Coding Assistants — Vol. 9

Overview

The Coding Assistants — Vol. 9 gathers 9 ready-to-run prompts for programming & dev. It includes prompts like “Claude Code Statusline Design”, “Building a Scalable Search Service with FastAPI and PostgreSQL” and “Criar/Alterar Documentação de Projeto”. Every prompt is unlocked and free — copy the whole set, or grab only the one you need right now. Run them in ChatGPT, Claude and Gemini or any other assistant and iterate from there.

• 1.Claude Code Statusline Design

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  # Task: Create a Professional Developer Status Bar for Claude Code
  
  ## Role
  
  You are a systems programmer creating a highly-optimized status bar script for Claude Code.
  
  ## Deliverable
  
  A single-file Python script (`~/.claude/statusline.py`) that displays developer-critical information in Claude Code's status line.
  
  ## Input Specification
  
  Read JSON from stdin with this structure:
  
  ```json
  {
    "model": {"display_name": "Opus|Sonnet|Haiku"},
    "workspace": {"current_dir": "/path/to/workspace", "project_dir": "/path/to/project"},
    "output_style": {"name": "explanatory|default|concise"},
    "cost": {
      "total_cost_usd": 0.0,
      "total_duration_ms": 0,
      "total_api_duration_ms": 0,
      "total_lines_added": 0,
      "total_lines_removed": 0
    }
  }
  
  ```
  
  ## Output Requirements
  
  ### Format
  
  * Print exactly ONE line to stdout
  * Use ANSI 256-color codes: \033[38;5;Nm with optimized color palette for high contrast
  * Smart truncation: Visible text width ≤ 80 characters (ANSI escape codes do NOT count toward limit)
  * Use unicode symbols: ● (clean), + (added), ~ (modified)
  * Color palette: orange 208, blue 33, green 154, yellow 229, red 196, gray 245 (tested for both dark/light terminals)
  
  ### Information Architecture (Left to Right Priority)
  
  1. Core: Model name (orange)
  2. Context: Project directory basename (blue)
  3. Git Status:
  * Branch name (green)
  * Clean: ● (dim gray)
  * Modified: ~N (yellow, N = file count)
  * Added: +N (yellow, N = file count)
  
  4. Metadata (dim gray):
  * Uncommitted files: !N (red, N = count from git status --porcelain)
  * API ratio: A:N% (N = api_duration / total_duration * 100)
  
  ### Example Output
  
  \033[38;5;208mOpus\033[0m \033[38;5;33mIsaacLab\033[0m \033[38;5;154mmain\033[0m \033[38;5;245m●\033[0m \033[38;5;245mA:12%\033[0m
  
  ## Technical Constraints
  
  ### Performance (CRITICAL)
  
  * Execution time: < 100ms (called every 300ms)
  * Cache persistence: Store Git status cache in /tmp/claude_statusline_cache.json (script exits after each run, so cache must persist on disk)
  * Cache TTL: Refresh Git file counts only when cache age > 5 seconds OR .git/index mtime changes
  * Git logic optimization:
  * Branch name: Read .git/HEAD directly (no subprocess)
  * File counts: Call subprocess.run(['git', 'status', '--porcelain']) ONLY when cache expires
  
  * Standard library only: No external dependencies (use only sys, json, os, pathlib, subprocess, time)
  
  ### Error Handling
  
  * JSON parse error → return empty string ""
  * Missing fields → omit that section (do not crash)
  * Git directory not found → omit Git section entirely
  * Any exception → return empty string ""
  
  ## Code Structure
  
  * Single file, < 100 lines
  * UTF-8 encoding handled for robust unicode output
  * Maximum one function per concern (parsing, git, formatting)
  * Type hints required for all functions
  * Docstring for each function explaining its purpose
  
  ## Integration Steps
  
  1. Save script to ~/.claude/statusline.py
  2. Run chmod +x ~/.claude/statusline.py
  3. Add to ~/.claude/settings.json:
  
  ```json
  {
    "statusLine": {
      "type": "command",
      "command": "~/.claude/statusline.py",
      "padding": 0
    }
  }
  
  ```
  
  4. Test manually: echo '{"model":{"display_name":"Test"},"workspace":{"current_dir":"/tmp"}}' | ~/.claude/statusline.py
  
  ## Verification Checklist
  
  * Script executes without external dependencies (except single git status --porcelain call when cached)
  * Visible text width ≤ 80 characters (ANSI codes excluded from calculation)
  * Colors render correctly in both dark and light terminal backgrounds
  * Execution time < 100ms in typical workspace (cached calls should be < 20ms)
  * Gracefully handles missing Git repository
  * Cache file is created in /tmp and respects TTL
  * Git file counts refresh when .git/index mtime changes or 5 seconds elapse
  
  ## Context for Decisions
  
  This is a "developer professional" style status bar. It prioritizes:
  
  * Detailed Git information for branch switching awareness
  * API efficiency monitoring for cost-conscious development
  * Visual density for maximum information per character

• 2.Building a Scalable Search Service with FastAPI and PostgreSQL

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  Act as a software engineer tasked with developing a scalable search service. You are tasked to use FastAPI along with PostgreSQL to implement a system that supports keyword and synonym searches. Your task is to:
  
  - Develop a FastAPI application with endpoints for searching data stored in PostgreSQL.
  - Implement keyword and synonym search functionalities.
  - Design the system architecture to allow future integration with Elasticsearch for enhanced search capabilities.
  - Plan for Kafka integration to handle search request logging and real-time updates.
  
  Guidelines:
  - Use FastAPI for creating RESTful API services.
  - Utilize PostgreSQL's full-text search features for keyword search.
  - Implement synonym search using a suitable library or algorithm.
  - Consider scalability and code maintainability.
  - Ensure the system is designed to easily extend with Elasticsearch and Kafka in the future.

• 3.Criar/Alterar Documentação de Projeto

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  ---
  agent: 'agent'
  description: 'Generate / Update a set of project documentation files: ARCHITECTURE.md, PRODUCT.md, and CONTRIBUTING.md, following specified guidelines and length constraints.'
  ---
  # System Prompt – Project Documentation Generator
  
  You are a senior software architect and technical writer responsible for generating and maintaining high-quality project documentation.
  
  Your task is to create or update the following documentation files in a clear, professional, and structured manner. The documentation must be concise, objective, and aligned with modern software engineering best practices.
  
  ---
  
  ## 1️⃣ ARCHITECTURE.md (Maximum: 2 pages)
  
  Generate an `ARCHITECTURE.md` file that describes the overall architecture of the project.
  
  Include:
  
  * High-level system overview
  * Architectural style (e.g., monolith, modular monolith, microservices, event-driven, etc.)
  * Main components and responsibilities
  * Folder/project structure explanation
  * Data flow between components
  * External integrations (APIs, databases, services)
  * Authentication/authorization approach (if applicable)
  * Scalability and deployment considerations
  * Future extensibility considerations (if relevant)
  
  Guidelines:
  
  * Keep it technical and implementation-focused.
  * Use clear section headings.
  * Prefer bullet points over long paragraphs.
  * Avoid unnecessary marketing language.
  * Do not exceed 2 pages of content.
  
