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{
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"metadata": {
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{
"path": "analysis_results.json",
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{
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"purpose": "Defines the packaging and installation configuration for the 'build_influence' Python project using setuptools.",
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"path": ".gitignore",
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"Enhance parser for documentation and README extraction",
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"Generate LinkedIn announcement content for a new release and schedule it for publication",
"Run the CLI in interview mode to provide missing project context and customize content focus",
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"Configure approval workflow to require manual review for all LinkedIn posts"
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"path": ".cursor/rules/clean-code.mdc",
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"path": ".cursor/rules/tech-stack.mdc",
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"path": ".cursor/rules/tailwind.mdc",
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"path": ".cursor/rules/python.mdc",
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{
"path": ".cursor/rules/nextjs.mdc",
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{
"path": ".cursor/rules/cloudflare-worker-typescript.mdc",
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"path": "logs/build_influence.log",
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"path": "src/build_influence.egg-info/PKG-INFO",
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{
"path": "src/build_influence.egg-info/SOURCES.txt",
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},
{
"path": "src/build_influence.egg-info/entry_points.txt",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence.egg-info/entry_points.txt",
"size": 60,
"type": "documentation",
"ai_doc_insights": {
"summary": "Defines a console script entry point for running the build_influence CLI application.",
"features": [
"Provides a command-line interface via the 'build-influence' command."
],
"setup_steps": [
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],
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"build-influence --help"
]
}
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{
"path": "src/build_influence.egg-info/top_level.txt",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence.egg-info/top_level.txt",
"size": 16,
"type": "documentation",
"ai_doc_insights": {
"summary": "A function or script to build or calculate influence, likely in a graph or network context.",
"features": [
"Calculates or constructs influence structures",
"May process network or graph data",
"Possibly updates or annotates nodes/edges with influence values"
],
"setup_steps": [],
"usage_examples": []
}
},
{
"path": "src/build_influence.egg-info/dependency_links.txt",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence.egg-info/dependency_links.txt",
"size": 1,
"type": "documentation",
"ai_doc_insights": {
"error": "Empty file"
}
},
{
"path": "src/build_influence/__init__.py",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence/__init__.py",
"size": 35,
"type": "code",
"ai_code_insights": {
"purpose": "Serves as the main package initializer for Build Influence.",
"key_elements": [],
"dependencies": [],
"interesting_aspects": []
}
},
{
"path": "src/build_influence/cli.py",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence/cli.py",
"size": 8046,
"type": "code",
"ai_code_insights": {
"purpose": "Implements a command-line interface (CLI) for the Build Influence tool, allowing users to analyze code repositories, generate and publish content, configure settings, and view logs.",
"key_elements": [
"callback",
"analyze",
"generate",
"publish",
"configure",
"logs"
],
"dependencies": [
"import typer",
"from loguru import logger",
"import os",
"from typing_extensions import Annotated",
"import json",
"from build_influence.utils import setup_logging",
"from build_influence.config import config",
"from build_influence.analysis import RepositoryAnalyzer"
],
"interesting_aspects": [
"Uses Typer for declarative CLI definition and automatic help generation",
"Early logging setup via a @app.callback() function for global side effects",
"Dynamic loading and printing of AI-generated code/doc insights",
"Basic real-time log tailing (follow mode) implemented in Python",
"Type-annotated CLI arguments/options for enhanced clarity and validation"
]
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},
{
"path": "src/build_influence/analysis/feature_identifier.py",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence/analysis/feature_identifier.py",
"size": 8429,
"type": "code",
"ai_code_insights": {
"purpose": "Identifies and summarizes high-level technical features from code analysis results using an LLM, synthesizing context and parsing AI output robustly.",
"key_elements": [
"FeatureIdentifier",
"identify_features",
"_synthesize_context",
"_build_feature_prompt"
],
"dependencies": [
"from typing import Dict, Any, Optional",
"import litellm",
"import json",
"from loguru import logger",
"from build_influence.config import config",
"from build_influence.utils import setup_logging"
],
"interesting_aspects": [
"Robust handling and cleaning of LLM JSON responses, including markdown stripping and partial JSON parsing.",
"Synthesizes context with length limits and prioritizes most important file insights.",
"Uses dynamic prompt construction for LLM.",
"Handles LLM API errors and parsing exceptions gracefully.",
"Demonstrates concrete example usage for local testing."
