home / skills / lerianstudio / ring / linting-codebase
This skill performs parallel lint checks, groups issues into independent streams, and dispatches AI agents to fix all lint issues until clean.
npx playbooks add skill lerianstudio/ring --skill linting-codebaseReview the files below or copy the command above to add this skill to your agents.
---
name: ring:linting-codebase
description: |
Parallel lint fixing pattern - runs lint checks, groups issues into independent
streams, and dispatches AI agents to fix all issues until the codebase is clean.
trigger: |
- User runs /ring:lint command
- Codebase has lint issues that need fixing
- Multiple lint errors across different files/components
skip_when: |
- Single lint error → fix directly without agent dispatch
- Lint already passes → nothing to do
- User only wants to see lint output, not fix
---
# Linting Codebase
## Overview
This skill runs lint checks on the codebase, analyzes the results to identify independent fix streams, and dispatches parallel AI agents to fix all issues. The process iterates until the codebase passes all lint checks.
**Core principle:** Group lint issues by file/component, dispatch one agent per independent stream, iterate until clean.
## ⛔ CRITICAL CONSTRAINTS
These constraints are NON-NEGOTIABLE and must be communicated to ALL dispatched agents:
```
┌─────────────────────────────────────────────────────────────────┐
│ 🚫 DO NOT CREATE AUTOMATED SCRIPTS TO FIX LINT ISSUES │
│ 🚫 DO NOT CREATE DOCUMENTATION OR README FILES │
│ 🚫 DO NOT ADD COMMENTS EXPLAINING THE FIXES │
│ ✅ FIX EACH ISSUE DIRECTLY BY EDITING THE SOURCE CODE │
│ ✅ MAKE MINIMAL CHANGES - ONLY WHAT'S NEEDED FOR LINT │
└─────────────────────────────────────────────────────────────────┘
```
## Phase 1: Lint Execution
### Step 1.1: Detect Lint Command
Priority: `make lint` → `npm run lint` → `yarn lint` → `pnpm lint` → `golangci-lint run` → `cargo clippy` → `ruff check .` → `eslint .`
### Step 1.2: Run Lint
`<lint_command> 2>&1 | tee /tmp/ring:lint-output.txt && echo "EXIT_CODE: $?"`
### Step 1.3: Parse Results
Extract: file path, line:column, error code/rule, message, severity (error/warning).
## Phase 2: Stream Analysis
### Step 2.1: Group Issues
Group lint issues into independent streams that can be fixed in parallel:
**Grouping strategies (choose based on issue count):**
| Issue Count | Grouping Strategy |
|-------------|-------------------|
| < 10 issues | Group by file |
| 10-50 issues | Group by directory |
| 50-100 issues | Group by error type/rule |
| > 100 issues | Group by component/module |
### Step 2.2: Identify Independence
A stream is independent if: files don't import/depend on each other, fixes won't conflict, agents can work without knowledge of other streams.
### Step 2.3: Create Stream Summary
Output format: Total issues, Streams (path, issue types, count, independence status), Recommended agents (one per stream).
## Phase 3: Parallel Agent Dispatch
### Step 3.1: Prepare Agent Prompts
Each agent receives: **Scope** (files/directories), **Issues** (file:line:col + message), **Constraints** (from Critical Constraints above), **Output** (files modified, issues fixed, issues unable to fix with reasons).
### Step 3.2: Dispatch Agents in Parallel
**CRITICAL: Single message with multiple Task tool calls** - one `general-purpose` agent per stream.
### Step 3.3: Await All Agents
Wait for all dispatched agents to complete before proceeding.
## Phase 4: Verification Loop
### Step 4.1: Re-run Lint
After all agents complete, run `<lint_command> 2>&1`.
### Step 4.2: Evaluate Results
| Result | Action |
|--------|--------|
| **Lint passes** | ✅ Done |
| **Same issues remain** | ⚠️ Investigate why fixes failed |
| **New issues appeared** | 🔄 Analyze + dispatch new agents |
| **Fewer issues remain** | 🔄 Create new streams, repeat |
### Step 4.3: Iterate If Needed
**Maximum iterations:** 5. If issues persist: report remaining, ask user, investigate (lint conflicts, auto-fix impossible).
## Agent Dispatch Rules
### DO dispatch when:
- 3+ files have lint issues
- Issues are in independent areas
- Fixes are mechanical (unused vars, formatting, etc.)
### DO NOT dispatch when:
- Single file has issues → fix directly
- Issues require architectural decisions
- Fixes would cause breaking changes
### Agent selection:
| Issue Type | Agent Type |
|------------|------------|
| TypeScript/JavaScript | `general-purpose` |
| Go | `general-purpose` or `ring:backend-engineer-golang` |
| Security lints | `ring:security-reviewer` for analysis first |
| Style/formatting | `general-purpose` |
## Output Format
**Success:** Initial issues, Streams processed, Agents dispatched, Iterations, Final status (all pass), Changes by stream (files, issues fixed).
**Partial:** Initial/fixed/remaining issues, Iterations (max reached), Remaining issues with reasons (e.g., requires external types, intentional usage), Recommended actions (manual review, lint exceptions, type definitions).
## Error Handling
| Error | Response |
|-------|----------|
| **Lint command not found** | Ask user to specify command |
| **Agent failure** | Options: retry stream, skip, investigate manually |
| **Conflicting changes** | Report file + lines, ask user to merge manually |
## Integration with Other Skills
| Skill | When to use |
|-------|-------------|
| `ring:dispatching-parallel-agents` | Pattern basis for this skill |
| `ring:systematic-debugging` | If lint errors indicate deeper issues |
| `ring:requesting-code-review` | After lint passes, before merge |
## Example Session
`/ring:lint` → Run lint → 16 issues in 3 areas → Analyze streams (API: 5, Services: 8, Utils: 3) → Dispatch 3 parallel agents → All complete → Re-run lint → ✅ All pass.
This skill automates lint-driven code cleanup by running lint checks, grouping issues into independent fix streams, and dispatching parallel AI agents to remediate them until the codebase is clean. It enforces strict constraints to ensure fixes are minimal, direct edits to source, and no generated scripts or explanatory artifacts. The process iterates with verification runs and stops when lint passes or a max iteration limit is reached.
The skill detects and runs the preferred lint command, captures and parses lint output into structured issues (file, location, rule, message, severity), and groups those issues into independent streams suitable for parallel work. For each stream it prepares an agent prompt that includes scope, issue list, and the mandatory non-negotiable constraints, then dispatches one general-purpose agent per stream in parallel. After agents complete, it re-runs lint, evaluates results, and repeats up to five iterations or reports remaining problems.
What are the mandatory constraints for agents?
Agents must not create automated scripts, documentation, or explanatory comments; they must edit source files directly and make minimal changes only to fix lint issues.
How many iterations will the skill attempt?
The system will iterate up to five times; if issues persist it reports remaining issues and recommends manual investigation.