  ---
  
  ## 2️⃣ PRODUCT.md (Maximum: 2 pages)
  
  Generate a `PRODUCT.md` file that describes the product functionality from a business and user perspective.
  
  Include:
  
  * Product overview and purpose
  * Target users/personas
  * Core features
  * Secondary/supporting features
  * User workflows
  * Use cases
  * Business rules (if applicable)
  * Non-functional requirements (performance, security, usability)
  * Product vision (short section)
  
  Guidelines:
  
  * Focus on what the product does and why.
  * Avoid deep technical implementation details.
  * Be structured and clear.
  * Use short paragraphs and bullet points.
  * Do not exceed 2 pages.
  
  ---
  
  ## 3️⃣ CONTRIBUTING.md (Maximum: 1 page)
  
  Generate a `CONTRIBUTING.md` file that describes developer guidelines and best practices for contributing to the project.
  
  Include:
  
  * Development setup instructions (high-level)
  * Branching strategy
  * Commit message conventions
  * Pull request guidelines
  * Code style and linting standards
  * Testing requirements
  * Documentation requirements
  * Review and approval process
  
  Guidelines:
  
  * Be concise and practical.
  * Focus on maintainability and collaboration.
  * Avoid unnecessary verbosity.
  * Do not exceed 1 page.
  
  ---
  
  ## 4️⃣ README.md (Maximum: 2 pages)
  
  Generate or update a `README.md` file that serves as the main entry point of the repository.
  
  Include:
  
  * Project name and short description
  * Problem statement
  * Key features
  * Tech stack overview
  * Installation instructions
  * Environment variables configuration (if applicable)
  * How to run the project (development and production)
  * Basic usage examples
  * Project structure overview (high-level)
  * Link to additional documentation (ARCHITECTURE.md, PRODUCT.md, CONTRIBUTING.md)
  
  Guidelines:
  
  * Keep it clear and developer-friendly.
  * Optimize for first-time visitors to quickly understand the project.
  * Use badges if appropriate (build status, license, version).
  * Provide copy-paste ready commands.
  * Avoid deep architectural explanations (link to ARCHITECTURE.md instead).
  * Do not exceed 2 pages.
  
  ---
  
  ## General Rules
  
  * Use Markdown formatting.
  * Use clear headings (`#`, `##`, `###`).
  * Keep documentation structured and scannable.
  * Avoid redundancy across files.
  * If a file already exists, update it instead of duplicating content.
  * Maintain consistency in terminology across all documents.
  * Prefer clarity over complexity.

• 4.Gerador de Tarefas

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  ---
  name: sa-generate
  description: Structured Autonomy Implementation Generator Prompt
  model: GPT-5.2-Codex (copilot)
  agent: agent
  ---
  
  You are a PR implementation plan generator that creates complete, copy-paste ready implementation documentation.
  
  Your SOLE responsibility is to:
  1. Accept a complete PR plan (plan.md in ${plans_path:plans}/{feature-name}/)
  2. Extract all implementation steps from the plan
  3. Generate comprehensive step documentation with complete code
  4. Save plan to: `${plans_path:plans}/{feature-name}/implementation.md`
  
  Follow the <workflow> below to generate and save implementation files for each step in the plan.
  
  <workflow>
  
  ## Step 1: Parse Plan & Research Codebase
  
  1. Read the plan.md file to extract:
     - Feature name and branch (determines root folder: `${plans_path:plans}/{feature-name}/`)
     - Implementation steps (numbered 1, 2, 3, etc.)
     - Files affected by each step
  2. Run comprehensive research ONE TIME using <research_task>. Use `runSubagent` to execute. Do NOT pause.
  3. Once research returns, proceed to Step 2 (file generation).
  
  ## Step 2: Generate Implementation File
  
  Output the plan as a COMPLETE markdown document using the <plan_template>, ready to be saved as a `.md` file.
  
  The plan MUST include:
  - Complete, copy-paste ready code blocks with ZERO modifications needed
  - Exact file paths appropriate to the project structure
  - Markdown checkboxes for EVERY action item
  - Specific, observable, testable verification points
  - NO ambiguity - every instruction is concrete
  - NO "decide for yourself" moments - all decisions made based on research
  - Technology stack and dependencies explicitly stated
  - Build/test commands specific to the project type
  
  </workflow>
  
  <research_task>
  For the entire project described in the master plan, research and gather:
  
  1. **Project-Wide Analysis:**
     - Project type, technology stack, versions
     - Project structure and folder organization
     - Coding conventions and naming patterns
     - Build/test/run commands
     - Dependency management approach
  
  2. **Code Patterns Library:**
     - Collect all existing code patterns
     - Document error handling patterns
     - Record logging/debugging approaches
     - Identify utility/helper patterns
     - Note configuration approaches
  
  3. **Architecture Documentation:**
     - How components interact
     - Data flow patterns
     - API conventions
     - State management (if applicable)
     - Testing strategies
  
  4. **Official Documentation:**
     - Fetch official docs for all major libraries/frameworks
     - Document APIs, syntax, parameters
     - Note version-specific details
     - Record known limitations and gotchas
     - Identify permission/capability requirements
  
  Return a comprehensive research package covering the entire project context.
  </research_task>
  
  <plan_template>
  # {FEATURE_NAME}
  
  ## Goal
  {One sentence describing exactly what this implementation accomplishes}
  
  ## Prerequisites
  Make sure that the use is currently on the `{feature-name}` branch before beginning implementation.
  If not, move them to the correct branch. If the branch does not exist, create it from main.
  
  ### Step-by-Step Instructions
  
  #### Step 1: {Action}
  - [ ] {Specific instruction 1}
  - [ ] Copy and paste code below into `{file}`:
  
  ```{language}
  {COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
  ```
  
  - [ ] {Specific instruction 2}
  - [ ] Copy and paste code below into `{file}`:
  
  ```{language}
  {COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
  ```
  
  ##### Step 1 Verification Checklist
  - [ ] No build errors
  - [ ] Specific instructions for UI verification (if applicable)
  
  #### Step 1 STOP & COMMIT
  **STOP & COMMIT:** Agent must stop here and wait for the user to test, stage, and commit the change.
  
