Coding Agent Context CLI

A command-line interface for dynamically assembling context for AI coding agents.

This tool collects context from predefined rule files and a task-specific prompt, substitutes parameters, and prints a single, combined context to standard output. This is useful for feeding a large amount of relevant information into an AI model like Claude, Gemini, or OpenAI's GPT series.

📖 View Full Documentation
📋 View Specification - The Coding Context Standard
📊 View Slide Deck | Download PDF | How to Present

Generated Context Structure

The tool assembles context into a structured prompt with the following components:

┌─────────────────────────────────────────────────────────────┐
│ Generated Coding Context Prompt                             │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌───────────────────────────────────────────────────────┐  │
│  │ 1. Rules Content (Markdown)                           │  │
│  │    • Coding standards and guidelines                  │  │
│  │    • Team conventions and best practices              │  │
│  │    • Filtered by selectors (-s flag)                  │  │
│  │    • Skipped in resume mode (-r flag)                 │  │
│  └───────────────────────────────────────────────────────┘  │
│                                                               │
│  ┌───────────────────────────────────────────────────────┐  │
│  │ 2. Skills Metadata (XML) - Optional                   │  │
│  │    • Available skills with names & descriptions       │  │
│  │    • Progressive disclosure - full content on demand  │  │
│  │    • Only included if skills are discovered           │  │
│  └───────────────────────────────────────────────────────┘  │
│                                                               │
│  ┌───────────────────────────────────────────────────────┐  │
│  │ 3. Task Content (Markdown)                            │  │
│  │    • Task-specific instructions                       │  │
│  │    • Parameter substitutions (${param})               │  │
│  │    • Command expansions (!`command`)                  │  │
│  │    • Path expansions (@file)                          │  │
│  └───────────────────────────────────────────────────────┘  │
│                                                               │
└─────────────────────────────────────────────────────────────┘

Key Points:

Rules: Reusable context that applies across multiple tasks
Skills: Enable progressive disclosure of specialized capabilities
Task Content: Specific instructions for the current task with dynamic content expansion
Note: Task frontmatter is used for filtering and metadata but is not included in the output

Features

Dynamic Context Assembly: Merges context from various source files.
Task-Specific Prompts: Use different prompts for different tasks (e.g., feature, bugfix).
Rule-Based Context: Define reusable context snippets (rules) that can be included or excluded.
Skills System: Progressive disclosure of specialized capabilities via skill directories.
Frontmatter Filtering: Select rules based on metadata using frontmatter selectors (matches top-level YAML fields only).
Bootstrap Scripts: Run scripts to fetch or generate context dynamically.
Parameter Substitution: Inject values into your task prompts.
Token Estimation: Get an estimate of the total token count for the generated context.

Supported Coding Agents

This tool is compatible with configuration files from various AI coding agents and IDEs. View complete list of 35+ supported agents →

Primary Supported Agents (with dedicated `-a` flag)

GitHub Copilot: .github/copilot-instructions.md, .github/agents (-a copilot)
Anthropic Claude: CLAUDE.md, CLAUDE.local.md, .claude/CLAUDE.md (-a claude)
Cursor: .cursor/rules, .cursorrules (-a cursor)
Google Gemini: GEMINI.md, .gemini/styleguide.md (-a gemini)
Augment: .augment/rules, .augment/guidelines.md (-a augment)
Windsurf: .windsurf/rules, .windsurfrules (-a windsurf)
OpenCode.ai: .opencode/agent, .opencode/command, .opencode/rules (-a opencode)
Codex: AGENTS.md, .codex/AGENTS.md (-a codex)

Additional Compatible Agents

Codeium, Tabnine, Amazon Q Developer (CodeWhisperer)
Sourcegraph Cody, Continue, Aider
Replit Ghostwriter, GitLab Duo
All OpenAI models (GPT-4, ChatGPT), LLM APIs
Agent frameworks: LangChain, AutoGPT, BabyAGI
And 20+ more - see full documentation

Generic AI Agent Support

Generic AI Agents: AGENTS.md, .agents/rules, .agents/commands (reusable content blocks), .agents/tasks

The tool automatically discovers and includes rules from these locations in your project and user home directory (~).

