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name: learning-tutoring description: Learning tutoring planning and content production skill for creating study plans, generating exercises, writing answer explanations, and providing review/adjustment guidance; triggered by requests like “study plan”, “exercise set/question bank”, “answer analysis”, “error analysis”, “exam prep plan”, or “spaced/periodic review schedule”. license: MIT author: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Learning Tutoring Skills

When to Use

• Use this skill when you need learning tutoring planning and content production skill for creating study plans, generating exercises, writing answer explanations, and providing review/adjustment guidance; triggered by requests like “study plan”, “exercise set/question bank”, “answer analysis”, “error analysis”, “exam prep plan”, or “spaced/periodic review schedule” in a reproducible workflow.
• Use this skill when a others task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when scripts/build_tutoring_pack.py is the most direct path to complete the request.
• Use this skill when you need the learning-tutoring package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Learning tutoring planning and content production skill for creating study plans, generating exercises, writing answer explanations, and providing review/adjustment guidance; triggered by requests like “study plan”, “exercise set/question bank”, “answer analysis”, “error analysis”, “exam prep plan”, or “spaced/periodic review schedule”.
• Packaged executable path(s): scripts/build_tutoring_pack.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Others/learning-tutoring"
python -m py_compile scripts/build_tutoring_pack.py
python scripts/build_tutoring_pack.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/build_tutoring_pack.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/build_tutoring_pack.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

1. When to Use

Use this skill when the user needs end-to-end learning support, especially in these scenarios:

1. Study plan creation: The user asks for a multi-week/phase plan with clear weekly tasks and milestones.
2. Exercise/question bank generation: The user requests practice questions by topic, difficulty, and question type.
3. Answer explanations and solution walkthroughs: The user wants step-by-step reasoning, not just final answers.
4. Error analysis and remediation: The user provides mistakes (or weak areas) and asks for diagnosis plus targeted practice.
5. Exam preparation planning: The user has an exam date/format and needs a structured prep + review schedule.

2. Key Features

• Learner profiling: Identify subject scope, current level, constraints (time), goals, and preferences.
• Structured study plans: Produce phase/week plans with explicit tasks, deliverables, and milestones.
• Exercise generation: Create questions across difficulty levels and key knowledge points.
• High-quality explanations: Provide correct logic, common pitfalls, and transfer tips (how to generalize).
• Review and adjustment guidance: Recommend periodic review, spaced repetition, and plan updates based on performance.
• Batch/structured output support: When needed, generate structured results (e.g., JSON) via scripts, then render to readable English.

3. Dependencies

• Python 3.10+
• (Optional) Local script runner environment capable of executing:
  • scripts/build_tutoring_pack.py

4. Example Usage

Example Request

Create a 6-week study plan for high-school algebra (functions). I can study 6 hours/week. Goal: score 85+ on a school exam in 7 weeks. I prefer practice questions and concise notes.

Example Output (Runnable Template)

Learner Profile

• Subject/Scope: High-school Algebra — Functions (domain/range, transformations, composition, inverse, graph interpretation)
• Current Level: Basic familiarity; inconsistent accuracy on word problems and graph questions
• Time Budget: 6 hours/week (4 sessions × 90 minutes)
• Goal: 85+ on exam in 7 weeks
• Preferred Style: Practice-heavy + concise notes

Goals

• Build reliable mastery of core function concepts and common exam patterns.
• Improve accuracy under time constraints via mixed sets and error-driven review.

6-Week Plan (Weekly Focus → Tasks → Deliverables)

Week 1 — Foundations & Graph Reading

• Tasks:
  • Create a 1-page “Function Basics” sheet (definition, notation, domain/range).
  • Practice: evaluate functions, interpret graphs/tables.
• Deliverables:
  • 30 questions (easy→medium), error log with categories.

Week 2 — Transformations

• Tasks:
  • Notes: vertical/horizontal shifts, stretches/compressions, reflections.
  • Practice: match equations to graphs; graph from equation.
• Deliverables:
  • 35 questions + 5 “explain the transformation” short answers.

Week 3 — Composition & Inverse

• Tasks:
  • Notes: composition rules, inverse existence conditions, verifying inverses.
  • Practice: compute compositions; find inverses; domain restrictions.
• Deliverables:
  • 30 questions + 10 targeted inverse checks.