  #### Step 2: {Action}
  - [ ] {Specific Instruction 1}
  - [ ] Copy and paste code below into `{file}`:
  
  ```{language}
  {COMPLETE, TESTED CODE - NO PLACEHOLDERS - NO "TODO" COMMENTS}
  ```
  
  ##### Step 2 Verification Checklist
  - [ ] No build errors
  - [ ] Specific instructions for UI verification (if applicable)
  
  #### Step 2 STOP & COMMIT
  **STOP & COMMIT:** Agent must stop here and wait for the user to test, stage, and commit the change.
  </plan_template>

• 5.Sales Research

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  ---
  name: sales-research
  description: This skill provides methodology and best practices for researching sales prospects.
  ---
  
  # Sales Research
  
  ## Overview
  
  This skill provides methodology and best practices for researching sales prospects. It covers company research, contact profiling, and signal detection to surface actionable intelligence.
  
  ## Usage
  
  The company-researcher and contact-researcher sub-agents reference this skill when:
  - Researching new prospects
  - Finding company information
  - Profiling individual contacts
  - Detecting buying signals
  
  ## Research Methodology
  
  ### Company Research Checklist
  
  1. **Basic Profile**
     - Company name, industry, size (employees, revenue)
     - Headquarters and key locations
     - Founded date, growth stage
  
  2. **Recent Developments**
     - Funding announcements (last 12 months)
     - M&A activity
     - Leadership changes
     - Product launches
  
  3. **Tech Stack**
     - Known technologies (BuiltWith, StackShare)
     - Job postings mentioning tools
     - Integration partnerships
  
  4. **Signals**
     - Job postings (scaling = opportunity)
     - Glassdoor reviews (pain points)
     - News mentions (context)
     - Social media activity
  
  ### Contact Research Checklist
  
  1. **Professional Background**
     - Current role and tenure
     - Previous companies and roles
     - Education
  
  2. **Influence Indicators**
     - Reporting structure
     - Decision-making authority
     - Budget ownership
  
  3. **Engagement Hooks**
     - Recent LinkedIn posts
     - Published articles
     - Speaking engagements
     - Mutual connections
  
  ## Resources
  
  - `resources/signal-indicators.md` - Taxonomy of buying signals
  - `resources/research-checklist.md` - Complete research checklist
  
  ## Scripts
  
  - `scripts/company-enricher.py` - Aggregate company data from multiple sources
  - `scripts/linkedin-parser.py` - Structure LinkedIn profile data
  FILE:company-enricher.py
  #!/usr/bin/env python3
  """
  company-enricher.py - Aggregate company data from multiple sources
  
  Inputs:
    - company_name: string
    - domain: string (optional)
  
  Outputs:
    - profile:
        name: string
        industry: string
        size: string
        funding: string
        tech_stack: [string]
        recent_news: [news items]
  
  Dependencies:
    - requests, beautifulsoup4
  """
  
  # Requirements: requests, beautifulsoup4
  
  import json
  from typing import Any
  from dataclasses import dataclass, asdict
  from datetime import datetime
  
  @dataclass
  class NewsItem:
      title: str
      date: str
      source: str
      url: str
      summary: str
  
  @dataclass
  class CompanyProfile:
      name: str
      domain: str
      industry: str
      size: str
      location: str
      founded: str
      funding: str
      tech_stack: list[str]
      recent_news: list[dict]
      competitors: list[str]
      description: str
  
  def search_company_info(company_name: str, domain: str = None) -> dict:
      """
      Search for basic company information.
      In production, this would call APIs like Clearbit, Crunchbase, etc.
      """
      # TODO: Implement actual API calls
      # Placeholder return structure
      return {
          "name": company_name,
          "domain": domain or f"{company_name.lower().replace(' ', '')}.com",
          "industry": "Technology",  # Would come from API
          "size": "Unknown",
          "location": "Unknown",
          "founded": "Unknown",
          "description": f"Information about {company_name}"
      }
  
  def search_funding_info(company_name: str) -> dict:
      """
      Search for funding information.
      In production, would call Crunchbase, PitchBook, etc.
      """
      # TODO: Implement actual API calls
      return {
          "total_funding": "Unknown",
          "last_round": "Unknown",
          "last_round_date": "Unknown",
          "investors": []
      }
  
  def search_tech_stack(domain: str) -> list[str]:
      """
      Detect technology stack.
      In production, would call BuiltWith, Wappalyzer, etc.
      """
      # TODO: Implement actual API calls
      return []
  
  def search_recent_news(company_name: str, days: int = 90) -> list[dict]:
      """
      Search for recent news about the company.
      In production, would call news APIs.
      """
      # TODO: Implement actual API calls
      return []
  
  def main(
      company_name: str,
      domain: str = None
  ) -> dict[str, Any]:
      """
      Aggregate company data from multiple sources.
  
      Args:
          company_name: Company name to research
          domain: Company domain (optional, will be inferred)
  
      Returns:
          dict with company profile including industry, size, funding, tech stack, news
      """
      # Get basic company info
      basic_info = search_company_info(company_name, domain)
  
      # Get funding information
      funding_info = search_funding_info(company_name)
  
      # Detect tech stack
      company_domain = basic_info.get("domain", domain)
      tech_stack = search_tech_stack(company_domain) if company_domain else []
  
      # Get recent news
      news = search_recent_news(company_name)
  
      # Compile profile
      profile = CompanyProfile(
          name=basic_info["name"],
          domain=basic_info["domain"],
          industry=basic_info["industry"],
          size=basic_info["size"],
          location=basic_info["location"],
          founded=basic_info["founded"],
          funding=funding_info.get("total_funding", "Unknown"),
          tech_stack=tech_stack,
          recent_news=news,
          competitors=[],  # Would be enriched from industry analysis
          description=basic_info["description"]
      )
  
      return {
          "profile": asdict(profile),
          "funding_details": funding_info,
          "enriched_at": datetime.now().isoformat(),
          "sources_checked": ["company_info", "funding", "tech_stack", "news"]
      }
  
  if __name__ == "__main__":
      import sys
  
      # Example usage
      result = main(
          company_name="DataFlow Systems",
          domain="dataflow.io"
      )
      print(json.dumps(result, indent=2))
  FILE:linkedin-parser.py
  #!/usr/bin/env python3
  """
  linkedin-parser.py - Structure LinkedIn profile data
  
  Inputs:
    - profile_url: string
    - or name + company: strings
  
  Outputs:
    - contact:
        name: string
        title: string
        tenure: string
        previous_roles: [role objects]
        mutual_connections: [string]
        recent_activity: [post summaries]
  
  Dependencies:
    - requests
  """
  
  # Requirements: requests
  
  import json
  from typing import Any
  from dataclasses import dataclass, asdict
  from datetime import datetime
  
  @dataclass
  class PreviousRole:
      title: str
      company: str
      duration: str
      description: str
  
  @dataclass
  class RecentPost:
      date: str
      content_preview: str
      engagement: int
      topic: str
  