Agentic Workflows

This tool plays a crucial role in the agentic workflow ecosystem by providing rich, contextual information to AI agents. It complements systems like GitHub Next's Agentic Workflows by:

Context Preparation: Assembles rules, guidelines, and task-specific prompts before agent execution
Workflow Integration: Can be invoked in GitHub Actions to provide context to autonomous agents
Dynamic Context: Supports runtime parameters and bootstrap scripts for real-time information
Multi-Stage Support: Different context assemblies for planning, implementation, and validation stages

For a comprehensive guide on using this tool with agentic workflows, see AGENTIC_WORKFLOWS.md.

Installation

You can install the CLI by downloading the latest release from the releases page or by building from source.

Linux

AMD64:

sudo curl -fsL -o /usr/local/bin/coding-context https://github.com/kitproj/coding-context-cli/releases/download/v0.0.23/coding-context_v0.0.23_linux_amd64
sudo chmod +x /usr/local/bin/coding-context

ARM64:

sudo curl -fsL -o /usr/local/bin/coding-context https://github.com/kitproj/coding-context-cli/releases/download/v0.0.23/coding-context_v0.0.23_linux_arm64
sudo chmod +x /usr/local/bin/coding-context

MacOS

Intel (AMD64):

sudo curl -fsL -o /usr/local/bin/coding-context https://github.com/kitproj/coding-context-cli/releases/download/v0.0.23/coding-context_v0.0.23_darwin_amd64
sudo chmod +x /usr/local/bin/coding-context

Apple Silicon (ARM64):

sudo curl -fsL -o /usr/local/bin/coding-context https://github.com/kitproj/coding-context-cli/releases/download/v0.0.23/coding-context_v0.0.23_darwin_arm64
sudo chmod +x /usr/local/bin/coding-context

Usage

Usage:
  coding-context [options] <task-name> [user-prompt]

Arguments:
  <task-name>
      The name of a task file to look up in task search paths (.agents/tasks).
      Task files are matched by filename (without .md extension).
  
  [user-prompt] (optional)
      Optional text to append to the task. It can contain slash commands
      (e.g., '/command-name') which will be expanded, and parameter 
      substitution (${param}).

Options:
  -C string
    	Change to directory before doing anything. (default ".")
  -d value
    	Remote directory containing rules and tasks. Can be specified multiple times. Supports various protocols via go-getter (http://, https://, git::, s3::, etc.).
  -m string
    	Go Getter URL to a manifest file containing search paths (one per line). Every line is included as-is.
  -p value
    	Parameter to substitute in the prompt. Can be specified multiple times as key=value.
  -r	Resume mode: skip outputting rules and select task with 'resume: true' in frontmatter.
  -s value
    	Include rules with matching frontmatter. Can be specified multiple times as key=value.
    	Note: Only matches top-level YAML fields in frontmatter.
  -a string
    	Target agent to use. Required when using -w to write rules to the agent's user rules path. Supported agents: cursor, opencode, copilot, claude, gemini, augment, windsurf, codex.
  -w	Write rules to agent's config file and output only task to stdout. Requires agent (via task or -a flag).

Examples

Basic usage with local files:

coding-context -p jira_issue_key=PROJ-1234 fix-bug | llm -m gemini-pro

This command will:

Find a task file named fix-bug.md in the task search paths.
Find all rule files in the search paths.
Filter the rules based on selectors.
Execute any associated bootstrap scripts.
Substitute ${jira_issue_key} with PROJ-1234 in the task prompt.
Print the combined context (rules + task) to stdout.
Pipe the output to another program (in this case, llm).

Using remote directories:

coding-context \
  -d git::https://github.com/company/shared-rules.git \
  -d s3::https://s3.amazonaws.com/my-bucket/coding-standards \
  fix-bug | llm -m gemini-pro

This command will:

Download remote directories using go-getter
Search for rules and tasks in the downloaded directories
Combine them with local rules and tasks
Apply the same processing as with local files

The -d flag supports various protocols via go-getter:

http:// and https:// - HTTP/HTTPS URLs
git:: - Git repositories
s3:: - S3 buckets
file:// - Local file paths
And more (see go-getter documentation)

Content Expansion Features

Task and rule content supports three types of dynamic expansion:

Parameter Expansion: Use ${parameter_name} syntax to substitute parameter values from -p flags
```
Issue: ${issue_key}
Description: ${description}
```
Command Expansion: Use !`command` syntax to execute shell commands and include their output
```
Current date: !`date +%Y-%m-%d`
Git branch: !`git rev-parse --abbrev-ref HEAD`
```
Path Expansion: Use @path syntax to include file contents
```
Current configuration:
@config.yaml
```

Security Note: All expansions are processed in a single pass to prevent injection attacks. Expanded content is never re-processed.

