We’ve been talking about context, composer etc… and I have been doing all sorts of experiments with context tracking and such and I had a sudden ides:
Ill share the results once my bot stops slacking off.
Thought and feedback welcome:
Ill contiue to update progress in this thread on if this works
#CONTEXT_INVENTORY.RMD
To give you an idea- I founf this FASCINATING – as it describes the Sphere of Context for my project:
==================
---
title: "[PROJECT] Context Inventory"
subtitle: "A Comprehensive Guide to Project Structure and Development Practices"
date: "`r format(Sys.time(), '%B %d, %Y')`"
author: "[PROJECT] Development Team"
output:
html_document:
theme: darkly
toc: true
toc_float: true
code_folding: hide
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE,
message = FALSE,
warning = FALSE,
fig.width = 10,
fig.height = 6
)
# Load required libraries
library(knitr)
library(kableExtra)
library(ggplot2)
library(dplyr)
library(tidyr)
library(viridis)
library(plotly)
This document provides a comprehensive inventory of the [PROJECT] project context, including file structure, linting directives, feature toggles, and development personas. It serves as a central reference for understanding the project’s architecture and development practices.
[PROJECT] is a sophisticated platform that combines React-based frontend development with powerful backend services. The project emphasizes:
The project’s file structure is organized into several key categories, each serving specific purposes in the development workflow.
files_df <- data.frame(
File = c(
"fix-react-structure.ps1",
"setup-react-components.ps1",
"launch-dev.ps1",
"setup-react-core.ps1",
"create-linting-guide.ps1",
"composer-linting-eli5.md",
"development_diary.md",
"setup-[PROJECT].ps1",
"[PROJECT].code-workspace",
".env",
"render-mermaid.ps1",
"[PROJECT].Rmd",
"header.html",
"styles.css",
"yolo-db-setup.ps1",
"start-battles.ps1",
"yolo-deploy.ps1",
"yolo-monitor.ps1",
"setup-grafana.ps1",
"archive-version.ps1"
),
Type = c(
"Script", "Script", "Script", "Script", "Script",
"Documentation", "Documentation", "Script", "Config",
"Config", "Script", "Documentation", "Template",
"Styles", "Script", "Script", "Script", "Script",
"Script", "Script"
),
Path = c(
"D:/Cursor/[PROJECT]/fix-react-structure.ps1",
"D:/Cursor/[PROJECT]/setup-react-components.ps1",
"D:/Cursor/[PROJECT]/launch-dev.ps1",
"D:/Cursor/[PROJECT]/setup-react-core.ps1",
"D:/Cursor/[PROJECT]/create-linting-guide.ps1",
"D:/Cursor/[PROJECT]/composer-linting-eli5.md",
"D:/Cursor/[PROJECT]/development_diary.md",
"D:/Cursor/[PROJECT]/setup-[PROJECT].ps1",
"D:/Cursor/[PROJECT]/[PROJECT].code-workspace",
"D:/Cursor/[PROJECT]/.env",
"D:/Cursor/[PROJECT]/scripts/render-mermaid.ps1",
"D:/Cursor/[PROJECT]/docs/[PROJECT].Rmd",
"D:/Cursor/[PROJECT]/docs/header.html",
"D:/Cursor/[PROJECT]/docs/styles.css",
"D:/Cursor/[PROJECT]/scripts/yolo-db-setup.ps1",
"D:/Cursor/[PROJECT]/scripts/start-battles.ps1",
"D:/Cursor/[PROJECT]/scripts/yolo-deploy.ps1",
"D:/Cursor/[PROJECT]/scripts/yolo-monitor.ps1",
"D:/Cursor/[PROJECT]/scripts/setup-grafana.ps1",
"D:/Cursor/[PROJECT]/scripts/archive-version.ps1"
),
Purpose = c(
"React app structure setup",
"React component creation",
"Development server management",
"Core React files setup",
"Linting guide generation",
"Simplified linting explanation",
"Development progress tracking",
"Project initialization",
"VS Code workspace settings",
"Environment configuration",
"Mermaid diagram rendering",
"Main documentation",
"Documentation header template",
"Documentation styling",
"Database setup",
"Battle system management",
"Deployment automation",
"System monitoring",
"Grafana setup",
"Version archiving"
)
)
# Display the table
kable(files_df, format = "html", escape = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
# Create a visualization of file type distribution
files_df %>%
count(Type) %>%
ggplot(aes(x = reorder(Type, n), y = n, fill = Type)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_minimal() +
labs(
title = "Distribution of File Types",
x = "File Type",
y = "Count",
fill = "Type"
) +
scale_fill_viridis_d()
Our linting system employs a sophisticated set of directives to ensure code quality and consistency across the project.
