How to Use with AI Agents

This guide shows you how to use the Coding Context CLI with various AI agents and tools.

Basic Usage

Pipe the assembled context to any AI agent:

coding-context fix-bug | your-ai-agent

With Claude CLI 

coding-context \
  -p issue_key=BUG-123 \
  -s languages=go \
  /fix-bug | claude

With OpenAI API 

coding-context code-review | openai api completions.create \
  -m gpt-4 \
  --stream

With LLM Tool

The llm tool supports many models:

# Using Claude
coding-context fix-bug | llm -m claude-3-5-sonnet-20241022

# Using Gemini
coding-context code-review | llm -m gemini-pro

# Using local models
coding-context implement-feature | llm -m llama2

Saving Context to File

Save the context for later use or inspection:

# Save to file
coding-context fix-bug > context.txt

# Review the context
cat context.txt

# Use with AI agent
cat context.txt | claude

Multi-Step Workflows

Use context in iterative workflows:

# Step 1: Initial analysis
coding-context -s resume=false fix-bug > context-initial.txt
cat context-initial.txt | ai-agent > analysis.txt

# Step 2: Implementation (skip rules with -r)
coding-context -r fix-bug > context-resume.txt
cat context-resume.txt analysis.txt | ai-agent > implementation.txt

With GitHub Copilot

If you’re using GitHub Copilot, the CLI can prepare context for custom instructions:

# Generate context
coding-context implement-feature > .github/copilot-context.md

# Copilot will read this file automatically

Write-Rules Mode

Write-rules mode (-w flag) separates rules from tasks, allowing AI agents to read rules from their standard configuration files while keeping task prompts clean.

Benefits

  • Token Savings: Avoid including all rules in every prompt
  • Agent Integration: Write rules to agent-specific config files
  • Clean Prompts: Output only the task to stdout

Basic Usage 

# Write rules to agent's config file, output task to stdout
coding-context -a copilot -w fix-bug | llm -m claude-3-5-sonnet

This will:

  1. Write all rules to ~/.github/agents/AGENTS.md
  2. Output only the task prompt to stdout
  3. The AI agent reads rules from its config file

Agent-Specific Paths

Each agent has a designated configuration file:

# GitHub Copilot
coding-context -a copilot -w fix-bug  # → ~/.github/agents/AGENTS.md

# Claude
coding-context -a claude -w fix-bug   # → ~/.claude/CLAUDE.md

# Cursor
coding-context -a cursor -w fix-bug   # → ~/.cursor/rules/AGENTS.md

# Gemini
coding-context -a gemini -w fix-bug   # → ~/.gemini/GEMINI.md

Task-Specified Agent

Tasks can specify their preferred agent in frontmatter:

Task file (deploy.md):

---
agent: claude
---
# Deploy to Production
...

Usage:

# Task's agent field is used (writes to ~/.claude/CLAUDE.md)
coding-context -w deploy

# Task agent overrides -a flag
coding-context -a copilot -w deploy  # Still uses claude

Workflow Example 

# 1. Initial setup: Write rules once
coding-context -a copilot -w setup-project

# 2. Run multiple tasks without re-including rules
coding-context -a copilot -w fix-bug | llm
coding-context -a copilot -w code-review | llm
coding-context -a copilot -w refactor | llm

# 3. Update rules when needed
coding-context -a copilot -w -s languages=go update-rules

Environment Variables for Bootstrap Scripts

Pass environment variables to bootstrap scripts:

# Set environment variables
export JIRA_API_KEY="your-api-key"
export GITHUB_TOKEN="your-token"
export DATABASE_URL="your-db-url"

# Bootstrap scripts can access these
coding-context -s source=jira fix-bug | ai-agent

Token Count Monitoring

The CLI prints token estimates to stderr:

# See token count while piping to AI
coding-context fix-bug 2>&1 | tee >(grep -i token >&2) | ai-agent

# Or redirect stderr to file
coding-context fix-bug 2> tokens.log | ai-agent

Batch Processing

Process multiple tasks:

# Process multiple bug fixes
for issue in BUG-101 BUG-102 BUG-103; do
  coding-context \
    -p issue_key=$issue \
    /fix-bug | ai-agent > "fix-$issue.txt"
done

Custom AI Agent Scripts

Create a wrapper script for your preferred setup:

#!/bin/bash
# ai-fix-bug.sh

ISSUE_KEY=$1
DESCRIPTION=$2

coding-context \
  -s languages=go \
  -s priority=high \
  -p issue_key="$ISSUE_KEY" \
  -p description="$DESCRIPTION" \
  /fix-bug | llm -m claude-3-5-sonnet-20241022

Use with:

chmod +x ai-fix-bug.sh
./ai-fix-bug.sh BUG-123 "Application crashes on startup"

Handling Large Contexts

If your context exceeds token limits:

  1. Use selectors to reduce included rules: 
    coding-context -s priority=high fix-bug
  2. Use resume mode to skip rules: 
    coding-context -r fix-bug
  3. Split into multiple requests: 
    # First request: Planning
coding-context -s stage=planning plan-feature | ai-agent
   
# Second request: Implementation
coding-context -s stage=implementation implement-feature | ai-agent

See Also