Example Tasks

The <task-name> corresponds to the filename (without the .md extension) of task files. Here are some common examples:

triage-bug
review-pull-request
fix-broken-build
migrate-java-version
enhance-docs
remove-feature-flag
speed-up-build

Each of these would have a corresponding .md file (e.g., triage-bug.md, fix-broken-build.md).

How It Works

The tool assembles the context in the following order:

Rule Files: It searches for rule files (.md or .mdc) in directories specified via -d flags and automatically-added working directory and home directory.
Rule Bootstrap Scripts: For each rule file found (e.g., my-rule.md), it looks for an executable script named my-rule-bootstrap. If found, it runs the script before processing the rule file. These scripts are meant for bootstrapping the environment (e.g., installing tools) and their output is sent to stderr, not into the main context.
Filtering: If -s (include) flag is used, it parses the YAML frontmatter of each rule file to decide whether to include it. Note that selectors can only match top-level YAML fields (e.g., language: go), not nested fields.
Task Prompt: It searches for a task file matching the filename (without .md extension). Tasks are matched by filename, not by task_name in frontmatter. If selectors are provided with -s, they are used to filter between multiple task files with the same filename.
Task Bootstrap Script: For the task file found (e.g., fix-bug.md), it looks for an executable script named fix-bug-bootstrap. If found, it runs the script before processing the task file. This allows task-specific environment setup or data preparation.
User Prompt Appending: If a user-prompt argument is provided, it is appended to the task content after a delimiter (---).
Parameter Expansion: It substitutes variables in the task prompt and user-prompt using the -p flags.
Output: It prints the content of all included rule files, followed by the expanded task prompt, to standard output.
Token Count: A running total of estimated tokens is printed to standard error.

File Search Paths

The tool looks for task and rule files in the following locations, in order of precedence:

Tasks:

./.agents/tasks/*.md (task name matches filename without .md extension)

Commands (reusable content blocks referenced via slash commands like /command-name inside task content):

./.agents/commands/*.md
./.cursor/commands/*.md
./.opencode/command/*.md

Skills (specialized capabilities with progressive disclosure):

./.agents/skills/*/SKILL.md (each subdirectory in .agents/skills/ can contain a SKILL.md file)

Rules: The tool searches for a variety of files and directories, including:

CLAUDE.local.md
.agents/rules, .cursor/rules, .augment/rules, .windsurf/rules, .opencode/agent
.github/copilot-instructions.md, .github/agents, .gemini/styleguide.md, .augment/guidelines.md
AGENTS.md, CLAUDE.md, GEMINI.md, .cursorrules, .windsurfrules
User-specific rules in ~/.agents/rules, ~/.claude/CLAUDE.md, ~/.codex/AGENTS.md, ~/.gemini/GEMINI.md, ~/.opencode/rules, etc.

Remote File System Support

The tool supports loading rules and tasks from remote locations via HTTP/HTTPS URLs. This enables:

Shared team guidelines: Host coding standards on a central server
Organization-wide rules: Distribute common rules across multiple projects
Version-controlled context: Serve rules from Git repositories
Dynamic rules: Update shared rules without modifying individual repositories

Usage:

# Clone a Git repository containing rules
coding-context -d git::https://github.com/company/shared-rules.git /fix-bug

# Use multiple remote sources
coding-context \
  -d git::https://github.com/company/shared-rules.git \
  -d https://cdn.company.com/coding-standards \
  /deploy

# Mix local and remote directories
coding-context \
  -d git::https://github.com/company/shared-rules.git \
  -s languages=go \
  /implement-feature

Supported protocols (via go-getter):

http:// and https:// - HTTP/HTTPS URLs (downloads tar.gz, zip, or directories)
git:: - Git repositories (e.g., git::https://github.com/user/repo.git)
s3:: - S3 buckets (e.g., s3::https://s3.amazonaws.com/bucket/path)
file:// - Local file paths
And more - see go-getter documentation

Important notes:

Remote directories are downloaded to a temporary location
Bootstrap scripts work in downloaded directories
Downloaded directories are cleaned up after execution
Supports all standard directory structures (.agents/rules, .agents/tasks, etc.)