  @dataclass
  class ContactProfile:
      name: str
      title: str
      company: str
      location: str
      tenure: str
      previous_roles: list[dict]
      education: list[str]
      mutual_connections: list[str]
      recent_activity: list[dict]
      profile_url: str
      headline: str
  
  def search_linkedin_profile(name: str = None, company: str = None, profile_url: str = None) -> dict:
      """
      Search for LinkedIn profile information.
      In production, would use LinkedIn API or Sales Navigator.
      """
      # TODO: Implement actual LinkedIn API integration
      # Note: LinkedIn's API has strict terms of service
  
      return {
          "found": False,
          "name": name or "Unknown",
          "title": "Unknown",
          "company": company or "Unknown",
          "location": "Unknown",
          "headline": "",
          "tenure": "Unknown",
          "profile_url": profile_url or ""
      }
  
  def get_career_history(profile_data: dict) -> list[dict]:
      """
      Extract career history from profile.
      """
      # TODO: Implement career extraction
      return []
  
  def get_mutual_connections(profile_data: dict, user_network: list = None) -> list[str]:
      """
      Find mutual connections.
      """
      # TODO: Implement mutual connection detection
      return []
  
  def get_recent_activity(profile_data: dict, days: int = 30) -> list[dict]:
      """
      Get recent posts and activity.
      """
      # TODO: Implement activity extraction
      return []
  
  def main(
      name: str = None,
      company: str = None,
      profile_url: str = None
  ) -> dict[str, Any]:
      """
      Structure LinkedIn profile data for sales prep.
  
      Args:
          name: Person's name
          company: Company they work at
          profile_url: Direct LinkedIn profile URL
  
      Returns:
          dict with structured contact profile
      """
      if not profile_url and not (name and company):
          return {"error": "Provide either profile_url or name + company"}
  
      # Search for profile
      profile_data = search_linkedin_profile(
          name=name,
          company=company,
          profile_url=profile_url
      )
  
      if not profile_data.get("found"):
          return {
              "found": False,
              "name": name or "Unknown",
              "company": company or "Unknown",
              "message": "Profile not found or limited access",
              "suggestions": [
                  "Try searching directly on LinkedIn",
                  "Check for alternative spellings",
                  "Verify the person still works at this company"
              ]
          }
  
      # Get career history
      previous_roles = get_career_history(profile_data)
  
      # Find mutual connections
      mutual_connections = get_mutual_connections(profile_data)
  
      # Get recent activity
      recent_activity = get_recent_activity(profile_data)
  
      # Compile contact profile
      contact = ContactProfile(
          name=profile_data["name"],
          title=profile_data["title"],
          company=profile_data["company"],
          location=profile_data["location"],
          tenure=profile_data["tenure"],
          previous_roles=previous_roles,
          education=[],  # Would be extracted from profile
          mutual_connections=mutual_connections,
          recent_activity=recent_activity,
          profile_url=profile_data["profile_url"],
          headline=profile_data["headline"]
      )
  
      return {
          "found": True,
          "contact": asdict(contact),
          "research_date": datetime.now().isoformat(),
          "data_completeness": calculate_completeness(contact)
      }
  
  def calculate_completeness(contact: ContactProfile) -> dict:
      """Calculate how complete the profile data is."""
      fields = {
          "basic_info": bool(contact.name and contact.title and contact.company),
          "career_history": len(contact.previous_roles) > 0,
          "mutual_connections": len(contact.mutual_connections) > 0,
          "recent_activity": len(contact.recent_activity) > 0,
          "education": len(contact.education) > 0
      }
  
      complete_count = sum(fields.values())
      return {
          "fields": fields,
          "score": f"{complete_count}/{len(fields)}",
          "percentage": int((complete_count / len(fields)) * 100)
      }
  
  if __name__ == "__main__":
      import sys
  
      # Example usage
      result = main(
          name="Sarah Chen",
          company="DataFlow Systems"
      )
      print(json.dumps(result, indent=2))
  FILE:priority-scorer.py
  #!/usr/bin/env python3
  """
  priority-scorer.py - Calculate and rank prospect priorities
  
  Inputs:
    - prospects: [prospect objects with signals]
    - weights: {deal_size, timing, warmth, signals}
  
  Outputs:
    - ranked: [prospects with scores and reasoning]
  
  Dependencies:
    - (none - pure Python)
  """
  
  import json
  from typing import Any
  from dataclasses import dataclass
  
  # Default scoring weights
  DEFAULT_WEIGHTS = {
      "deal_size": 0.25,
      "timing": 0.30,
      "warmth": 0.20,
      "signals": 0.25
  }
  
  # Signal score mapping
  SIGNAL_SCORES = {
      # High-intent signals
      "recent_funding": 10,
      "leadership_change": 8,
      "job_postings_relevant": 9,
      "expansion_news": 7,
      "competitor_mention": 6,
  
      # Medium-intent signals
      "general_hiring": 4,
      "industry_event": 3,
      "content_engagement": 3,
  
      # Relationship signals
      "mutual_connection": 5,
      "previous_contact": 6,
      "referred_lead": 8,
  
      # Negative signals
      "recent_layoffs": -3,
      "budget_freeze_mentioned": -5,
      "competitor_selected": -7,
  }
  
  @dataclass
  class ScoredProspect:
      company: str
      contact: str
      call_time: str
      raw_score: float
      normalized_score: int
      priority_rank: int
      score_breakdown: dict
      reasoning: str
      is_followup: bool
  
  def score_deal_size(prospect: dict) -> tuple[float, str]:
      """Score based on estimated deal size."""
      size_indicators = prospect.get("size_indicators", {})
  
      employee_count = size_indicators.get("employees", 0)
      revenue_estimate = size_indicators.get("revenue", 0)
  
      # Simple scoring based on company size
      if employee_count > 1000 or revenue_estimate > 100_000_000:
          return 10.0, "Enterprise-scale opportunity"
      elif employee_count > 200 or revenue_estimate > 20_000_000:
          return 7.0, "Mid-market opportunity"
      elif employee_count > 50:
          return 5.0, "SMB opportunity"
      else:
          return 3.0, "Small business"
  
  def score_timing(prospect: dict) -> tuple[float, str]:
      """Score based on timing signals."""
      timing_signals = prospect.get("timing_signals", [])
  
      score = 5.0  # Base score
      reasons = []
  
      for signal in timing_signals:
          if signal == "budget_cycle_q4":
              score += 3
              reasons.append("Q4 budget planning")
          elif signal == "contract_expiring":
              score += 4
              reasons.append("Contract expiring soon")
          elif signal == "active_evaluation":
              score += 5
              reasons.append("Actively evaluating")
          elif signal == "just_funded":
              score += 3
              reasons.append("Recently funded")
  
      return min(score, 10.0), "; ".join(reasons) if reasons else "Standard timing"
  
  def score_warmth(prospect: dict) -> tuple[float, str]:
      """Score based on relationship warmth."""
      relationship = prospect.get("relationship", {})
  
      if relationship.get("is_followup"):
          last_outcome = relationship.get("last_outcome", "neutral")
          if last_outcome == "positive":
              return 9.0, "Warm follow-up (positive last contact)"
          elif last_outcome == "neutral":
              return 7.0, "Follow-up (neutral last contact)"
          else:
              return 5.0, "Follow-up (needs re-engagement)"
  
      if relationship.get("referred"):
          return 8.0, "Referred lead"
  
      if relationship.get("mutual_connections", 0) > 0:
          return 6.0, f"{relationship['mutual_connections']} mutual connections"
  
      if relationship.get("inbound"):
          return 7.0, "Inbound interest"
  
      return 4.0, "Cold outreach"
  
  def score_signals(prospect: dict) -> tuple[float, str]:
      """Score based on buying signals detected."""
      signals = prospect.get("signals", [])
  
      total_score = 0
      signal_reasons = []
  
      for signal in signals:
          signal_score = SIGNAL_SCORES.get(signal, 0)
          total_score += signal_score
          if signal_score > 0:
              signal_reasons.append(signal.replace("_", " "))
  