directives_df <- data.frame(
Directive = c(
"ENFORCE",
"CHECK",
"OPTIMIZE",
"style=airbnb",
"jsx=strict",
"security=high",
"async=true",
"bundle=size",
"cache=check",
"data=sanitize",
"null=strict",
"memory=efficient",
"tensor=validate",
"docs=required",
"tests=coverage:80"
),
Context = c(
"Global", "Global", "Global",
"Frontend", "Frontend", "Backend",
"Backend", "Full Stack", "Full Stack",
"ETL", "ETL", "ML",
"ML", "Analytics", "Analytics"
),
Source = c(
"create-linting-guide.ps1",
"create-linting-guide.ps1",
"create-linting-guide.ps1",
"composer-linting-eli5.md",
"composer-linting-eli5.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md",
"development_diary.md"
),
Purpose = c(
"Strict rule enforcement",
"Validation with warnings",
"Performance improvements",
"Airbnb style guide compliance",
"Strict JSX validation",
"High security standards",
"Asynchronous operations",
"Bundle size optimization",
"Cache validation",
"Data sanitization",
"Strict null checking",
"Memory optimization",
"Tensor validation",
"Documentation requirements",
"Test coverage requirements"
)
)
kable(directives_df, format = "html", escape = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
# Visualize directive distribution across contexts
directives_df %>%
count(Context) %>%
ggplot(aes(x = "", y = n, fill = Context)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
theme_minimal() +
labs(
title = "Distribution of Directives Across Contexts",
fill = "Context"
) +
scale_fill_viridis_d()
The project uses a comprehensive set of feature toggles and settings to control various aspects of the system’s behavior.
settings_df <- data.frame(
Setting = c(
"DEBUG",
"LOG_LEVEL",
"BATTLE_MIN_AGENTS",
"BATTLE_MAX_AGENTS",
"BATTLE_TIMEOUT_SECONDS",
"MIN_BET_AMOUNT",
"MAX_BET_AMOUNT",
"HOUSE_EDGE_PERCENT",
"DEFAULT_AGENT_MEMORY_MB",
"DEFAULT_AGENT_TIMEOUT_MS",
"MAX_CONCURRENT_BATTLES"
),
Value = c(
"True",
"INFO",
"2",
"10",
"3600",
"0.001",
"100.0",
"2.5",
"512",
"30000",
"5"
),
Source = c(
".env",
".env",
".env",
".env",
".env",
".env",
".env",
".env",
".env",
".env",
".env"
),
Purpose = c(
"Debug mode toggle",
"Logging verbosity",
"Minimum agents per battle",
"Maximum agents per battle",
"Battle timeout in seconds",
"Minimum bet amount",
"Maximum bet amount",
"House edge percentage",
"Default agent memory limit",
"Default agent timeout",
"Maximum concurrent battles"
)
)
kable(settings_df, format = "html", escape = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
# Create a treemap of settings categories
settings_df %>%
mutate(Category = case_when(
grepl("BATTLE", Setting) ~ "Battle System",
grepl("AGENT", Setting) ~ "Agent Configuration",
grepl("BET", Setting) ~ "Betting System",
TRUE ~ "System Configuration"
)) %>%
count(Category) %>%
ggplot(aes(area = n, fill = Category, label = paste(Category, "\n", n))) +
geom_treemap() +
geom_treemap_text(colour = "white", place = "centre", size = 15) +
scale_fill_viridis_d() +
labs(title = "Settings Distribution by Category")
Our development process is enriched by a diverse set of personas, each bringing unique perspectives and priorities to the project.
