Plan Generator

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

Validation Shortcut

Run this minimal command first to verify the supported execution path:

python scripts/plan_generator.py --help

When to Use

• You need a final exam review plan across a specific start/end date range.
• You need a lab experiment schedule that allocates tasks by duration within a time window.
• You want to generate a calendar-style day-by-day plan and export it as Markdown.
• You need to account for task dependencies (e.g., Experiment B after Experiment A).
• You need to consider resource constraints for lab work (e.g., shared instruments).

Key Features

• Supports two plan types:
  • Review plan (course/exam-oriented)
  • Lab schedule (task/dependency/resource-oriented)
• Two input modes:
  • Interactive step-by-step prompts
  • One-time JSON submission
• Produces a Markdown output containing:
  • Plan summary
  • Day-by-day schedule
  • Task/item list
• Offline and local-only execution:
  • No network access
  • Reads only a user-specified JSON file (if provided)
  • Writes output to the current working directory

Dependencies

• Python 3.x
• Python Standard Library only (no third-party packages)

Example Usage

1) Interactive mode

python scripts/plan_generator.py

Follow the prompts to provide:

• plan_type (review or lab)
• start_date, end_date (YYYY-MM-DD)
• items (tasks/courses/experiments)
• daily_hours (available hours per day; may differ for weekdays vs weekends)

2) One-time JSON input mode

Create an input file (e.g., input.json) and run:

python scripts/plan_generator.py --json input.json

Example: Review plan JSON

{
  "plan_type": "review",
  "start_date": "2026-06-01",
  "end_date": "2026-06-14",
  "daily_hours": {
    "weekday": 3,
    "weekend": 5
  },
  "items": [
    {
      "name": "Linear Algebra",
      "exam_date": "2026-06-15",
      "importance": 1,
      "topics": ["Vectors", "Matrices", "Eigenvalues"]
    },
    {
      "name": "Operating Systems",
      "exam_date": "2026-06-18",
      "importance": 2,
      "topics": ["Processes", "Scheduling", "Memory"]
    }
  ]
}

Example: Lab schedule JSON

{
  "plan_type": "lab",
  "start_date": "2026-03-01",
  "end_date": "2026-03-07",
  "daily_hours": {
    "weekday": 6,
    "weekend": 4
  },
  "items": [
    {
      "name": "Experiment A",
      "duration_hours": 6,
      "dependencies": [],
      "resources": ["Centrifuge"]
    },
    {
      "name": "Experiment B",
      "duration_hours": 4,
      "dependencies": ["Experiment A"],
      "resources": ["PCR Machine"]
    }
  ]
}

Implementation Details

• Plan types

  • review: Items represent courses/exams. Each item may include:
    • exam_date (YYYY-MM-DD)
    • importance (integer priority/weight)
    • topics (list of strings)
  • lab: Items represent experiments/tasks. Each item may include:
    • duration_hours (numeric)
    • dependencies (list of prerequisite item names)
    • resources (list of required instruments/resources)

• Scheduling window

  • The schedule is generated only within [start_date, end_date] (inclusive).
  • Daily capacity is derived from daily_hours (e.g., weekday vs weekend).

• Constraints and assumptions

  • Lab items may be ordered/placed to respect dependencies (a dependent task should not be scheduled before its prerequisites).
  • Resource fields are included to support resource-aware planning; the schedule output records resource needs alongside tasks.

• I/O and safety

  • The script does not access the network.
  • It reads only the JSON file path explicitly provided by the user (when using --json).
  • It writes the generated Markdown plan to the current directory.
  • It does not store or emit sensitive personal data beyond what the user provides in the input.

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

• A clearly specified task goal aligned with the documented scope.
• All required files, identifiers, parameters, or environment variables before execution.
• Any domain constraints, formatting requirements, and expected output destination if applicable.