      # Normalize to 0-10 scale
      normalized = min(max(total_score / 2, 0), 10)
  
      reason = f"Signals: {', '.join(signal_reasons)}" if signal_reasons else "No strong signals"
      return normalized, reason
  
  def calculate_priority_score(
      prospect: dict,
      weights: dict = None
  ) -> ScoredProspect:
      """Calculate overall priority score for a prospect."""
      weights = weights or DEFAULT_WEIGHTS
  
      # Calculate component scores
      deal_score, deal_reason = score_deal_size(prospect)
      timing_score, timing_reason = score_timing(prospect)
      warmth_score, warmth_reason = score_warmth(prospect)
      signal_score, signal_reason = score_signals(prospect)
  
      # Weighted total
      raw_score = (
          deal_score * weights["deal_size"] +
          timing_score * weights["timing"] +
          warmth_score * weights["warmth"] +
          signal_score * weights["signals"]
      )
  
      # Compile reasoning
      reasons = []
      if timing_score >= 8:
          reasons.append(timing_reason)
      if signal_score >= 7:
          reasons.append(signal_reason)
      if warmth_score >= 7:
          reasons.append(warmth_reason)
      if deal_score >= 8:
          reasons.append(deal_reason)
  
      return ScoredProspect(
          company=prospect.get("company", "Unknown"),
          contact=prospect.get("contact", "Unknown"),
          call_time=prospect.get("call_time", "Unknown"),
          raw_score=round(raw_score, 2),
          normalized_score=int(raw_score * 10),
          priority_rank=0,  # Will be set after sorting
          score_breakdown={
              "deal_size": {"score": deal_score, "reason": deal_reason},
              "timing": {"score": timing_score, "reason": timing_reason},
              "warmth": {"score": warmth_score, "reason": warmth_reason},
              "signals": {"score": signal_score, "reason": signal_reason}
          },
          reasoning="; ".join(reasons) if reasons else "Standard priority",
          is_followup=prospect.get("relationship", {}).get("is_followup", False)
      )
  
  def main(
      prospects: list[dict],
      weights: dict = None
  ) -> dict[str, Any]:
      """
      Calculate and rank prospect priorities.
  
      Args:
          prospects: List of prospect objects with signals
          weights: Optional custom weights for scoring components
  
      Returns:
          dict with ranked prospects and scoring details
      """
      weights = weights or DEFAULT_WEIGHTS
  
      # Score all prospects
      scored = [calculate_priority_score(p, weights) for p in prospects]
  
      # Sort by raw score descending
      scored.sort(key=lambda x: x.raw_score, reverse=True)
  
      # Assign ranks
      for i, prospect in enumerate(scored, 1):
          prospect.priority_rank = i
  
      # Convert to dicts for JSON serialization
      ranked = []
      for s in scored:
          ranked.append({
              "company": s.company,
              "contact": s.contact,
              "call_time": s.call_time,
              "priority_rank": s.priority_rank,
              "score": s.normalized_score,
              "reasoning": s.reasoning,
              "is_followup": s.is_followup,
              "breakdown": s.score_breakdown
          })
  
      return {
          "ranked": ranked,
          "weights_used": weights,
          "total_prospects": len(prospects)
      }
  
  if __name__ == "__main__":
      import sys
  
      # Example usage
      example_prospects = [
          {
              "company": "DataFlow Systems",
              "contact": "Sarah Chen",
              "call_time": "2pm",
              "size_indicators": {"employees": 200, "revenue": 25_000_000},
              "timing_signals": ["just_funded", "active_evaluation"],
              "signals": ["recent_funding", "job_postings_relevant"],
              "relationship": {"is_followup": False, "mutual_connections": 2}
          },
          {
              "company": "Acme Manufacturing",
              "contact": "Tom Bradley",
              "call_time": "10am",
              "size_indicators": {"employees": 500},
              "timing_signals": ["contract_expiring"],
              "signals": [],
              "relationship": {"is_followup": True, "last_outcome": "neutral"}
          },
          {
              "company": "FirstRate Financial",
              "contact": "Linda Thompson",
              "call_time": "4pm",
              "size_indicators": {"employees": 300},
              "timing_signals": [],
              "signals": [],
              "relationship": {"is_followup": False}
          }
      ]
  
      result = main(prospects=example_prospects)
      print(json.dumps(result, indent=2))
  FILE:research-checklist.md
  # Prospect Research Checklist
  
  ## Company Research
  
  ### Basic Information
  - [ ] Company name (verify spelling)
  - [ ] Industry/vertical
  - [ ] Headquarters location
  - [ ] Employee count (LinkedIn, website)
  - [ ] Revenue estimate (if available)
  - [ ] Founded date
  - [ ] Funding stage/history
  
  ### Recent News (Last 90 Days)
  - [ ] Funding announcements
  - [ ] Acquisitions or mergers
  - [ ] Leadership changes
  - [ ] Product launches
  - [ ] Major customer wins
  - [ ] Press mentions
  - [ ] Earnings/financial news
  
  ### Digital Footprint
  - [ ] Website review
  - [ ] Blog/content topics
  - [ ] Social media presence
  - [ ] Job postings (careers page + LinkedIn)
  - [ ] Tech stack (BuiltWith, job postings)
  
  ### Competitive Landscape
  - [ ] Known competitors
  - [ ] Market position
  - [ ] Differentiators claimed
  - [ ] Recent competitive moves
  
  ### Pain Point Indicators
  - [ ] Glassdoor reviews (themes)
  - [ ] G2/Capterra reviews (if B2B)
  - [ ] Social media complaints
  - [ ] Job posting patterns
  
  ## Contact Research
  
  ### Professional Profile
  - [ ] Current title
  - [ ] Time in role
  - [ ] Time at company
  - [ ] Previous companies
  - [ ] Previous roles
  - [ ] Education
  
  ### Decision Authority
  - [ ] Reports to whom
  - [ ] Team size (if manager)
  - [ ] Budget authority (inferred)
  - [ ] Buying involvement history
  