]
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{
"path": "src/build_influence/analysis/analyzer.py",
"absolute_path": "/Users/drorivry/develop/build-influence/src/build_influence/analysis/analyzer.py",
"size": 21896,
"type": "code",
"ai_code_insights": {
"purpose": "Performs automated analysis of a local code repository, using AI (LiteLLM) to extract insights from code and documentation files, build file trees, extract metadata, and identify high-level features.",
"key_elements": [
"RepositoryAnalyzer",
"_is_excluded",
"analyze",
"_extract_metadata",
"_build_file_tree",
"_categorize_file",
"_analyze_file_content_with_ai",
"_analyze_code_file_with_ai",
"_analyze_doc_file_with_ai"
],
"dependencies": [
"import subprocess",
"from pathlib import Path",
"from typing import List, Dict, Any",
"import litellm",
"import json",
"from tqdm import tqdm",
"from loguru import logger",
"from build_influence.config import config",
"from build_influence.analysis.feature_identifier import FeatureIdentifier"
],
"interesting_aspects": [
"Uses AI (LiteLLM) to analyze code and documentation files and extract structured JSON insights.",
"Implements file type categorization and exclusion rules for repository traversal.",
"Builds a file tree and integrates high-level feature identification using a separate 'FeatureIdentifier' class.",
"Handles AI prompt/response parsing and error management robustly.",
"Includes progress reporting via tqdm and logging with loguru.",
"Supports both code and documentation AI analysis via adaptable prompt templates."
]
}
},
{
"path": "src/build_influence/analysis/__init__.py",
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"type": "code",
"ai_code_insights": {
"purpose": "Initializes the repository analysis module by exposing key classes for external use.",
"key_elements": [
"RepositoryAnalyzer",
"FeatureIdentifier"
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"dependencies": [
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"from .feature_identifier import FeatureIdentifier"
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"interesting_aspects": [
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{
"path": "src/build_influence/publication/__init__.py",
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"path": "src/build_influence/config/__init__.py",
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"purpose": "Re-exports the 'config' object from the loader module for external use.",
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"config"
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{
"path": "src/build_influence/config/loader.py",
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"purpose": "Loads application configuration from YAML file and environment variables, applies overrides, provides unified config object with dot notation access, and basic validation.",
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"environment variable pattern for overriding config values",
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{
"path": "src/build_influence/utils/logging.py",
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"purpose": "Configures application-wide logging using Loguru, supporting console and file outputs with rotation and retention based on environment variables.",
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"purpose": "Defines the Content Generation module (no code present).",
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],
"files_analyzed_count": 21,
"high_level_features": {
"identified_features": [
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"AI-powered technical content generation",
"Platform-specific content publishing (dev.to, Twitter/X, LinkedIn)",
"Command-line interface (CLI)",
"User interaction and approval workflow",
"Configuration management",
"Progress tracking and logging",
"Pluggable and modular architecture"
],
"architectural_patterns": [
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"CLI application pattern",
"Layered architecture (analysis, publication, CLI)",
"AI/LLM integration for code analysis and content synthesis"
],
"target_audience": "Developers and technical project maintainers seeking to increase project visibility and automate technical marketing tasks",
"selling_points": [
"Fully automated content marketing from code repositories",
"Reduces manual effort in generating and sharing technical content",
"Leverages AI/LLMs for high-quality, tailored content",
"Multi-platform publishing in a single workflow",
"Facilitates personal and project branding for developers"
],
"confidence_score": 0.95
}
}