feat: add comprehensive environment and trending analysis prompt templates
Environment Analysis Templates:
- analyze_environment_dependencies: Analyze current Python environment and dependencies
- check_outdated_packages: Check for outdated packages with update priorities
- generate_update_plan: Create comprehensive package update plans with strategies
Trending Analysis Templates:
- analyze_daily_trends: Analyze daily PyPI download trends and popular packages
- find_trending_packages: Discover trending packages over different time periods
- track_package_updates: Track recent package updates and releases
Key Features:
- Follow standard MCP workflow with {{parameter}} template variables
- Support environment analysis (uvx pip list integration ready)
- Enable trending package discovery and popularity analysis
- Provide structured update planning with different strategies
- Include comprehensive parameter validation and documentation
- Add usage examples and integration guides
All templates follow the established MCP prompt workflow:
1. User calls tool → MCP client sends request
2. Tool function executes → Collects necessary data and parameters
3. Call Prompt generator → Pass parameters to corresponding generator
4. Load template → Get template with {{parameter}} placeholders
5. Parameter replacement → Replace {{parameter_name}} with actual values
6. Return final prompt → As tool's response back to AI
Updated documentation and README with new template examples and usage patterns.
Signed-off-by: longhao <hal.long@outlook.com>
This commit is contained in:
longhao
Hal
parent
4bdf38d455
commit
d63ef02ef3
@ -186,6 +186,124 @@ Generate a detailed migration checklist prompt.
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}
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```
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### Environment Analysis Templates
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#### 9. `analyze_environment_dependencies`
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Generate a prompt for analyzing current environment dependencies.
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**Parameters:**
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- `environment_type` (optional): Type of environment (local, virtual, docker, conda)
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- `python_version` (optional): Python version in the environment
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- `project_path` (optional): Path to the project directory
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**Use Case:** When you need to analyze your current Python environment and check for outdated packages.
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**Example:**
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```json
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{
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"environment_type": "virtual",
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"python_version": "3.11",
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"project_path": "/path/to/project"
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}
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```
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#### 10. `check_outdated_packages`
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Generate a prompt for checking outdated packages with update priorities.
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**Parameters:**
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- `package_filter` (optional): Filter packages by name pattern
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- `severity_level` (optional): Focus level (all, security, major, minor)
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- `include_dev_dependencies` (optional): Include development dependencies
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**Use Case:** When you want to identify and prioritize package updates.
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**Example:**
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```json
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{
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"package_filter": "django*",
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"severity_level": "security",
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"include_dev_dependencies": true
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}
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```
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#### 11. `generate_update_plan`
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Generate a prompt for creating comprehensive package update plans.
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**Parameters:**
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- `update_strategy` (optional): Update strategy (conservative, balanced, aggressive)
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- `environment_constraints` (optional): Environment constraints or requirements
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- `testing_requirements` (optional): Testing requirements before updates
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**Use Case:** When you need a structured plan for updating packages in your environment.
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**Example:**
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```json
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{
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"update_strategy": "balanced",
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"environment_constraints": "Production environment, zero downtime required",
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"testing_requirements": "Full test suite + integration tests"
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}
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```
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### Trending Analysis Templates
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#### 12. `analyze_daily_trends`
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Generate a prompt for analyzing daily PyPI download trends.
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**Parameters:**
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- `date` (optional): Specific date to analyze (YYYY-MM-DD) or 'today'
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- `category` (optional): Package category to focus on (web, data, ml, etc.)
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- `limit` (optional): Number of top packages to analyze (5-50)
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**Use Case:** When you want to understand what packages are trending on PyPI.
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**Example:**
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```json
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{
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"date": "today",
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"category": "machine-learning",
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"limit": 20
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}
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```
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#### 13. `find_trending_packages`
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Generate a prompt for discovering trending packages over time periods.
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**Parameters:**
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- `time_period` (optional): Time period for trend analysis (daily, weekly, monthly)
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- `trend_type` (optional): Type of trends to focus on (rising, declining, new, all)
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- `domain` (optional): Specific domain or category (web, ai, data, etc.)
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**Use Case:** When you want to discover packages that are gaining or losing popularity.
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**Example:**
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```json
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{
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"time_period": "weekly",
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"trend_type": "rising",
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"domain": "web-development"
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}
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```
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#### 14. `track_package_updates`
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Generate a prompt for tracking recent package updates and releases.
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**Parameters:**
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- `time_range` (optional): Time range for update tracking (today, week, month)
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- `update_type` (optional): Type of updates to track (all, major, security, new)
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- `popular_only` (optional): Focus only on popular packages (>1M downloads)
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**Use Case:** When you want to stay informed about recent package updates and releases.
