linter-agent

name	linter-agent
description	Detect and fix code style violations, enforce project conventions, and ensure consistent formatting across the codebase. Use when cleaning lint errors before PRs, migrating linters, or bulk-applying new rules.
domain	agents
tags	["agent","ai-agent","automation","linter","orchestration"]

Linter Agent

When to Use

Trigger phrases:

• "linter agent"

• "Cleaning up lint errors before a PR"

• "Applying a new linting rule across the entire codebase"

• "Formatting code after a merge conflict resolution"

• Cleaning up lint errors before a PR

• Applying a new linting rule across the entire codebase

• Formatting code after a merge conflict resolution

• Enforcing consistent import ordering

• Removing unused imports and dead code patterns

• Setting up linting for a project that has none

• Migrating to a new linter or new configuration

When NOT to Use

• When the task is simple enough for a single command
• When real-time human judgment is required
• When the agent lacks access to required tools or data

Overview

Linter Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.

Capabilities

• Autonomous operation — Execute multi-step linter agent workflows independently
• Context awareness — Adapt behavior based on current state and history
• Error recovery — Handle failures gracefully with retry and fallback logic
• Integration — Connect with external tools and services as needed

Workflow

# Example: Agent orchestration
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    priority: int
    assigned_agent: str

def orchestrate(tasks: list[Task]) -> dict:
    results = {}
    for task in sorted(tasks, key=lambda t: t.priority):
        results[task.name] = execute(task)
    return results

1. Initialize — Set up the agent context and load required resources
2. Plan — Break down the task into executable steps
3. Execute — Run each step, monitoring for errors and adapting as needed
4. Verify — Validate results against acceptance criteria
5. Report — Summarize outcomes and suggest next steps

Configuration

• Define task objectives and constraints clearly
• Set appropriate timeout and retry limits
• Configure tool access and permissions
• Enable logging for debugging and audit

Anti-Rationalization

Rationalization	Reality
"I will just do it manually"	Agents automate repetitive tasks — manual work does not scale
"The agent will figure it out"	Without clear instructions, agents hallucinate. Give explicit context.
"One agent is enough"	Complex tasks benefit from specialized agents working in parallel

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run linter agent workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings