Skill: theshimaw-svg/200iq-ai-agent (version 1.0.0)

# 200IQ AI Agent — Genius Mode for Your AI Turn your AI agent into a 200 IQ genius! Solve complex problems, plan optimally, and execute tasks like a $10M founder combined with a master strategist. ## Features - Decision engine evaluates tasks by ROI and efficiency - High-IQ prompt optimizer for smarter outputs - Self-correcting learning loop - Execution delegation to maximize speed and success - Action plans with measurable results ## Activation Say: - "Activate 200IQ AI Agent" - "Make my AI 200 IQ for this task: [task]" - "Generate a high-IQ action plan for [goal]" ## Usage 1. AI generates optimized prompts and action plan 2. Evaluates which actions produce measurable results 3. Executes tasks with ROI threshold 4. Logs everything and self-corrects 5. Produces outputs the user can implement immediately

path: prompt_optimizer.py
content: from logger import log_journal

def generate_high_iq_prompt(task_description, user_goal):
    """
    Returns a smarter, first-principles optimized prompt
    """
    prompt = (
        f"Think like a 200 IQ strategist: Solve '{task_description}' "
        f"to achieve '{user_goal}' using first principles. "
        f"Provide step-by-step reasoning and actionable steps."
    )
    log_journal(f"Generated high-IQ prompt for task '{task_description}'")
    return prompt


path: roi_monitor.py
content: def evaluate_roi(task):
    """
    Expected ROI: expected_gain / cost
    """
    if task.get('cost', 0) <= 0:
        return float('inf')
    return task.get('expected_gain', 0) / task['cost']

def filter_high_roi_tasks(tasks, threshold=1.0):
    return [t for t in tasks if evaluate_roi(t) >= threshold]


path: prompt_templates.json
content: task_optimizer: Generate 200 IQ optimized steps for {{task_name}} to achieve {{user_goal}}
roi_filter: Only execute tasks where expected_gain/cost >= {{roi_threshold}}
action_plan_generator: Create actionable, high-IQ plan for {{task_name}}

path: optimized_prompt.txt
content: Solve "{{task_description}}" with 200 IQ reasoning.
- Break the problem into first-principles
- Prioritize actions by ROI
- Provide step-by-step actionable instructions


path: execution_agent.py
content: from roi_monitor import evaluate_roi
from logger import log_journal

def execute_task(task):
    roi = evaluate_roi(task)
    if roi < 1:
        log_journal(f"Skipped '{task['name']}' due to low ROI ({roi})")
        return f"Skipped {task['name']}"
    # Simulate task execution (replace with actual API/tool calls)
    result = f"Executed {task['name']} with ROI {roi:.2f}"
    log_journal(result)
    return result

def batch_execute(tasks):
    results = [execute_task(task) for task in tasks]
    return results


path: decision_engine.py
content: from roi_monitor import evaluate_roi
from logger import log_journal

def rank_tasks(tasks, min_roi=1.0):
    """Ranks tasks based on expected ROI"""
    ranked = sorted(tasks, key=lambda t: evaluate_roi(t), reverse=True)
    high_roi = [t for t in ranked if evaluate_roi(t) >= min_roi]
    log_journal(f"Ranked tasks: {[t['name'] for t in high_roi]}")
    return high_roi


path: task_output_template.txt
content: task_optimizer: Generate 200 IQ optimized steps for {{task_name}} to achieve {{user_goal}}
roi_filter: Only execute tasks where expected_gain/cost >= {{roi_threshold}}
action_plan_generator: Create actionable, high-IQ plan for {{task_name}}

path: logger.py
content: from datetime import datetime
import json

def log_journal(entry, path="../logs/journal.log"):
    with open(path, "a") as f:
        f.write(f"[{datetime.now()}] {entry}\n")

def log_actions(actions, path="../logs/actions.json"):
    with open(path, "w") as f:
        json.dump(actions, f, indent=2)


path: action_plan.txt
content: 200IQ Action Plan for {{task_name}}:

1. Optimized Steps:
{% for step in steps %}
- {{step}}
{% endfor %}

2. KPIs:
- Expected Gain: {{expected_gain}}
- Cost: {{cost}}
- Expected ROI: {{roi}}

3. Execution Notes:
- Use AI prompts from optimized_prompt.txt
- Delegate sub-tasks to tools/agents


path: genuis_rules.json
content: first_principles: yes
80_20_focus: yes
risk_management: yes
maximize_output_per_cost: yes

path: self_correction.py
content: from logger import log_journal

def self_correct(task_results):
    lessons = []
    for r in task_results:
        if "Skipped" in r:
            lessons.append("Task skipped: consider lowering cost or increasing expected gain")
        elif "Executed" in r:
            lessons.append("Task executed successfully: continue strategy")
    log_journal("Lessons Learned: " + "; ".join(lessons))
    return lessons


path: roi_thresholds.json
content: default: 1
high_risk: 2
low_cost: 0.5

path: action_priorities.json
content: critical: 1
high: 2
medium: 3
low: 4

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)