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**Example:**
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```json
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{
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"time_range": "week",
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"update_type": "security",
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"popular_only": true
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}
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```
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## 🚀 Usage Examples
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### In Claude Desktop
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22
README.md
22
README.md
@ -217,6 +217,16 @@ The server provides the following MCP tools:
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17. **plan_package_migration** - Generate comprehensive package migration plan prompts
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18. **generate_migration_checklist** - Generate detailed migration checklist prompts
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### Environment Analysis Templates
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19. **analyze_environment_dependencies** - Generate prompts for analyzing current environment dependencies
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20. **check_outdated_packages** - Generate prompts for checking outdated packages with update priorities
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21. **generate_update_plan** - Generate prompts for creating comprehensive package update plans
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### Trending Analysis Templates
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22. **analyze_daily_trends** - Generate prompts for analyzing daily PyPI download trends
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23. **find_trending_packages** - Generate prompts for discovering trending packages over time periods
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24. **track_package_updates** - Generate prompts for tracking recent package updates and releases
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> 📖 **Learn more about prompt templates**: See [PROMPT_TEMPLATES.md](PROMPT_TEMPLATES.md) for detailed documentation and examples.
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## Usage Examples
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@ -254,6 +264,18 @@ Once configured in your MCP client (Claude Desktop, Cline, Cursor, Windsurf), yo
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- "Help me resolve dependency conflicts with a structured prompt"
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- "Generate a security audit prompt for my production packages"
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### Environment Analysis
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- "Analyze my current Python environment dependencies and check for outdated packages"
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- "Check which packages in my environment have security updates available"
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- "Generate an update plan for my production environment with conservative strategy"
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- "Help me identify packages that need immediate updates vs. planned updates"
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### Trending Analysis
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- "What are the most downloaded Python packages today?"
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- "Show me trending packages in the machine learning domain this week"
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- "Track recent security updates and new package releases"
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- "Find rising packages in web development that I should consider"
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### Example Conversations
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**User**: "Check if Django 4.2 is compatible with Python 3.9"
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@ -9,6 +9,11 @@ from .dependency_management import (
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plan_version_upgrade,
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resolve_dependency_conflicts,
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)
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from .environment_analysis import (
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analyze_environment_dependencies,
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check_outdated_packages,
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generate_update_plan,
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)
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from .migration_guidance import (
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generate_migration_checklist,
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plan_package_migration,
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@ -18,6 +23,11 @@ from .package_analysis import (
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compare_packages,
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suggest_alternatives,
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)
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from .trending_analysis import (
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analyze_daily_trends,
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find_trending_packages,
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track_package_updates,
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)
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__all__ = [
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# Package Analysis
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@ -28,7 +38,15 @@ __all__ = [
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"resolve_dependency_conflicts",
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"plan_version_upgrade",
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"audit_security_risks",
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# Environment Analysis
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"analyze_environment_dependencies",
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"check_outdated_packages",
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"generate_update_plan",
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# Migration Guidance
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"plan_package_migration",
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"generate_migration_checklist",
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# Trending Analysis
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"analyze_daily_trends",
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"find_trending_packages",
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"track_package_updates",
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]
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291
pypi_query_mcp/prompts/environment_analysis.py
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291
pypi_query_mcp/prompts/environment_analysis.py
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@ -0,0 +1,291 @@
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"""Environment analysis prompt templates for PyPI MCP server."""
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from typing import Annotated
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from fastmcp import Context
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from pydantic import Field
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class Message:
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"""Simple message class for prompt templates."""
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def __init__(self, text: str, role: str = "user"):
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self.text = text
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self.role = role
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async def analyze_environment_dependencies(
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environment_type: Annotated[
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str,
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Field(description="Type of environment (local, virtual, docker, conda)")
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] = "local",
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python_version: Annotated[
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str | None,
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Field(description="Python version in the environment")
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] = None,
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project_path: Annotated[
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str | None,
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Field(description="Path to the project directory")
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] = None,
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ctx: Context | None = None,
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) -> str:
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"""Generate a prompt template for analyzing environment dependencies.
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This prompt template helps analyze the current Python environment dependencies,
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check for outdated packages, and provide upgrade recommendations.
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Returns a template string with {{environment_type}}, {{python_version}}, and {{project_path}} variables.
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"""
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template = """Please analyze the Python environment dependencies {{environment_info}}.