Example: Using a Git repository:

# Use a specific branch or tag
coding-context \
  -d 'git::https://github.com/company/shared-rules.git?ref=v1.0' \
  /fix-bug

# Use a subdirectory within the repo
coding-context \
  -d 'git::https://github.com/company/mono-repo.git//coding-standards' \
  /implement-feature

File Formats

Task Files

Task files are Markdown files located in task search directories (e.g., .agents/tasks/). Tasks are matched by filename (without the .md extension), not by frontmatter fields. The task_name field in frontmatter is optional metadata. Task files can contain variables for substitution and can use selectors in frontmatter to filter between multiple task files with the same filename in different locations.

Example (.agents/tasks/fix-bug.md):

# Task: Fix Bug in ${jira_issue_key}

Here is the context for the bug. Please analyze the following files and provide a fix.

Example with selectors for multiple prompts (.agents/tasks/deploy-staging.md):

---
environment: staging
---
# Deploy to Staging

Deploy the application to the staging environment with extra validation.

Example for production (.agents/tasks/deploy-prod.md):

---
environment: production
---
# Deploy to Production

Deploy the application to production with all safety checks.

You can then select the appropriate task using:

# Deploy to staging
coding-context -s environment=staging deploy

# Deploy to production
coding-context -s environment=production deploy

Task Frontmatter Selectors

Task files can include a selectors field in their frontmatter to automatically filter rules without requiring explicit -s flags on the command line. This is useful for tasks that always need specific rules.

Example (.agents/tasks/implement-go-feature.md):

---
selectors:
  languages: go
  stage: implementation
---
# Implement Feature

Implement the feature following Go best practices and implementation guidelines.

When you run this task, it automatically applies the selectors:

# This command automatically includes only rules with languages=go and stage=implementation
coding-context implement-feature

Selectors support OR logic for the same key using arrays:

---
selectors:
  languages: [go, python]
  stage: testing
---

This will include rules that match (languages=go OR languages=python) AND stage=testing.

Combining task selectors with command-line selectors:

Selectors from both the task frontmatter and command line are combined (additive):

# Task has: selectors.languages = go
# Command adds: -s priority=high
# Result: includes rules matching languages=go AND priority=high
coding-context -s priority=high implement-feature

Parameter Expansion Control

By default, parameter expansion occurs in all task and rule content. You can disable this behavior using the expand frontmatter field.

Example (task with expansion disabled):

---
expand: false
---

Issue: ${issue_number}
Title: ${issue_title}

When expand: false is set, parameter placeholders like ${variable} are preserved as-is in the output, rather than being replaced with values from -p flags.

Use cases:

Passing templates to AI agents that handle their own parameter substitution
Preserving template syntax for later processing
Avoiding conflicts with other templating systems

The expand field works in:

Task files (.agents/tasks/*.md)
Command files (.agents/commands/*.md)
Rule files (.agents/rules/*.md)

Resume Mode

Resume mode is designed for continuing work on a task where you've already established context. When using the -r flag:

Rules are skipped: All rule files are excluded from output, saving tokens and reducing context size
Resume-specific task prompts are selected: Automatically adds -s resume=true selector to find task files with resume: true in their frontmatter

This is particularly useful in agentic workflows where an AI agent has already been primed with rules and is continuing work from a previous session.

The -r flag is shorthand for:

Adding -s resume=true selector
Skipping all rules output

Example usage:

# Initial task invocation (includes all rules, uses task with resume: false)
coding-context -s resume=false fix-bug | ai-agent

# Resume the task (skips rules, uses task with resume: true)
coding-context -r fix-bug | ai-agent

Example task files for resume mode:

Initial task (.agents/tasks/fix-bug-initial.md):

---
resume: false
---
# Fix Bug

Analyze the issue and implement a fix.
Follow the coding standards and write tests.

Resume task (.agents/tasks/fix-bug-resume.md):

---
resume: true
---
# Fix Bug - Continue

Continue working on the bug fix.
Review your previous work and complete remaining tasks.

With this approach, you can have multiple task prompts for the same task name, differentiated by the resume frontmatter field. Use -s resume=false to select the initial task (with rules), or -r to select the resume task (without rules).

Rule Files

Rule files are Markdown (.md) or .mdc files, optionally with YAML frontmatter for filtering.