Week 4 — Word Problems & Modeling

• Tasks:
  • Translate contexts into functions; interpret parameters.
  • Mixed practice with graph + context.
• Deliverables:
  • 25 application problems + written explanations for 5.

Week 5 — Mixed Sets + Timed Practice

• Tasks:
  • 2 timed sets (exam-like), then deep review.
  • Build a “Top 10 Mistakes” list from the error log.
• Deliverables:
  • 2 timed sets + corrected solutions + mistake list.

Week 6 — Final Consolidation & Spaced Review

• Tasks:
  • Spaced review sessions: revisit weakest 3 topics.
  • 1 full mock + targeted drills.
• Deliverables:
  • Mock exam report (score, weak points, next actions).

Milestones

• End of Week 2: ≥80% accuracy on transformation questions.
• End of Week 4: Can solve modeling problems with a consistent setup method.
• End of Week 6: Mock exam score ≥85 with stable timing.

Exercises (Sample Set with Required Fields)

1. Type: choice
   Stem: If ( f(x)=2x-3 ), what is ( f(5) )?
   Answer: 7
   Explanation: Substitute (x=5): (2(5)-3=10-3=7).
   Difficulty: easy
   Tags: [evaluation, function-notation]

2. Type: short_answer
   Stem: Describe the transformation from ( y=x^2 ) to ( y=(x-2)^2+3 ).
   Answer: Shift right 2, up 3
   Explanation: (x-2) shifts right; (+3) shifts up.
   Common Pitfall: Confusing (x-2) with left shift.
   Transfer Tip: For (y=f(x-h)+k), shift right by (h), up by (k).
   Difficulty: medium
   Tags: [transformations, quadratics]

3. Type: application
   Stem: A taxi charges a base fee of $4 plus $1.5 per mile. Write a function for cost (C(m)) and find (C(10)).
   Answer: (C(m)=4+1.5m), (C(10)=19)
   Explanation: Base fee is the intercept; per-mile rate is the slope. (4+1.5(10)=19).
   Common Pitfall: Swapping base fee and rate.
   Transfer Tip: Linear models often follow “fixed + variable × quantity”.
   Difficulty: medium
   Tags: [modeling, linear-functions]

Review & Adjustment Suggestions

• Maintain an error log with categories (concept, algebra slip, misread graph, time pressure).
• Use spaced review: revisit weak topics after 1 day, 3 days, 7 days.
• If Week 3 accuracy on inverses <70%, reduce new content in Week 4 and add 2 extra inverse drill sessions.

5. Implementation Details

Core Workflow (Execute as Needed)

1. Clarify goals and learner profile
2. Produce a phased/weekly study plan
3. Generate exercises aligned to weekly focus, key points, and difficulty distribution
4. Write answer explanations including:
   • Correct logic (step-by-step where needed)
   • Common pitfalls (typical mistakes)
   • Transfer tips (generalization patterns)
5. Provide review and adjustment guidance based on milestones and observed errors

Required Clarifying Questions (Ask if Missing)

• Subject and topic scope (required)
• Current level/foundation (required)
• Learning cycle and weekly time budget (required)
• Goal and exam format/date (if applicable)
• Preferred learning style (notes, projects, practice-heavy, etc.)

Output Specifications

Study Plan Format

• Use a clear hierarchy:
  • Learner Profile → Goals → Weekly/Phase Plan → Milestones → Review Suggestions
• Weekly tasks must be specific and measurable (avoid vague wording like “study more”).
• Write in English; keep technical terms in their original form when appropriate.

Exercises & Explanations Format

• Supported question types: choice / short_answer / application
• Each question includes:
  • stem, answer, explanation, difficulty, knowledge point tags
• Explanations should include:
  • correct logic, common pitfalls, transfer tips
• Quantity and difficulty should be adjusted to the plan, time budget, and goal.

Structured Batch Generation (Preferred for Stable Output)

If consistent batch output is required, generate structured data first (e.g., JSON), then render to readable English:

1. Open and fill in CONFIG in scripts/build_tutoring_pack.py
2. Run:

   python scripts/build_tutoring_pack.py
   

3. Read outputs/tutoring_pack.json and convert it into a readable English deliverable.

Reference Templates

For consistent layout and quality standards, see: references/tutoring_templates.md.