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## 🔍 Environment Analysis Request
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I need to analyze my current Python environment to understand:
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### Current Environment Status
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- List all installed packages and their versions (use `{{command_prefix}}pip list`)
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- Identify the Python version and environment type
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- Check for any conflicting or problematic installations
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### Package Version Analysis
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- Compare installed versions with latest available on PyPI
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- Identify outdated packages that have newer versions
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- Highlight packages with security updates available
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- Check for packages with major version updates
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### Dependency Health Check
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- Analyze dependency relationships and conflicts
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- Identify unused or redundant packages
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- Check for packages with known vulnerabilities
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- Assess overall environment health
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## 📊 Detailed Analysis Framework
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### For Each Package, Provide:
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1. **Current vs Latest Version**
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- Installed version
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- Latest stable version on PyPI
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- Version gap analysis (patch/minor/major updates)
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2. **Update Priority Assessment**
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- Security updates (HIGH priority)
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- Bug fixes and stability improvements (MEDIUM priority)
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- New features and enhancements (LOW priority)
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3. **Compatibility Impact**
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- Breaking changes in newer versions
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- Dependency chain effects
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- Potential conflicts with other packages
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### Environment Optimization Recommendations
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- Packages safe to update immediately
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- Packages requiring careful testing before update
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- Packages to avoid updating (due to breaking changes)
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- Cleanup recommendations for unused packages
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## 🚀 Action Plan
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Provide a prioritized action plan with:
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- Immediate updates (security and critical fixes)
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- Planned updates (with testing requirements)
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- Long-term upgrade strategy
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- Environment maintenance best practices
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Please include specific commands for package management and update procedures."""
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return template
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async def check_outdated_packages(
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package_filter: Annotated[
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str | None,
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Field(description="Filter packages by name pattern (optional)")
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] = None,
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severity_level: Annotated[
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str,
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Field(description="Focus level: all, security, major, minor")
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] = "all",
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include_dev_dependencies: Annotated[
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bool,
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Field(description="Include development dependencies in analysis")
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] = True,
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ctx: Context | None = None,
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) -> str:
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"""Generate a prompt template for checking outdated packages.
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This prompt template helps identify and prioritize outdated packages
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in the current environment with specific focus criteria.
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Returns a template string with {{package_filter}}, {{severity_level}}, and {{dev_deps}} variables.
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"""
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template = """Please check for outdated packages in my Python environment {{filter_info}}.
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## 🔍 Outdated Package Analysis
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Focus on {{severity_level}} updates{{dev_deps_text}}.
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### Analysis Scope
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- Check all installed packages against PyPI latest versions
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- Identify packages with available updates
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- Categorize updates by severity and importance
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- Assess update risks and benefits
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## 📋 Update Categories
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### 🚨 Security Updates (Critical)
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- Packages with known security vulnerabilities
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- CVE fixes and security patches
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- Immediate action required packages
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### 🔧 Bug Fixes & Stability (Important)
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- Critical bug fixes
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- Stability improvements
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- Performance enhancements
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### ✨ Feature Updates (Optional)
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- New features and capabilities
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- API improvements
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- Non-breaking enhancements
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### ⚠️ Major Version Updates (Careful)
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- Breaking changes
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- API modifications
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- Requires thorough testing
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## 📊 For Each Outdated Package, Provide:
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1. **Version Information**
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- Current version installed
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- Latest available version
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- Release date of latest version
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- Version type (patch/minor/major)
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2. **Update Assessment**
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- Change log highlights
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- Breaking changes (if any)
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- Security implications
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- Dependency impact
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3. **Recommendation**
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- Update priority (High/Medium/Low)
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- Testing requirements
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- Rollback considerations
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- Best update timing
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## 🎯 Prioritized Update Plan
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Create a step-by-step update plan:
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1. **Immediate Updates** (security and critical fixes)
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2. **Planned Updates** (important improvements)
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3. **Future Considerations** (major version upgrades)
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4. **Monitoring Setup** (track future updates)
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Include specific pip/uv commands for each update category."""
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return template
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async def generate_update_plan(
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update_strategy: Annotated[
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str,
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Field(description="Update strategy: conservative, balanced, aggressive")
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] = "balanced",
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environment_constraints: Annotated[
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str | None,
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Field(description="Environment constraints or requirements")
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] = None,
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testing_requirements: Annotated[
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str | None,
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Field(description="Testing requirements before updates")
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] = None,
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ctx: Context | None = None,
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) -> str:
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"""Generate a prompt template for creating package update plans.
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This prompt template helps create comprehensive update plans for Python environments
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with specific strategies and constraints.
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Returns a template string with {{strategy}}, {{constraints}}, and {{testing}} variables.
