ver Cloud APIs**: Keeps proprietary diffs on-device, eliminates rate limiting, and ensures offline functionality. Ollama's REST API is lightweight and predictable. 2. Modular Function Design: Separates repository validation, staging, LLM invocation, and commit execution. This enables unit testing, easier debugging, and future hook integration. 3. Configurable Parameters: Branch names, model selection, and prompt templates are externalized. Hardcoding master or phi3.5 breaks when teams rename default branches or upgrade models. 4. Defensive Error Handling: Uses set -euo pipefail to fail fast on missing dependencies, Ollama downtime, or Git state conflicts. Prevents partial commits or orphaned staging areas.

Implementation

Create a new utility script named ai-commit.sh. This version replaces the original monolithic structure with explicit functions, improved diff handling, and production-grade error management.

#!/usr/bin/env bash
set -euo pipefail

# Configuration defaults
AI_MODEL="${AI_MODEL:-phi3.5}"
TARGET_BRANCH="${TARGET_BRANCH:-main}"
OLLAMA_ENDPOINT="${OLLAMA_ENDPOINT:-http://localhost:11434}"
PROMPT_TEMPLATE="Generate a concise, imperative-style commit message summarizing these file changes. Focus on purpose and impact. Avoid implementation details. Format: <type>: <description>"

# Utility: Validate Git repository state
validate_repo() {
  if ! git rev-parse --git-dir >/dev/null 2>&1; then
    echo "❌ Error: Not a Git repository." >&2
    exit 1
  fi
}

# Utility: Check for staged or unstaged changes
check_changes() {
  if git diff-index --quiet HEAD -- && git diff --cached --quiet; then
    echo "ℹ️  No changes detected. Exiting."
    exit 0
  fi
}

# Utility: Stage all modifications and return changed paths
stage_and_list() {
  echo "📦 Staging modifications..."
  git add -A
  
  # Extract only file paths for context (avoids diff bloat)
  git diff --cached --name-only | tr '\n' ', ' | sed 's/,$//'
}

# Utility: Query Ollama for commit suggestion
invoke_llm() {
  local file_context="$1"
  local prompt="${PROMPT_TEMPLATE}\nFiles modified: ${file_context}"
  
  echo "🤖 Querying ${AI_MODEL} via Ollama..."
  
  # Use curl for explicit endpoint control and timeout handling
  local response
  response=$(curl -s --max-time 30 "${OLLAMA_ENDPOINT}/api/generate" \
    -d "{\"model\":\"${AI_MODEL}\",\"prompt\":\"${prompt}\",\"stream\":false}" 2>/dev/null)
  
  if [[ -z "$response" ]]; then
    echo "⚠️  Ollama unreachable or model not loaded. Falling back to manual input." >&2
    return 1
  fi
  
  # Extract message from JSON response (requires jq or basic parsing)
  echo "$response" | grep -o '"response":"[^"]*"' | head -1 | cut -d'"' -f4
}

# Utility: Execute commit with user confirmation
execute_commit() {
  local suggested_msg="$1"
  local final_msg=""
  
  echo "💡 Suggested message: ${suggested_msg}"
  read -rp "Accept? (y/n/e to edit): " choice
  
  case "$choice" in
    [Yy]*) final_msg="$suggested_msg" ;;
    [Ee]*)
      echo "📝 Enter commit message (Ctrl+D to finish):"
      final_msg=$(cat)
      ;;
    *)
      echo "❌ Commit aborted."
      exit 0
      ;;
  esac
  
  if [[ -z "$final_msg" ]]; then
    echo "❌ Empty commit message. Aborting." >&2
    exit 1
  fi
  
  git commit -m "$final_msg"
  echo "✅ Committed successfully."
  
  # Optional: Push to remote
  read -rp "Push to ${TARGET_BRANCH}? (y/n): " push_choice
  if [[ "$push_choice" =~ ^[Yy]$ ]]; then
    git push origin "$TARGET_BRANCH"
    echo "🚀 Pushed to remote."
  fi
}

# Main execution flow
main() {
  validate_repo
  check_changes
  
  local changed_files
  changed_files=$(stage_and_list)
  
  local ai_suggestion
  ai_suggestion=$(invoke_llm "$changed_files") || ai_suggestion="feat: update codebase"
  
  execute_commit "$ai_suggestion"
}

main "$@"

Why This Structure Works

• set -euo pipefail: Prevents silent failures. If curl times out or git encounters a conflict, the script exits immediately instead of leaving the repository in a half-committed state.
• File-only context extraction: Passing full diffs to LLMs wastes tokens and increases latency. Extracting --name-only provides sufficient context for accurate summarization while keeping the prompt under 500 tokens.
• Explicit endpoint control: Using curl instead of ollama run gives precise timeout handling, JSON parsing control, and easier debugging when Ollama is under heavy load.
• Fallback mechanism: If Ollama is unreachable, the script defaults to a generic imperative message rather than failing entirely, ensuring workflow continuity.