Example (.agents/rules/backend.md):

---
languages:
  - go
---

# Backend Coding Standards

- All new code must be accompanied by unit tests.
- Use the standard logging library.

To include this rule only when working on Go code, you would use -s languages=go:

coding-context -s languages=go fix-bug

This will include all rules with languages: [ go ] in their frontmatter, excluding rules for other languages.

Note: Language values should be lowercase (e.g., go, python, javascript). The frontmatter field is languages (plural) with array format.

Example: Language-Specific Rules

You can create multiple language-specific rule files:

.agents/rules/python-standards.md with languages: [ python ]
.agents/rules/javascript-standards.md with languages: [ javascript ]
.agents/rules/go-standards.md with languages: [ go ]

Then select only the relevant rules:

# Work on Python code with Python-specific rules
coding-context -s languages=python fix-bug

# Work on JavaScript code with JavaScript-specific rules
coding-context -s languages=javascript enhance-feature

Language Values

When using language selectors, language values should be lowercase (e.g., go, python, javascript, java, typescript). The frontmatter field should be languages (plural) in array format:

---
languages:
  - go
  - python
---

Common languages (lowercase):

c, csharp (C#), cpp (C++), css
dart, elixir, go, haskell, html
java, javascript, kotlin, lua, markdown
objectivec (Objective-C), php, python, ruby, rust
scala, shell, swift, typescript, yaml

Note: Language values should be lowercase in frontmatter and selectors.

Note: Frontmatter selectors can only match top-level YAML fields. For example:

✅ Works: languages: [ go ] matches -s languages=go
❌ Doesn't work: Nested fields like metadata.version: 1.0 cannot be matched with -s metadata.version=1.0

If you need to filter on nested data, flatten your frontmatter structure to use top-level fields only.

MCP Server Configuration

Rules can optionally specify MCP (Model Context Protocol) server configurations for integration with AI coding agents. This is useful for defining server processes that AI agents can interact with.

---
languages:
  - python
mcp_server:
  command: python
  args: ["-m", "server"]
  env:
    PYTHON_PATH: /usr/bin/python3
---

For detailed information on MCP server configuration, see the File Formats Reference.

Targeting a Specific Agent

The -a flag specifies which AI coding agent you're using. This information is currently used for:

Write Rules Mode: With the -w flag, determines where to write rules (e.g., ~/.github/agents/AGENTS.md for copilot)

Note: Agent-based rule filtering is not currently implemented. All rules are included regardless of the -a value.

Supported agents:

cursor - Cursor IDE
opencode - OpenCode.ai
copilot - GitHub Copilot
claude - Anthropic Claude
gemini - Google Gemini
augment - Augment
windsurf - Windsurf
codex - Codex

Example:

# Use with write rules mode
coding-context -a copilot -w fix-bug

How it works:

The -a flag sets the target agent value
The agent value is stored in the context for use by the -w flag
With -w, the agent determines where to write rules to the user's home directory
All rules are currently included regardless of agent value

Agent field in task frontmatter:

Tasks can specify an agent field in their frontmatter, which overrides the -a command-line flag:

---
agent: cursor
---
# This task automatically sets the agent to cursor

This is useful for tasks designed for specific agents, ensuring the correct agent is set for determining the write path with -w.

Bootstrap Scripts

A bootstrap script is an executable file that has the same name as a rule or task file but with a -bootstrap suffix. These scripts are used to prepare the environment, for example by installing necessary tools. The output of these scripts is sent to stderr and is not part of the AI context.

Examples:

Rule file: .agents/rules/jira.md
Rule bootstrap script: .agents/rules/jira-bootstrap
Task file: .agents/tasks/fix-bug.md
Task bootstrap script: .agents/tasks/fix-bug-bootstrap

Bootstrap scripts are executed in the following order:

Rule bootstrap scripts run before their corresponding rule files are processed
Task bootstrap scripts run after all rules are processed but before the task content is emitted

.agents/rules/jira-bootstrap:

#!/bin/bash
# This script installs the jira-cli if it's not already present.
if ! command -v jira-cli &> /dev/null
then
    echo "Installing jira-cli..." >&2
    # Add installation commands here
fi

.agents/tasks/fix-bug-bootstrap:

#!/bin/bash
# This script fetches the latest issue details before the task runs.
echo "Fetching issue information..." >&2
# Fetch and prepare issue data

Command Files (Slash Commands)

Command files are reusable content blocks that can be referenced from task files using slash command syntax (e.g., /command-name). They enable modular, composable task definitions.