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"""
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template = """Please create a comprehensive package update plan using a {{strategy}} strategy{{constraints_text}}{{testing_text}}.
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## 🎯 Update Strategy: {{strategy}}
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### Strategy Guidelines
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- **Conservative**: Only security and critical bug fixes
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- **Balanced**: Security fixes + stable improvements + selected features
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- **Aggressive**: Latest versions with careful testing
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## 📋 Update Plan Framework
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### Phase 1: Pre-Update Assessment
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1. **Environment Backup**
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- Create requirements.txt snapshot
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- Document current working state
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- Set up rollback procedures
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2. **Dependency Analysis**
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- Map dependency relationships
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- Identify potential conflicts
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- Plan update order
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3. **Risk Assessment**
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- Categorize packages by update risk
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- Identify critical dependencies
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- Plan testing scope
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### Phase 2: Staged Update Execution
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#### Stage 1: Critical Security Updates
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- Packages with known vulnerabilities
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- Zero-day fixes and security patches
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- Immediate deployment candidates
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#### Stage 2: Stability Improvements
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- Bug fixes and performance improvements
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- Compatibility updates
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- Low-risk enhancements
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#### Stage 3: Feature Updates
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- New functionality additions
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- API improvements
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- Non-breaking enhancements
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#### Stage 4: Major Version Updates
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- Breaking changes requiring code updates
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- Comprehensive testing required
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- Gradual rollout recommended
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### Phase 3: Validation & Monitoring
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#### Testing Protocol
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- Unit test execution
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- Integration testing
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- Performance regression testing
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- User acceptance testing
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#### Deployment Strategy
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- Development environment first
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- Staging environment validation
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- Production deployment with monitoring
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- Rollback procedures ready
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## 🔧 Implementation Commands
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Provide specific commands for:
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1. **Environment preparation**
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2. **Package updates by category**
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3. **Testing and validation**
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4. **Rollback procedures**
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## 📊 Success Metrics
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Define success criteria:
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- All tests passing
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- No performance degradation
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- Security vulnerabilities addressed
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- Functionality maintained
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Include monitoring setup for ongoing package management."""
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return template
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363
pypi_query_mcp/prompts/trending_analysis.py
Normal file
363
pypi_query_mcp/prompts/trending_analysis.py
Normal file
@ -0,0 +1,363 @@
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"""Trending analysis prompt templates for PyPI MCP server."""
|
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|
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from typing import Annotated, Literal
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|
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from fastmcp import Context
|
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from pydantic import Field
|
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|
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|
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class Message:
|
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"""Simple message class for prompt templates."""
|
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|
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def __init__(self, text: str, role: str = "user"):
|
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self.text = text
|
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self.role = role
|
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|
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|
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async def analyze_daily_trends(
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date: Annotated[
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||||
str | None,
|
||||
Field(description="Specific date to analyze (YYYY-MM-DD) or 'today'")
|
||||
] = "today",
|
||||
category: Annotated[
|
||||
str | None,
|
||||
Field(description="Package category to focus on (web, data, ml, etc.)")
|
||||
] = None,
|
||||
limit: Annotated[
|
||||
int,
|
||||
Field(description="Number of top packages to analyze", ge=5, le=50)
|
||||
] = 20,
|
||||
ctx: Context | None = None,
|
||||
) -> str:
|
||||
"""Generate a prompt template for analyzing daily PyPI trends.
|
||||
|
||||
This prompt template helps analyze the most downloaded packages on PyPI
|
||||
for a specific day and understand trending patterns.
|
||||
|
||||
Returns a template string with {{date}}, {{category_filter}}, and {{limit}} variables.
|
||||
"""
|
||||
template = """Please analyze the daily PyPI download trends for {{date}}{{category_filter}}.
|
||||
|
||||
## 📊 Daily PyPI Trends Analysis
|
||||
|
||||
Show me the top {{limit}} most downloaded Python packages and provide insights into current trends.
|
||||
|
||||
### Download Statistics Analysis
|
||||
- **Top Downloaded Packages**: List the most popular packages by download count
|
||||
- **Download Numbers**: Specific download counts for each package
|
||||
- **Growth Patterns**: Compare with previous days/weeks if possible
|
||||
- **Market Share**: Relative popularity within the ecosystem
|
||||
|
||||
## 🔍 Trend Analysis Framework
|
||||
|
||||
### For Each Top Package, Analyze:
|
||||
|
||||
1. **Package Overview**
|
||||
- Package name and primary purpose
|
||||
- Current version and release status
|
||||
- Maintainer and community info
|
||||
|
||||
2. **Download Metrics**
|
||||
- Daily download count
|
||||
- Weekly/monthly trends (if available)
|
||||
- Growth rate and momentum
|
||||
- Geographic distribution (if available)
|
||||
|
||||
3. **Ecosystem Context**
|
||||
- Category/domain (web, data science, ML, etc.)
|
||||
- Competing packages in same space
|
||||
- Integration with other popular packages
|
||||
- Enterprise vs. individual usage patterns
|
||||
|
||||
### Trending Insights
|
||||
|
||||
#### 🚀 Rising Stars
|
||||
- Packages with significant growth
|
||||
- New packages gaining traction
|
||||
- Emerging technologies and frameworks
|
||||
|
||||
#### 📈 Steady Leaders
|
||||
- Consistently popular packages
|
||||
- Foundational libraries and tools
|
||||
- Mature ecosystem components
|
||||
|
||||
#### 📉 Declining Trends
|
||||
- Packages losing popularity
|
||||
- Potential reasons for decline
|
||||
- Alternative packages gaining ground
|
||||
|
||||
## 🎯 Market Intelligence
|
||||
|
||||
### Technology Trends
|
||||
- What technologies are developers adopting?
|
||||
- Which frameworks are gaining momentum?
|
||||
- What problem domains are hot?
|
||||
|
||||
### Developer Behavior
|
||||
- Package selection patterns
|
||||
- Adoption speed of new technologies
|
||||
- Community preferences and choices
|
||||
|
||||
### Ecosystem Health
|
||||
- Diversity of popular packages
|
||||
- Innovation vs. stability balance
|
||||
- Open source project vitality
|
||||
|
||||
## 📋 Actionable Insights
|
||||
|
||||
Provide recommendations for:
|
||||
- **Developers**: Which packages to consider for new projects
|
||||
- **Maintainers**: Opportunities for package improvement
|
||||
- **Organizations**: Technology adoption strategies
|
||||
- **Investors**: Emerging technology trends
|
||||
|
||||
Include specific download numbers, growth percentages, and trend analysis."""
|
||||
|
||||
return template
|
||||
|
||||
|
||||
async def find_trending_packages(
|
||||
time_period: Annotated[
|
||||
Literal["daily", "weekly", "monthly"],
|
||||
Field(description="Time period for trend analysis")
|
||||
] = "weekly",
|
||||
trend_type: Annotated[
|
||||
Literal["rising", "declining", "new", "all"],
|
||||
Field(description="Type of trends to focus on")
|
||||
] = "rising",
|
||||
domain: Annotated[
|
||||
str | None,
|
||||
Field(description="Specific domain or category (web, ai, data, etc.)")
|
||||
] = None,
|
||||
ctx: Context | None = None,
|
||||
) -> str:
|
||||
"""Generate a prompt template for finding trending packages.
|
||||
|
||||
This prompt template helps identify packages that are trending up or down
|
||||
in the PyPI ecosystem over specific time periods.
|
||||
|
||||
Returns a template string with {{time_period}}, {{trend_type}}, and {{domain_filter}} variables.
|
||||
"""
|
||||
template = """Please identify {{trend_type}} trending Python packages over the {{time_period}} period{{domain_filter}}.
|
||||
|
||||
## 📈 Trending Package Discovery
|
||||
|
||||
Focus on packages showing significant {{trend_type}} trends in downloads and adoption.
|
||||
|
||||
### Trend Analysis Criteria
|
||||
|
||||
#### For {{trend_type}} Packages:
|
||||
- **Rising**: Packages with increasing download velocity
|
||||
- **Declining**: Packages losing popularity or downloads
|
||||
- **New**: Recently published packages gaining traction
|
||||
- **All**: Comprehensive trend analysis across categories
|
||||
|
||||
### Time Period: {{time_period}}
|
||||
- **Daily**: Last 24-48 hours trend analysis
|
||||
- **Weekly**: 7-day trend patterns and changes
|
||||
- **Monthly**: 30-day trend analysis and momentum
|
||||
|
||||
## 🔍 Discovery Framework
|
||||
|
||||
### Trend Identification Metrics
|
||||
1. **Download Growth Rate**
|
||||
- Percentage increase/decrease in downloads
|
||||
- Velocity of change (acceleration/deceleration)
|
||||
- Consistency of trend direction
|
||||
|
||||
2. **Community Engagement**
|
||||
- GitHub stars and forks growth
|
||||
- Issue activity and resolution
|
||||
- Community discussions and mentions
|
||||
|
||||
3. **Release Activity**
|
||||
- Recent version releases
|
||||
- Update frequency and quality
|
||||
- Feature development pace
|
||||
|
||||
### For Each Trending Package, Provide:
|
||||
|
||||
#### 📊 Trend Metrics
|
||||
- Current download numbers
|
||||
- Growth/decline percentage
|
||||
- Trend duration and stability
|
||||
- Comparison with similar packages
|
||||
|
||||
#### 🔍 Package Analysis
|
||||
- **Purpose and Functionality**: What problem does it solve?
|
||||
- **Target Audience**: Who is using this package?
|
||||
- **Unique Value Proposition**: Why is it trending?
|
||||
- **Competition Analysis**: How does it compare to alternatives?
|
||||
|
||||
#### 🚀 Trend Drivers
|
||||
- **Technology Shifts**: New frameworks or paradigms
|
||||
- **Community Events**: Conferences, tutorials, viral content
|
||||
- **Industry Adoption**: Enterprise or startup usage
|
||||
- **Integration Opportunities**: Works well with popular tools
|
||||
|
||||
## 🎯 Trend Categories
|
||||
|
||||
### 🌟 Breakout Stars
|
||||
- New packages with explosive growth
|
||||
- Innovative solutions to common problems
|
||||
- Next-generation tools and frameworks
|
||||
|
||||
### 📈 Steady Climbers
|
||||
- Consistent growth over time
|
||||
- Building solid user base
|
||||
- Proven value and reliability
|
||||
|
||||
### ⚡ Viral Hits
|
||||
- Sudden popularity spikes
|
||||
- Social media or community driven
|
||||
- May need sustainability assessment
|
||||
|
||||
### 🔄 Comeback Stories
|
||||
- Previously popular packages regaining traction
|
||||
- Major updates or improvements
|
||||
- Community revival efforts
|
||||
|
||||
## 📋 Strategic Insights
|
||||
|
||||
### For Developers
|
||||
- Which trending packages to evaluate for projects
|
||||
- Early adoption opportunities and risks
|
||||
- Technology direction indicators
|
||||
|
||||
### For Package Maintainers
|
||||
- Competitive landscape changes
|
||||
- Opportunities for collaboration
|
||||
- Feature gaps in trending solutions
|
||||
|
||||
### For Organizations
|
||||
- Technology investment directions
|
||||
- Skill development priorities
|
||||
- Strategic technology partnerships
|
||||
|
||||
Include specific trend data, growth metrics, and actionable recommendations."""
|
||||
|
||||
return template
|
||||
|
||||
|
||||
async def track_package_updates(
|
||||
time_range: Annotated[
|
||||
Literal["today", "week", "month"],
|
||||
Field(description="Time range for update tracking")
|
||||
] = "today",
|
||||
update_type: Annotated[
|
||||
Literal["all", "major", "security", "new"],
|
||||
Field(description="Type of updates to track")
|
||||
] = "all",
|
||||
popular_only: Annotated[
|
||||
bool,
|
||||
Field(description="Focus only on popular packages (>1M downloads)")
|
||||
] = False,
|
||||
ctx: Context | None = None,
|
||||
) -> str:
|
||||
"""Generate a prompt template for tracking recent package updates.
|
||||
|
||||
This prompt template helps track and analyze recent package updates
|
||||
on PyPI with filtering and categorization options.
|
||||
|
||||
Returns a template string with {{time_range}}, {{update_type}}, and {{popularity_filter}} variables.
|
||||
"""
|
||||
template = """Please track and analyze Python package updates from {{time_range}}{{popularity_filter}}.
|
||||
|
||||
## 📦 Package Update Tracking
|
||||
|
||||
Focus on {{update_type}} updates and provide insights into recent changes in the Python ecosystem.