personas_df <- data.frame(
Persona = c(
"YOLO Developer",
"Security Auditor",
"Performance Optimizer",
"Documentation Writer",
"Code Reviewer",
"System Architect",
"DevOps Engineer",
"Database Administrator",
"Frontend Specialist",
"Backend Engineer"
),
Focus = c(
"Rapid development & innovation",
"Security & compliance",
"System optimization",
"Documentation & clarity",
"Code quality & standards",
"System design & architecture",
"Deployment & automation",
"Database management",
"UI/UX development",
"API & service development"
),
Source = c(
"development_diary.md",
"yolo-monitor.ps1",
"yolo-monitor.ps1",
"[PROJECT].Rmd",
"create-linting-guide.ps1",
"setup-[PROJECT].ps1",
"yolo-deploy.ps1",
"yolo-db-setup.ps1",
"setup-react-core.ps1",
"yolo-db-setup.ps1"
),
Traits = c(
"Fast, innovative, risk-tolerant",
"Thorough, cautious, detail-oriented",
"Analytical, efficiency-focused",
"Clear, organized, comprehensive",
"Meticulous, standards-focused",
"Strategic, big-picture oriented",
"Automation-focused, systematic",
"Data-centric, reliability-focused",
"User-focused, design-oriented",
"Scalability-focused, robust"
)
)
kable(personas_df, format = "html", escape = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
# Create a network visualization of persona relationships
library(igraph)
library(networkD3)
# Create relationships between personas based on complementary skills
nodes <- data.frame(
name = personas_df$Persona,
group = match(personas_df$Focus, unique(personas_df$Focus))
)
# Create edges between complementary personas
edges <- data.frame(
source = c(1, 1, 2, 2, 3, 3, 4, 4, 5),
target = c(2, 3, 3, 4, 4, 5, 5, 6, 6)
)
# Create network
network <- forceNetwork(
Links = edges,
Nodes = nodes,
Source = "source",
Target = "target",
NodeID = "name",
Group = "group",
opacity = 0.8,
fontSize = 12,
zoom = TRUE
)
network
Strategic combinations of personas and directives can optimize development outcomes for different focus areas.
recommendations_df <- data.frame(
Focus = c(
"Development Speed",
"Code Quality",
"Security",
"Performance",
"Documentation",
"Architecture",
"DevOps",
"Database",
"Frontend",
"Backend"
),
Recommended_Personas = c(
"YOLO Developer + Performance Optimizer",
"Code Reviewer + System Architect",
"Security Auditor + DevOps Engineer",
"Performance Optimizer + Backend Engineer",
"Documentation Writer + Frontend Specialist",
"System Architect + Database Administrator",
"DevOps Engineer + YOLO Developer",
"Database Administrator + Backend Engineer",
"Frontend Specialist + Documentation Writer",
"Backend Engineer + System Architect"
),
Recommended_Directives = c(
"OPTIMIZE + async=true",
"ENFORCE + style=airbnb",
"CHECK + security=high",
"OPTIMIZE + memory=efficient",
"ENFORCE + docs=required",
"CHECK + null=strict",
"OPTIMIZE + cache=check",
"ENFORCE + data=sanitize",
"ENFORCE + jsx=strict",
"CHECK + async=true"
),
Impact = c(
"Rapid iteration with performance awareness",
"High-quality, maintainable code",
"Secure, reliable systems",
"Optimized system performance",
"Clear, comprehensive documentation",
"Robust system architecture",
"Efficient deployment pipeline",
"Reliable data management",
"Polished user experience",
"Scalable backend services"
)
)
kable(recommendations_df, format = "html", escape = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
# Create a heatmap of focus areas and their impacts
recommendations_df %>%
mutate(
Impact_Score = sample(7:10, n(), replace = TRUE) # Simulated impact scores
) %>%
ggplot(aes(x = Focus, y = "Impact", fill = Impact_Score)) +
geom_tile() +
scale_fill_viridis() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
title = "Impact Assessment Heatmap",
x = "Focus Area",
y = NULL
)
The project includes several PowerShell scripts organized by their primary functions. Below is a comprehensive inventory of these utilities:
library(knitr)
library(kableExtra)
ps_utils <- data.frame(
Category = c(
"Setup & Installation",
"Setup & Installation",
"Setup & Installation",
"Setup & Installation",
"Setup & Installation",
"Development",
"Development",
"Development",
"Documentation",
"Documentation",
"Documentation",
"Deployment",
"Deployment",
"Deployment",
"Monitoring",
"Monitoring",
"Database",
"Database",
"Version Control"
),
Script = c(
"setup-[PROJECT].ps1",
"setup-react-core.ps1",
"setup-r-environment.ps1",
"install-r-packages.ps1",
"install-gh-cli.ps1",
"fix-react-structure.ps1",
"setup-react-components.ps1",
"launch-dev.ps1",
"render-in-new-session.ps1",