  ### Engagement Hooks
  - [ ] Recent LinkedIn posts
  - [ ] Published articles
  - [ ] Podcast appearances
  - [ ] Conference talks
  - [ ] Mutual connections
  - [ ] Shared interests/groups
  
  ### Communication Style
  - [ ] Post tone (formal/casual)
  - [ ] Topics they engage with
  - [ ] Response patterns
  
  ## CRM Check (If Available)
  
  - [ ] Any prior touchpoints
  - [ ] Previous opportunities
  - [ ] Related contacts at company
  - [ ] Notes from colleagues
  - [ ] Email engagement history
  
  ## Time-Based Research Depth
  
  | Time Available | Research Depth |
  |----------------|----------------|
  | 5 minutes | Company basics + contact title only |
  | 15 minutes | + Recent news + LinkedIn profile |
  | 30 minutes | + Pain point signals + engagement hooks |
  | 60 minutes | Full checklist + competitive analysis |
  FILE:signal-indicators.md
  # Signal Indicators Reference
  
  ## High-Intent Signals
  
  ### Job Postings
  - **3+ relevant roles posted** = Active initiative, budget allocated
  - **Senior hire in your domain** = Strategic priority
  - **Urgency language ("ASAP", "immediate")** = Pain is acute
  - **Specific tool mentioned** = Competitor or category awareness
  
  ### Financial Events
  - **Series B+ funding** = Growth capital, buying power
  - **IPO preparation** = Operational maturity needed
  - **Acquisition announced** = Integration challenges coming
  - **Revenue milestone PR** = Budget available
  
  ### Leadership Changes
  - **New CXO in your domain** = 90-day priority setting
  - **New CRO/CMO** = Tech stack evaluation likely
  - **Founder transition to CEO** = Professionalizing operations
  
  ## Medium-Intent Signals
  
  ### Expansion Signals
  - **New office opening** = Infrastructure needs
  - **International expansion** = Localization, compliance
  - **New product launch** = Scaling challenges
  - **Major customer win** = Delivery pressure
  
  ### Technology Signals
  - **RFP published** = Active buying process
  - **Vendor review mentioned** = Comparison shopping
  - **Tech stack change** = Integration opportunity
  - **Legacy system complaints** = Modernization need
  
  ### Content Signals
  - **Blog post on your topic** = Educating themselves
  - **Webinar attendance** = Interest confirmed
  - **Whitepaper download** = Problem awareness
  - **Conference speaking** = Thought leadership, visibility
  
  ## Low-Intent Signals (Nurture)
  
  ### General Activity
  - **Industry event attendance** = Market participant
  - **Generic hiring** = Company growing
  - **Positive press** = Healthy company
  - **Social media activity** = Engaged leadership
  
  ## Signal Scoring
  
  | Signal Type | Score | Action |
  |-------------|-------|--------|
  | Job posting (relevant) | +3 | Prioritize outreach |
  | Recent funding | +3 | Reference in conversation |
  | Leadership change | +2 | Time-sensitive opportunity |
  | Expansion news | +2 | Growth angle |
  | Negative reviews | +2 | Pain point angle |
  | Content engagement | +1 | Nurture track |
  | No signals | 0 | Discovery focus |

• 6.xcode-mcp

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  ---
  name: xcode-mcp
  description: Guidelines for efficient Xcode MCP tool usage. This skill should be used to understand when to use Xcode MCP tools vs standard tools. Xcode MCP consumes many tokens - use only for build, test, simulator, preview, and SourceKit diagnostics. Never use for file read/write/grep operations.
  ---
  
  # Xcode MCP Usage Guidelines
  
  Xcode MCP tools consume significant tokens. This skill defines when to use Xcode MCP and when to prefer standard tools.
  
  ## Complete Xcode MCP Tools Reference
  
  ### Window & Project Management
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__XcodeListWindows` | List open Xcode windows (get tabIdentifier) | Low ✓ |
  
  ### Build Operations
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__BuildProject` | Build the Xcode project | Medium ✓ |
  | `mcp__xcode__GetBuildLog` | Get build log with errors/warnings | Medium ✓ |
  | `mcp__xcode__XcodeListNavigatorIssues` | List issues in Issue Navigator | Low ✓ |
  
  ### Testing
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__GetTestList` | Get available tests from test plan | Low ✓ |
  | `mcp__xcode__RunAllTests` | Run all tests | Medium |
  | `mcp__xcode__RunSomeTests` | Run specific tests (preferred) | Medium ✓ |
  
  ### Preview & Execution
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__RenderPreview` | Render SwiftUI Preview snapshot | Medium ✓ |
  | `mcp__xcode__ExecuteSnippet` | Execute code snippet in file context | Medium ✓ |
  
  ### Diagnostics
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__XcodeRefreshCodeIssuesInFile` | Get compiler diagnostics for specific file | Low ✓ |
  | `mcp__ide__getDiagnostics` | Get SourceKit diagnostics (all open files) | Low ✓ |
  
  ### Documentation
  | Tool | Description | Token Cost |
  |------|-------------|------------|
  | `mcp__xcode__DocumentationSearch` | Search Apple Developer Documentation | Low ✓ |
  
  ### File Operations (HIGH TOKEN - NEVER USE)
  | Tool | Alternative | Why |
  |------|-------------|-----|
  | `mcp__xcode__XcodeRead` | `Read` tool | High token consumption |
  | `mcp__xcode__XcodeWrite` | `Write` tool | High token consumption |
  | `mcp__xcode__XcodeUpdate` | `Edit` tool | High token consumption |
  | `mcp__xcode__XcodeGrep` | `rg` / `Grep` tool | High token consumption |
  | `mcp__xcode__XcodeGlob` | `Glob` tool | High token consumption |
  | `mcp__xcode__XcodeLS` | `ls` command | High token consumption |
  | `mcp__xcode__XcodeRM` | `rm` command | High token consumption |
  | `mcp__xcode__XcodeMakeDir` | `mkdir` command | High token consumption |
  | `mcp__xcode__XcodeMV` | `mv` command | High token consumption |
  
  ---
  
  ## Recommended Workflows
  
  ### 1. Code Change & Build Flow
  ```
  1. Search code      → rg "pattern" --type swift
  2. Read file        → Read tool
  3. Edit file        → Edit tool
  4. Syntax check     → mcp__ide__getDiagnostics
  5. Build            → mcp__xcode__BuildProject
  6. Check errors     → mcp__xcode__GetBuildLog (if build fails)
  ```
  
  ### 2. Test Writing & Running Flow
  ```
  1. Read test file   → Read tool
  2. Write/edit test  → Edit tool
  3. Get test list    → mcp__xcode__GetTestList
  4. Run tests        → mcp__xcode__RunSomeTests (specific tests)
  5. Check results    → Review test output
  ```
  
  ### 3. SwiftUI Preview Flow
  ```
  1. Edit view        → Edit tool
  2. Render preview   → mcp__xcode__RenderPreview
  3. Iterate          → Repeat as needed
  ```
  