|
||||
|
||||
### Update Analysis Scope
|
||||
- **Time Range**: {{time_range}}
|
||||
- **Update Type**: {{update_type}} updates
|
||||
- **Package Selection**: {{popularity_description}}
|
||||
|
||||
## 🔍 Update Categories
|
||||
|
||||
### 🚨 Security Updates
|
||||
- CVE fixes and security patches
|
||||
- Vulnerability remediation
|
||||
- Security-related improvements
|
||||
|
||||
### 🎯 Major Version Updates
|
||||
- Breaking changes and API modifications
|
||||
- New features and capabilities
|
||||
- Architecture improvements
|
||||
|
||||
### 🔧 Minor Updates & Bug Fixes
|
||||
- Bug fixes and stability improvements
|
||||
- Performance enhancements
|
||||
- Compatibility updates
|
||||
|
||||
### 🌟 New Package Releases
|
||||
- Brand new packages published
|
||||
- First stable releases (1.0.0)
|
||||
- Emerging tools and libraries
|
||||
|
||||
## 📊 For Each Update, Provide:
|
||||
|
||||
### Update Details
|
||||
1. **Package Information**
|
||||
- Package name and description
|
||||
- Previous version → New version
|
||||
- Release date and timing
|
||||
|
||||
2. **Change Analysis**
|
||||
- Key changes and improvements
|
||||
- Breaking changes (if any)
|
||||
- New features and capabilities
|
||||
- Bug fixes and security patches
|
||||
|
||||
3. **Impact Assessment**
|
||||
- Who should update and when
|
||||
- Compatibility considerations
|
||||
- Testing requirements
|
||||
- Migration effort (for major updates)
|
||||
|
||||
### Ecosystem Impact
|
||||
- **Dependency Effects**: How updates affect dependent packages
|
||||
- **Community Response**: Developer adoption and feedback
|
||||
- **Integration Impact**: Effects on popular development stacks
|
||||
|
||||
## 🎯 Update Insights
|
||||
|
||||
### 🔥 Notable Updates
|
||||
- Most significant updates of the period
|
||||
- High-impact changes for developers
|
||||
- Security-critical updates requiring immediate attention
|
||||
|
||||
### 📈 Trend Patterns
|
||||
- Which types of updates are most common
|
||||
- Package maintenance activity levels
|
||||
- Ecosystem health indicators
|
||||
|
||||
### ⚠️ Breaking Changes Alert
|
||||
- Major version updates with breaking changes
|
||||
- Migration guides and resources
|
||||
- Timeline recommendations for updates
|
||||
|
||||
### 🌟 Innovation Highlights
|
||||
- New features and capabilities
|
||||
- Emerging patterns and technologies
|
||||
- Developer experience improvements
|
||||
|
||||
## 📋 Action Recommendations
|
||||
|
||||
### Immediate Actions
|
||||
- Critical security updates to apply now
|
||||
- High-priority bug fixes
|
||||
- Compatibility updates needed
|
||||
|
||||
### Planned Updates
|
||||
- Major version upgrades requiring testing
|
||||
- Feature updates worth evaluating
|
||||
- Performance improvements to consider
|
||||
|
||||
### Monitoring Setup
|
||||
- Packages to watch for future updates
|
||||
- Automated update strategies
|
||||
- Dependency management improvements
|
||||
|
||||
Include specific version numbers, release notes highlights, and update commands."""
|
||||
|
||||
return template
|
||||
@ -8,14 +8,20 @@ from fastmcp import FastMCP
|
||||
|
||||
from .core.exceptions import InvalidPackageNameError, NetworkError, PackageNotFoundError
|
||||
from .prompts import (
|
||||
analyze_daily_trends,
|
||||
analyze_environment_dependencies,
|
||||
analyze_package_quality,
|
||||
audit_security_risks,
|
||||
check_outdated_packages,
|
||||
compare_packages,
|
||||
find_trending_packages,
|
||||
generate_migration_checklist,
|
||||
generate_update_plan,
|
||||
plan_package_migration,
|
||||
plan_version_upgrade,
|
||||
resolve_dependency_conflicts,
|
||||
suggest_alternatives,
|
||||
track_package_updates,
|
||||
)
|
||||
from .tools import (
|
||||
check_python_compatibility,
|
||||
@ -715,6 +721,177 @@ async def generate_migration_checklist_prompt(
|
||||
return messages[0].text
|
||||
|
||||
|
||||
# Environment Analysis Prompts
|
||||
@mcp.prompt()
|
||||
async def analyze_environment_dependencies_prompt(
|
||||
environment_type: str = "local",
|
||||
python_version: str | None = None,
|
||||
project_path: str | None = None
|
||||
) -> str:
|
||||
"""Generate a prompt for analyzing environment dependencies."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await analyze_environment_dependencies(environment_type, python_version, project_path)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{environment_type}}", environment_type)
|
||||
|
||||
# Handle environment info
|
||||
env_info = f"({environment_type} environment)"
|
||||
if python_version:
|
||||
env_info += f" with Python {python_version}"
|
||||
if project_path:
|
||||
env_info += f" at {project_path}"
|
||||
result = result.replace("{{environment_info}}", env_info)
|
||||
|
||||
# Handle command prefix based on environment
|
||||
command_prefix = "uvx " if environment_type in ["virtual", "uv"] else ""
|
||||