Pitfall Guide

1. Blindly Staging Untracked Files

Explanation: git add -A stages everything, including build artifacts, IDE configs, and temporary files. This pollutes commit history and triggers CI failures. Fix: Use git add -u for tracked files only, or implement a .gitignore audit step. Consider staging selectively via git add -p in interactive mode.

2. Ignoring Ollama Cold-Start Latency

Explanation: Local models load into VRAM/RAM on first invocation. The initial request can take 5–15 seconds, causing developers to assume the script hung. Fix: Pre-warm the model in a background service or add a startup check: ollama list | grep -q "$AI_MODEL" || ollama pull "$AI_MODEL". Display a spinner or progress indicator during inference.

3. Hardcoding Default Branch Names

Explanation: Assuming master or main breaks when teams use develop, trunk, or feature-specific defaults. Fix: Dynamically resolve the default branch: git remote show origin | grep 'HEAD branch' | awk '{print $NF}'. Store it in TARGET_BRANCH or read from .git/config.

4. Prompt Context Overflow

Explanation: Large repositories with 50+ changed files can exceed token limits, causing truncated responses or model hallucination. Fix: Limit context to the first 20 files. Add a summary line: echo "📄 Context limited to top 20 files. Review manually if needed." before invoking the LLM.

5. NTFS/WSL Permission Mismatches

Explanation: Windows NTFS doesn't natively support Unix executable bits. Scripts copied from WSL or Git Bash may fail with Permission denied despite chmod +x. Fix: Run chmod +x ai-commit.sh inside the WSL2 terminal, not PowerShell. Alternatively, invoke explicitly: bash ai-commit.sh or create a Git alias that bypasses execution bit checks.

6. Failing to Handle Ollama API Errors

Explanation: Ollama may return HTTP 503 (model loading) or malformed JSON if interrupted. Unhandled responses break the commit flow. Fix: Validate JSON structure before parsing. Use jq if available, or implement strict regex fallbacks. Add retry logic with exponential backoff for transient failures.

7. Over-Reliance on AI Without Verification

Explanation: Accepting AI suggestions blindly can introduce inaccurate metadata, especially for complex refactors or multi-purpose commits. Fix: Always present the suggestion for review. Enforce a mandatory y/n/e prompt. Train teams to treat AI output as a draft, not a final artifact.

Production Bundle

Action Checklist

• Verify Ollama is running and phi3.5 is pulled: ollama list | grep phi3.5
• Place ai-commit.sh in ~/bin or a project-specific scripts/ directory
• Set executable permissions: chmod +x ai-commit.sh
• Add directory to PATH in ~/.bashrc or ~/.zshrc: export PATH="$HOME/bin:$PATH"
• Test in a non-critical repository: ai-commit.sh
• Configure fallback branch detection if not using main
• Add to Git alias for faster invocation: git config --global alias.ai '!bash ~/bin/ai-commit.sh'

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Solo developer / Open source	Local Ollama pipeline	Zero API cost, full privacy, offline capable	$0 (hardware dependent)
Enterprise / Proprietary codebase	Local Ollama + strict review	Keeps code context on-device, meets compliance	$0 (infrastructure only)
High-frequency CI/CD pipelines	Cloud API with rate limiting	Faster inference, scalable, managed uptime	$5–$50/month per team
Legacy systems / Low RAM	Manual + commitlint	Avoids VRAM/RAM overhead, deterministic	$0 (developer time only)
Multi-language monorepo	Local Ollama + selective staging	Handles diverse file types, reduces context noise	$0 (requires prompt tuning)

Configuration Template

Create a .ai-commit.env file in your project root or home directory to externalize settings:

# .ai-commit.env
AI_MODEL="phi3.5"
OLLAMA_ENDPOINT="http://localhost:11434"
TARGET_BRANCH="main"
PROMPT_TEMPLATE="Generate a concise, imperative-style commit message. Focus on purpose and impact. Avoid implementation details. Format: <type>: <description>"
MAX_CONTEXT_FILES=20
TIMEOUT_SECONDS=30

Load it in the script before execution:

if [[ -f ".ai-commit.env" ]]; then
  source ".ai-commit.env"
elif [[ -f "$HOME/.ai-commit.env" ]]; then
  source "$HOME/.ai-commit.env"
fi

Quick Start Guide

1. Install Ollama: Download from ollama.ai, run ollama pull phi3.5, and verify with ollama list.
2. Deploy the script: Save ai-commit.sh to ~/bin, run chmod +x ~/bin/ai-commit.sh, and add export PATH="$HOME/bin:$PATH" to your shell profile.
3. Initialize configuration: Create ~/.ai-commit.env with your preferred model, branch, and prompt template.
4. Test the workflow: Navigate to any Git repository, make a change, and run ai-commit.sh. Accept or edit the suggestion, then push.
5. Optional Git alias: Run git config --global alias.ai '!bash ~/bin/ai-commit.sh' to invoke via git ai from any directory.

This pipeline transforms commit generation from a manual bottleneck into a deterministic, context-aware process. By keeping inference local, enforcing structured prompts, and maintaining human-in-the-loop validation, teams gain consistent metadata without sacrificing privacy or workflow speed.