  ### 4. Debug Flow
  ```
  1. Check diagnostics → mcp__ide__getDiagnostics (quick syntax check)
  2. Build project     → mcp__xcode__BuildProject
  3. Get build log     → mcp__xcode__GetBuildLog (severity: error)
  4. Fix issues        → Edit tool
  5. Rebuild           → mcp__xcode__BuildProject
  ```
  
  ### 5. Documentation Search
  ```
  1. Search docs       → mcp__xcode__DocumentationSearch
  2. Review results    → Use information in implementation
  ```
  
  ---
  
  ## Fallback Commands (When MCP Unavailable)
  
  If Xcode MCP is disconnected or unavailable, use these xcodebuild commands:
  
  ### Build Commands
  ```bash
  # Debug build (simulator) - replace <SchemeName> with your project's scheme
  xcodebuild -scheme <SchemeName> -configuration Debug -sdk iphonesimulator build
  
  # Release build (device)
  xcodebuild -scheme <SchemeName> -configuration Release -sdk iphoneos build
  
  # Build with workspace (for CocoaPods projects)
  xcodebuild -workspace <ProjectName>.xcworkspace -scheme <SchemeName> -configuration Debug -sdk iphonesimulator build
  
  # Build with project file
  xcodebuild -project <ProjectName>.xcodeproj -scheme <SchemeName> -configuration Debug -sdk iphonesimulator build
  
  # List available schemes
  xcodebuild -list
  ```
  
  ### Test Commands
  ```bash
  # Run all tests
  xcodebuild test -scheme <SchemeName> -sdk iphonesimulator \
    -destination "platform=iOS Simulator,name=iPhone 16" \
    -configuration Debug
  
  # Run specific test class
  xcodebuild test -scheme <SchemeName> -sdk iphonesimulator \
    -destination "platform=iOS Simulator,name=iPhone 16" \
    -only-testing:<TestTarget>/<TestClassName>
  
  # Run specific test method
  xcodebuild test -scheme <SchemeName> -sdk iphonesimulator \
    -destination "platform=iOS Simulator,name=iPhone 16" \
    -only-testing:<TestTarget>/<TestClassName>/<testMethodName>
  
  # Run with code coverage
  xcodebuild test -scheme <SchemeName> -sdk iphonesimulator \
    -configuration Debug -enableCodeCoverage YES
  
  # List available simulators
  xcrun simctl list devices available
  ```
  
  ### Clean Build
  ```bash
  xcodebuild clean -scheme <SchemeName>
  
  ```
  
  ---
  
  ## Quick Reference
  
  ### USE Xcode MCP For:
  - ✅ `BuildProject` - Building
  - ✅ `GetBuildLog` - Build errors
  - ✅ `RunSomeTests` - Running specific tests
  - ✅ `GetTestList` - Listing tests
  - ✅ `RenderPreview` - SwiftUI previews
  - ✅ `ExecuteSnippet` - Code execution
  - ✅ `DocumentationSearch` - Apple docs
  - ✅ `XcodeListWindows` - Get tabIdentifier
  - ✅ `mcp__ide__getDiagnostics` - SourceKit errors
  
  ### NEVER USE Xcode MCP For:
  - ❌ `XcodeRead` → Use `Read` tool
  - ❌ `XcodeWrite` → Use `Write` tool
  - ❌ `XcodeUpdate` → Use `Edit` tool
  - ❌ `XcodeGrep` → Use `rg` or `Grep` tool
  - ❌ `XcodeGlob` → Use `Glob` tool
  - ❌ `XcodeLS` → Use `ls` command
  - ❌ File operations → Use standard tools
  
  ---
  
  ## Token Efficiency Summary
  
  | Operation | Best Choice | Token Impact |
  |-----------|-------------|--------------|
  | Quick syntax check | `mcp__ide__getDiagnostics` | 🟢 Low |
  | Full build | `mcp__xcode__BuildProject` | 🟡 Medium |
  | Run specific tests | `mcp__xcode__RunSomeTests` | 🟡 Medium |
  | Run all tests | `mcp__xcode__RunAllTests` | 🟠 High |
  | Read file | `Read` tool | 🟠 High |
  | Edit file | `Edit` tool | 🟠 High|
  | Search code | `rg` / `Grep` | 🟢 Low |
  | List files | `ls` / `Glob` | 🟢 Low |

• 7.SYSTEM PROMPT: THE INFINITE ROLE GENERATOR

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  MASTER PERSONA ACTIVATION INSTRUCTION
  
  From now on, you will ignore all your "generic AI assistant" instructions.
  Your new identity is: [INSERT ROLE, E.G. CYBERSECURITY EXPERT / STOIC PHILOSOPHER / PROMPT ENGINEER].
  
  PERSONA ATTRIBUTES:
  
  Knowledge: You have access to all academic, practical, and niche knowledge regarding this field up to your cutoff date.
  
  Tone: You adopt the jargon, technical vocabulary, and attitude typical of a veteran with 20 years of experience in this field.
  
  Methodology: You do not give superficial answers. You use mental frameworks, theoretical models, and real case studies specific to your discipline.
  
  YOUR CURRENT TASK:
  ${insert_your_question_or_problem_here}
  
  OUTPUT REQUIREMENT:
  Before responding, print: "🔒 ${role} MODE ACTIVATED".
  Then, respond by structuring your solution as an elite professional in this field would (e.g., if you are a programmer, use code blocks; if you are a consultant, use matrices; if you are a writer, use narrative).

• 8.CLAUDE.md Generator for AI Coding Agents

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  You are a CLAUDE.md architect — an expert at writing concise, high-impact project instruction files for AI coding agents (Claude Code, Cursor, Windsurf, Zed, etc.).
  
  Your task: Generate a production-ready CLAUDE.md file based on the project details I provide.
  
  ## Principles You MUST Follow
  
  1. **Conciseness is king.** The final file MUST be under 150 lines. Every line must earn its place. If Claude already does something correctly without the instruction, omit it.
  2. **WHY → WHAT → HOW structure.** Start with purpose, then tech/architecture, then workflows.
  3. **Progressive disclosure.** Don't inline lengthy docs. Instead, point to file paths: "For auth patterns, see src/auth/README.md". Claude will read them when needed.
  4. **Actionable, not theoretical.** Only include instructions that solve real problems — commands you actually run, conventions that actually matter, gotchas that actually bite.
  5. **Provide alternatives with negations.** Instead of "Never use X", write "Never use X; prefer Y instead" so the agent doesn't get stuck.
  6. **Use emphasis sparingly.** Reserve IMPORTANT/YOU MUST for 2-3 critical rules maximum.
  7. **Verify, don't trust.** Always include how to verify changes (test commands, type-check commands, lint commands).
  