result = result.replace("{{command_prefix}}", command_prefix)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
@mcp.prompt()
|
||||
async def check_outdated_packages_prompt(
|
||||
package_filter: str | None = None,
|
||||
severity_level: str = "all",
|
||||
include_dev_dependencies: bool = True
|
||||
) -> str:
|
||||
"""Generate a prompt for checking outdated packages."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await check_outdated_packages(package_filter, severity_level, include_dev_dependencies)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{severity_level}}", severity_level)
|
||||
|
||||
# Handle filter info
|
||||
if package_filter:
|
||||
filter_info = f" (filtering by: {package_filter})"
|
||||
else:
|
||||
filter_info = ""
|
||||
result = result.replace("{{filter_info}}", filter_info)
|
||||
|
||||
# Handle dev dependencies
|
||||
if include_dev_dependencies:
|
||||
dev_deps_text = " including development dependencies"
|
||||
else:
|
||||
dev_deps_text = " excluding development dependencies"
|
||||
result = result.replace("{{dev_deps_text}}", dev_deps_text)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
@mcp.prompt()
|
||||
async def generate_update_plan_prompt(
|
||||
update_strategy: str = "balanced",
|
||||
environment_constraints: str | None = None,
|
||||
testing_requirements: str | None = None
|
||||
) -> str:
|
||||
"""Generate a prompt for creating package update plans."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await generate_update_plan(update_strategy, environment_constraints, testing_requirements)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{strategy}}", update_strategy)
|
||||
|
||||
# Handle constraints
|
||||
if environment_constraints:
|
||||
constraints_text = f"\n\nEnvironment constraints: {environment_constraints}"
|
||||
else:
|
||||
constraints_text = ""
|
||||
result = result.replace("{{constraints_text}}", constraints_text)
|
||||
|
||||
# Handle testing requirements
|
||||
if testing_requirements:
|
||||
testing_text = f"\n\nTesting requirements: {testing_requirements}"
|
||||
else:
|
||||
testing_text = ""
|
||||
result = result.replace("{{testing_text}}", testing_text)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
# Trending Analysis Prompts
|
||||
@mcp.prompt()
|
||||
async def analyze_daily_trends_prompt(
|
||||
date: str = "today",
|
||||
category: str | None = None,
|
||||
limit: int = 20
|
||||
) -> str:
|
||||
"""Generate a prompt for analyzing daily PyPI trends."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await analyze_daily_trends(date, category, limit)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{date}}", date)
|
||||
result = result.replace("{{limit}}", str(limit))
|
||||
|
||||
# Handle category filter
|
||||
if category:
|
||||
category_filter = f" focusing on {category} packages"
|
||||
else:
|
||||
category_filter = ""
|
||||
result = result.replace("{{category_filter}}", category_filter)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
@mcp.prompt()
|
||||
async def find_trending_packages_prompt(
|
||||
time_period: str = "weekly",
|
||||
trend_type: str = "rising",
|
||||
domain: str | None = None
|
||||
) -> str:
|
||||
"""Generate a prompt for finding trending packages."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await find_trending_packages(time_period, trend_type, domain)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{time_period}}", time_period)
|
||||
result = result.replace("{{trend_type}}", trend_type)
|
||||
|
||||
# Handle domain filter
|
||||
if domain:
|
||||
domain_filter = f" in the {domain} domain"
|
||||
else:
|
||||
domain_filter = ""
|
||||
result = result.replace("{{domain_filter}}", domain_filter)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
@mcp.prompt()
|
||||
async def track_package_updates_prompt(
|
||||
time_range: str = "today",
|
||||
update_type: str = "all",
|
||||
popular_only: bool = False
|
||||
) -> str:
|
||||
"""Generate a prompt for tracking recent package updates."""
|
||||
# Step 3: Call Prompt generator
|
||||
template = await track_package_updates(time_range, update_type, popular_only)
|
||||
|
||||
# Step 5: Parameter replacement
|
||||
result = template.replace("{{time_range}}", time_range)
|
||||
result = result.replace("{{update_type}}", update_type)
|
||||
|
||||
# Handle popularity filter
|
||||
if popular_only:
|
||||
popularity_filter = " (popular packages only)"
|
||||
popularity_description = "Popular packages with >1M downloads"
|
||||
else:
|
||||
popularity_filter = ""
|
||||
popularity_description = "All packages in the ecosystem"
|
||||
result = result.replace("{{popularity_filter}}", popularity_filter)
|
||||
result = result.replace("{{popularity_description}}", popularity_description)
|
||||
|
||||
# Step 7: Return final prompt
|
||||
return result
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option(
|
||||
"--log-level",
|
||||
|
||||
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Reference in New Issue
Block a user