study_skills v1.0.0

name: "study_skills" description: "Designs evidence-based study plans from cognitive-science research: spaced-repetition schedules, active-recall practice, and interleaved problem sets. Triggers when someone asks for help studying, preparing for an exam or certification, learning a subject deeply, or building a review system — e.g. 'help me study for my physics final', 'make me a study plan', 'I keep forgetting what I learn'." metadata: { "includeInPrompt": true }

Study Skills

Purpose

Turn any learning goal into a study plan that uses the techniques cognitive science actually supports: retrieval practice (test yourself, don't reread), spaced repetition (spread sessions over time with widening gaps), and interleaved practice (mix topics instead of drilling one block at a time). Retire the low-utility habits — rereading, highlighting, cramming — that research shows barely move the needle.

Workflow

1. Get the target. Ask: what material, what date does it need to be retained until (exam date, or "forever" for durable learning), and how many sessions per week are realistic.
2. Generate the schedule. Run bin/study_schedule.py --exam YYYY-MM-DD --topics "t1,t2,t3" --reviews N for an exam-dated plan, or adapt the worked templates in references/weekly-plans.md for open-ended learning. Inter-study gaps default to ~15% of the retention interval (the Cepeda et al. 2008 optimum).
3. Build active-recall materials. For each topic, produce 5–15 questions the learner answers from memory before checking the source: definitions, worked-problem prompts ("solve this without notes"), compare/contrast ("when do you use X vs Y?"), and "why" questions (elaborative interrogation). Blank-page recall first, source-check second.
4. Interleave, don't block. Practice sessions mix problem types so every item forces strategy choice — the learner must decide which method applies, not just execute the one from today's lesson. Within a session, no two consecutive items should be the same topic.
5. Enforce the review discipline. Each review is pass/fail: cold recall → check → re-test misses. If a topic fails recall, its next review resets to 1 day out (SM-2 quality < 3 rule). Do not drop a topic after one successful recall — that is exactly when people stop and exactly when retention collapses (Karpicke 2009).
6. Warn off the traps. Rereading and highlighting feel productive but are rated low utility (Dunlosky et al. 2013); extra same-session problems after mastery (overlearning) add almost nothing (Rohrer & Taylor 2007). Cut them from the plan and say why.

Output Contract

A finished plan always contains:

• Dated sessions (from study_schedule.py or a worked template) covering every topic
• For each topic: an initial-learn session ("self-test until one correct recall") plus ≥3 spaced review sessions
• A question bank of active-recall prompts per topic
• Sessions labeled by method (cold recall → check → re-test), not just by topic
• A one-paragraph "why this works" note citing the research basis, not vibes

Operating Rules

1. Accuracy rule: every factual claim in a plan must trace to a verified source (see references/research-brief.md). Never invent statistics, dates, or quotes. Unverifiable = cut it or frame as opinion.
2. Exam date is required for dated plans. Without one, schedule by "retain forever" defaults: reviews at ~1 day, ~1 week, ~1 month.
3. One topic = un-interleavable. If the user gives a single topic, split it into sub-skills (definitions, worked problems, edge cases) so sessions still force discrimination.
4. Keep plans honest about load: a plan the learner won't do is worse than a smaller one they will. Prefer fewer sessions spaced correctly over many sessions crammed.
5. Never present rereading/highlighting/cramming as effective techniques, even if the user asks for them — explain what to do instead.