden).

Q: Does it survive switching AI CLIs? A: Yes. The skill is markdown instructions + reference docs; any client supporting the skills-directory convention can install it (--dest to point it there).