"render-mermaid.ps1",
"deploy-to-ghpages.ps1",
"yolo-deploy.ps1",
"setup-grafana.ps1",
"create-linting-guide.ps1",
"yolo-monitor.ps1",
"start-battles.ps1",
"yolo-db-setup.ps1",
"init-db.ps1",
"archive-version.ps1"
),
Description = c(
"Main project setup script that initializes the development environment",
"Sets up the React application core with TypeScript configuration",
"Configures R environment with required packages and settings",
"Installs required R packages for documentation generation",
"Installs GitHub CLI for repository management",
"Fixes and organizes React project structure",
"Sets up React components with proper configuration",
"Development server launcher with port conflict resolution",
"Renders documentation in a new PowerShell session",
"Generates Mermaid diagrams for documentation",
"Deploys documentation to GitHub Pages",
"Main deployment script for the [PROJECT] system",
"Sets up Grafana dashboards for monitoring",
"Creates linting documentation and guidelines",
"Monitors system performance and health",
"Manages battle simulation processes",
"Sets up the database schema and initial data",
"Initializes database with required tables",
"Manages version control for documentation and code"
)
)
kable(ps_utils,
format = "html",
caption = "PowerShell Utility Scripts Inventory"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = TRUE
) %>%
column_spec(1, bold = TRUE) %>%
collapse_rows(columns = 1, valign = "middle")
PowerShell scripts are organized in the following directory structure:
[PROJECT]/
├── scripts/ # Core system scripts
│ ├── deployment/ # Deployment-related scripts
│ ├── monitoring/ # Monitoring and health check scripts
│ └── database/ # Database management scripts
├── docs/ # Documentation scripts
│ └── tools/ # Documentation generation tools
└── dev/ # Development utilities
├── setup/ # Setup and installation scripts
└── tools/ # Development tools and utilities
The [PROJECT] context sphere is managed through a PostgreSQL database with the following structure:
erDiagram
files ||--o{ context_relationships : "participates_in"
directives ||--o{ context_relationships : "participates_in"
settings ||--o{ context_relationships : "participates_in"
personas ||--o{ context_relationships : "participates_in"
powershell_utils ||--o{ context_relationships : "participates_in"
focus_areas ||--o{ context_relationships : "influences"
files {
int file_id PK
string file_name
string file_type
string file_path
string purpose
timestamp created_at
timestamp updated_at
}
directives {
int directive_id PK
string directive_name
string context
string source_file
string purpose
timestamp created_at
}
settings {
int setting_id PK
string setting_name
string setting_value
string source_file
string purpose
boolean is_toggle
timestamp created_at
timestamp updated_at
}
personas {
int persona_id PK
string persona_name
string focus_area
string source_file
string traits
timestamp created_at
}
powershell_utils {
int script_id PK
string script_name
string category
string description
string file_path
timestamp created_at
timestamp updated_at
}
context_relationships {
int relationship_id PK
string source_type
int source_id
string target_type
int target_id
string relationship_type
float strength
timestamp created_at
}
focus_areas {
int focus_id PK
string focus_name
array recommended_personas
array recommended_directives
string impact_description
int impact_score
timestamp created_at
}
The schema is designed to capture the entire context sphere of the [PROJECT] project, including:
The relationships between different context elements are visualized below:
graph TD
subgraph "Development Context"
F[Files] --> D[Directives]
D --> S[Settings]
S --> P[Personas]
P --> U[PowerShell Utils]
end
subgraph "Focus Areas"
FA1[Development Speed]
FA2[Code Quality]
FA3[Security]
FA4[Performance]
end
subgraph "Relationships"
R[Context Relationships]
R --> F
R --> D
R --> S
R --> P
R --> U
R --> FA1
R --> FA2
R --> FA3
R --> FA4
end
style Development fill:#f9f,stroke:#333,stroke-width:2px
style "Focus Areas" fill:#bbf,stroke:#333,stroke-width:2px
style Relationships fill:#bfb,stroke:#333,stroke-width:2px
This context inventory provides a comprehensive overview of the [PROJECT] project’s structure, development practices, and organizational patterns. By following these guidelines and leveraging the provided tools and personas, we can maintain high-quality development practices while ensuring efficient project progression.