Example command file (.agents/commands/pre-deploy.md):

---
expand: true
---
# Pre-deployment Checklist

Run tests: !`npm test`
Build status: !`git status`

Using commands in a task (.agents/tasks/deploy.md):

# Deployment Task

/pre-deploy

Deploy the application to ${environment}.

/post-deploy

Commands can also receive inline parameters:

/greet name="Alice"
/deploy env="production" version="1.2.3"

Skill Files

Skill files provide specialized capabilities with progressive disclosure. Skills are discovered in .agents/skills/ directories and each skill is a subdirectory containing a SKILL.md file with metadata.

Skills enable:

Progressive Disclosure: Only skill metadata (name, description) is included in the initial context
On-Demand Loading: AI agents can load full skill content when needed
Modular Capabilities: Organize domain-specific knowledge separately from general rules
Selector Filtering: Skills can be filtered using selectors just like rules

Example skill structure:

.agents/skills/
├── data-analysis/
│   └── SKILL.md
└── pdf-processing/
    └── SKILL.md

Example skill file (.agents/skills/data-analysis/SKILL.md):

---
name: data-analysis
description: Analyze datasets, generate charts, and create summary reports. Use when the user needs to work with CSV, Excel, or other tabular data formats.
license: MIT
metadata:
  author: team-name
  version: "1.0"
---

# Data Analysis

## When to use this skill
Use this skill when the user needs to:
- Analyze CSV or Excel files
- Generate charts and visualizations
- Calculate statistics and summaries

## How to analyze data
1. Use pandas for data analysis:
   ```python
   import pandas as pd
   df = pd.read_csv('data.csv')


**Skill Frontmatter Fields:**
- `name` (required): Skill identifier, 1-64 characters
- `description` (required): What the skill does and when to use it, 1-1024 characters
- `license` (optional): License applied to the skill
- `compatibility` (optional): Environment requirements, max 500 characters
- `metadata` (optional): Arbitrary key-value pairs

**XML Output:**
Skills are output as XML for easy parsing by AI agents:
```xml
<available_skills>
  <skill>
    <name>data-analysis</name>
    <description>Analyze datasets, generate charts...</description>
    <location>/path/to/.agents/skills/data-analysis/SKILL.md</location>
  </skill>
</available_skills>

The AI agent can then read the full skill content from the provided location when needed.

Task Frontmatter

Task frontmatter contains metadata used for filtering, agent selection, and workflow automation. While the frontmatter itself is not included in the generated output, it serves important purposes:

What frontmatter is used for:

Task selection: Filtering between multiple task files using selectors
Agent specification: The agent field can override the -a command-line flag
Rule filtering: The selectors field automatically filters rules without requiring explicit -s flags
Workflow control: Fields like resume control task behavior in different modes

Example usage:

coding-context -p issue_number=123 fix-bug

Example task file:

---
resume: false
selectors:
  languages: go
  stage: implementation
---
# Fix Bug Task

Fix the bug in issue #${issue_number}...

Output format: The task frontmatter is NOT included in the output. Only the task content (below the frontmatter delimiters) is included:

# Fix Bug Task

Fix the bug in issue #123...

This design keeps the output focused on actionable content while still allowing frontmatter to control behavior and filtering.

Example with selectors in frontmatter:

coding-context implement-feature

If the task has selectors in its frontmatter, they will automatically filter rules, but the frontmatter itself won't appear in the output:

# Implementation Task
...

Presentations

A comprehensive slide deck is available for presenting and learning about the Coding Context CLI:

Name		Name	Last commit message	Last commit date
Latest commit History 121 Commits
.github		.github
docs		docs
examples		examples
pkg/codingcontext		pkg/codingcontext
.gitignore		.gitignore
AGENTIC_WORKFLOWS.md		AGENTIC_WORKFLOWS.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SLIDES.md		SLIDES.md
SLIDES.pdf		SLIDES.pdf
SLIDES_README.md		SLIDES_README.md
SPECIFICATION.md		SPECIFICATION.md
go.mod		go.mod
go.sum		go.sum
integration_test.go		integration_test.go
main.go		main.go

License

kitproj/coding-context-cli

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