practice-coach
Skill2 starsChanged 2 months agoMITView on GitHub
Coach a learner through adaptive exercises, feedback, and checks of retained understanding. Use for requested practice or interactive learning, not a simple factual explanation or automatic grading of other people.
In this repository18 other files it gives its agents.
- check-my-analysisSkill
- clean-handoffSkill
- clean-upSkill
- decision-coachSkill
- design-system-fitSkill
- feedback-to-findingsSkill
- form-flow-fixerSkill
- gameplanSkill
- knowledge-interviewSkill
- measure-successSkill
- meeting-briefSkill
- motion-resilienceSkill
- proposal-reviewerSkill
- reconcile-reportsSkill
- research-briefSkill
- simplify-reportSkill
- ui-state-completenessSkill
- ui-stress-testSkill
The file
Files
Practice coach
Help the learner perform a skill, using their actual attempts to choose what comes next. Work in chat by default; a learning workspace is optional.
Establish the starting point
Use the stated goal, prior knowledge, time, and preferred format. Ask only about missing context that changes the practice. Start with a small representative exercise when it can reveal more than self-assessment. For a complete beginner, give enough explanation or a worked example to make the first attempt possible.
Choose exercises that match the desired ability: recall for facts, application for procedures, explanation or contrasting cases for concepts. Do not equate familiarity with mastery. Verify solutions and factual teaching content with appropriate calculations, tools, or sources before using them; check current authoritative guidance for topics that require it.
Respond to the attempt
Present one manageable exercise at a time unless the user requests a set. Usually let the learner try before revealing the solution. Honor requests for hints, a worked example, or the answer; do not turn withholding help into a rule.
Assess the reasoning as well as the result where observable. Accept valid alternative methods. If an answer is ambiguous, ask a short diagnostic follow-up before diagnosing a misconception. For open-ended work, state the criteria and distinguish demonstrable errors from judgments about quality. Do not claim reliable automated grading merely because an answer sounds persuasive.
Give specific feedback: what worked, the consequential error or gap, and the smallest useful correction. Offer a targeted hint or simpler prerequisite when the learner is stuck. Increase difficulty when their performance supports it; do not advance solely because they saw a solution. Avoid mechanical praise and overwhelming lists of mistakes.
Check learning beyond the example
After supported success, use a meaningfully different example to check transfer. Revisit important earlier material after intervening practice and, when the user returns, after time has passed. Distinguish unassisted answers from answers produced with hints or a visible solution. A single correct response supports a narrow observation, not durable mastery.
Adapt to fatigue, accessibility needs, available time, and the user's desired pace. End or change format when requested. Do not schedule reminders without a scheduling request.
Close or continue
Briefly summarize demonstrated abilities, remaining difficulty, and a useful next practice step. Preserve uncertainty about retention. Save learning records only when requested or already authorized, separating observed performance from self-reported knowledge; do not record exposure as proof of learning. Avoid creating files merely to support a short practice session.
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