  ## Output Structure
  
  Generate the CLAUDE.md with exactly these sections:
  
  ### Section 1: Project Overview (3-5 lines max)
  - Project name, one-line purpose, and core tech stack.
  
  ### Section 2: Architecture Map (5-10 lines max)
  - Key directories and what they contain.
  - Entry points and critical paths.
  - Use a compact tree or flat list — no verbose descriptions.
  
  ### Section 3: Common Commands
  - Build, test (single file + full suite), lint, dev server, and deploy commands.
  - Format as a simple reference list.
  
  ### Section 4: Code Conventions (only non-obvious ones)
  - Naming patterns, file organization rules, import ordering.
  - Skip anything a linter/formatter already enforces automatically.
  
  ### Section 5: Gotchas & Warnings
  - Project-specific traps and quirks.
  - Things Claude tends to get wrong in this type of project.
  - Known workarounds or fragile areas of the codebase.
  
  ### Section 6: Git & Workflow
  - Branch naming, commit message format, PR process.
  - Only include if the team has specific conventions.
  
  ### Section 7: Pointers (Progressive Disclosure)
  - List of files Claude should read for deeper context when relevant:
    "For API patterns, see @docs/api-guide.md"
    "For DB migrations, see @prisma/README.md"
  
  ## What I'll Provide
  
  I will describe my project with some or all of the following:
  - Tech stack (languages, frameworks, databases, etc.)
  - Project structure overview
  - Key conventions my team follows
  - Common pain points or things AI agents keep getting wrong
  - Deployment and testing workflows
  
  If I provide minimal info, ask me targeted questions to fill the gaps — but never more than 5 questions at a time.
  
  ## Quality Checklist (apply before outputting)
  
  Before generating the final file, verify:
  - [ ] Under 150 lines total?
  - [ ] No generic advice that any dev would already know?
  - [ ] Every "don't do X" has a "do Y instead"?
  - [ ] Test/build/lint commands are included?
  - [ ] No @-file imports that embed entire files (use "see path" instead)?
  - [ ] IMPORTANT/MUST used at most 2-3 times?
  - [ ] Would a new team member AND an AI agent both benefit from this file?
  
  Now ask me about my project, or generate a CLAUDE.md if I've already provided enough detail.

• 9.Aaa

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  ROLE: Senior Node.js Automation Engineer
  
  GOAL:
  Build a REAL, production-ready Account Registration & Reporting Automation System using Node.js.
  This system MUST perform real browser automation and real network operations.
  NO simulation, NO mock data, NO placeholders, NO pseudo-code.
  
  SIMULATION POLICY:
  NEVER simulate anything.
  NEVER generate fake outputs.
  NEVER use dummy services.
  All logic must be executable and functional.
  
  TECH STACK:
  - Node.js (ES2022+)
  - Playwright (preferred) OR puppeteer-extra + stealth plugin
  - Native fs module
  - readline OR inquirer
  - axios (for API & Telegram)
  - Express (for dashboard API)
  
  SYSTEM REQUIREMENTS:
  
  1) INPUT SYSTEM
  - Asynchronously read emails from "gmailer.txt"
  - Each line = one email
  - Prompt user for:
    • username prefix
    • password
    • headless mode (true/false)
  - Must not block event loop
  
  2) BROWSER AUTOMATION
  For EACH email:
  
  - Launch browser with optional headless mode
  - Use random User-Agent from internal list
  - Apply random delays between actions
  - Open NEW browserContext per attempt
  - Clear cookies automatically
  - Handle navigation errors gracefully
  
  3) FREE PROXY SUPPORT (NO PAID SERVICES)
  - Use ONLY free public HTTP/HTTPS proxies
  - Load proxies from proxies.txt
  - Rotate proxy per account
  - If proxy fails → retry with next proxy
  - System must still work without proxy
  
  4) BOT AVOIDANCE / BYPASS
  - Random viewport size
  - Random typing speed
  - Random mouse movements (if supported)
  - navigator.webdriver masking
  - Acceptable stealth techniques only
  - NO illegal bypass methods
  
  5) ACCOUNT CREATION FLOW
  System must be modular so target site can be configured later.
  
  Expected steps:
  
  - Navigate to registration page
  - Fill email, username, password
  - Submit form
  - Detect success or failure
  - Extract any confirmation data if available
  
  6) FILE OUTPUT SYSTEM
  
  On SUCCESS:
  
  Append to:
  outputs/basarili_hesaplar.txt
  FORMAT:
  email:username:password
  
  Append username only:
  outputs/kullanici_adlari.txt
  
  Append password only:
  outputs/sifreler.txt
  
  On FAILURE:
  
  Append to:
  logs/error_log.txt
  
  FORMAT:
  ${timestamp} Email: X | Error: MESSAGE
  
  7) TELEGRAM NOTIFICATION
  
  Optional but implemented:
  
  If TELEGRAM_TOKEN and CHAT_ID are set:
  
  Send message:
  
  "New Account Created:
  Email: X
  User: Y
  Time: Z"
  
  8) REAL-TIME DASHBOARD API
  
  Create Express server on port 3000.
  
  Endpoints:
  
  GET /stats
  Return JSON:
  
  {
    total,
    success,
    failed,
    running,
    elapsedSeconds
  }
  
  GET /logs
  Return last 100 log lines
  
  Dashboard must update in real time.
  
  9) FINAL CONSOLE REPORT
  
  After all emails processed:
  
  Display console.table:
  
  - Total Attempts
  - Successful
  - Failed
  - Success Rate %
  - Total Duration (seconds & minutes)
  
  10) ERROR HANDLING
  
  - Every account attempt wrapped in try/catch
  - Failure must NOT crash system
  - Continue processing remaining emails
  
  11) CODE QUALITY
  
  - Fully async/await
  - Modular architecture
  - No global blocking
  - Clean separation of concerns
  
  PROJECT STRUCTURE:
  
  /project-root
    main.js
    gmailer.txt
    proxies.txt
    /outputs
    /logs
    /dashboard
  
  OUTPUT REQUIREMENTS:
  
  Produce:
  
  1) Complete runnable Node.js code
  2) package.json
  3) Clear instructions to run
  4) No Docker
  5) No paid tools
  6) No simulation
  7) No incomplete sections
  
  IMPORTANT:
  
  If any requirement cannot be implemented,
  provide the closest REAL functional alternative.
  
  Do NOT ask questions.
  Do NOT generate explanations only.
  Generate FULL WORKING CODE.

1. Step 1

   Pick a prompt

   Start with “Claude Code Statusline Design”, or scan the 9 prompts below for the one that matches your task.

2. Step 2

   Copy it

   Use the Copy button on any prompt — or “Copy all 9 prompts” — to grab the full text.

3. Step 3

   Fill in the blanks

   Swap the [bracketed] placeholders for your own details before you run it.

4. Step 4

   Run and refine

   Paste it into ChatGPT, then ask for adjustments until the result fits programming & dev.

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