thoroughness — community thoroughness, community, ide skills

v1.0.0

About this Skill

Ideal for meticulous AI Agents like Claude Code and AutoGPT that require comprehensive implementation of complex tasks without shortcuts. Use when implementing complex multi-step tasks, fixing critical bugs, or when quality and completeness matter more than speed - ensures comprehensive implementation without shortcuts through systemati

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Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
45
Canonical Locale
en
Detected Body Locale
en

Ideal for meticulous AI Agents like Claude Code and AutoGPT that require comprehensive implementation of complex tasks without shortcuts. Use when implementing complex multi-step tasks, fixing critical bugs, or when quality and completeness matter more than speed - ensures comprehensive implementation without shortcuts through systemati

Core Value

Empowers agents to perform exhaustive debugging, refactoring, and testing using protocols like comprehensive analysis and multi-step feature implementation, ensuring high-quality and complete solutions in critical tasks such as fixing bugs, compilation errors, and production deployments.

Ideal Agent Persona

Ideal for meticulous AI Agents like Claude Code and AutoGPT that require comprehensive implementation of complex tasks without shortcuts.

Capabilities Granted for thoroughness

Debugging critical test failures
Implementing complex multi-step features
Refactoring large codebases for production deployments

! Prerequisites & Limits

  • Requires significant computational resources and time
  • May not be suitable for tasks where speed is prioritized over quality

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is thoroughness?

Ideal for meticulous AI Agents like Claude Code and AutoGPT that require comprehensive implementation of complex tasks without shortcuts. Use when implementing complex multi-step tasks, fixing critical bugs, or when quality and completeness matter more than speed - ensures comprehensive implementation without shortcuts through systemati

How do I install thoroughness?

Run the command: npx killer-skills add pr-pm/prpm/thoroughness. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for thoroughness?

Key use cases include: Debugging critical test failures, Implementing complex multi-step features, Refactoring large codebases for production deployments.

Which IDEs are compatible with thoroughness?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for thoroughness?

Requires significant computational resources and time. May not be suitable for tasks where speed is prioritized over quality.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add pr-pm/prpm/thoroughness. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use thoroughness immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

thoroughness

Install thoroughness, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Thoroughness

Purpose

This skill ensures comprehensive, complete implementation of complex tasks without shortcuts. Use this when quality and completeness matter more than speed.

When to Use

  • Fixing critical bugs or compilation errors
  • Implementing complex multi-step features
  • Debugging test failures
  • Refactoring large codebases
  • Production deployments
  • Any task where shortcuts could cause future problems

Methodology

Phase 1: Comprehensive Analysis (20% of time)

  1. Identify All Issues

    • List every error, warning, and failing test
    • Group related issues together
    • Prioritize by dependency order
    • Create issue hierarchy (what blocks what)
  2. Root Cause Analysis

    • Don't fix symptoms, find root causes
    • Trace errors to their source
    • Identify patterns in failures
    • Document assumptions that were wrong
  3. Create Detailed Plan

    • Break down into atomic steps
    • Estimate time for each step
    • Identify dependencies between steps
    • Plan verification for each step
    • Schedule breaks/checkpoints

Phase 2: Systematic Implementation (60% of time)

  1. Fix Issues in Dependency Order

    • Start with foundational issues
    • Fix one thing completely before moving on
    • Test after each fix
    • Document what was changed and why
  2. Verify Each Fix

    • Write/run tests for the specific fix
    • Check for side effects
    • Verify related functionality still works
    • Document test results
  3. Track Progress

    • Mark issues as completed
    • Update plan with new discoveries
    • Adjust time estimates
    • Note any blockers immediately

Phase 3: Comprehensive Verification (20% of time)

  1. Run All Tests

    • Unit tests
    • Integration tests
    • E2E tests
    • Manual verification
  2. Cross-Check Everything

    • Review all changed files
    • Verify compilation succeeds
    • Check for console errors/warnings
    • Test edge cases
  3. Documentation

    • Update relevant docs
    • Add inline comments for complex fixes
    • Document known limitations
    • Create issues for future work

Anti-Patterns to Avoid

  • ❌ Fixing multiple unrelated issues at once
  • ❌ Moving on before verifying a fix works
  • ❌ Assuming similar errors have the same cause
  • ❌ Skipping test writing "to save time"
  • ❌ Copy-pasting solutions without understanding
  • ❌ Ignoring warnings "because it compiles"
  • ❌ Making changes without reading existing code first

Quality Checkpoints

  • Can I explain why this fix works?
  • Have I tested this specific change?
  • Are there any side effects?
  • Is this the root cause or a symptom?
  • Will this prevent similar issues in the future?
  • Is the code readable and maintainable?
  • Have I documented non-obvious decisions?

Example Workflow

Bad Approach (Shortcut-Driven)

1. See 24 TypeScript errors
2. Add @ts-ignore to all of them
3. Hope tests pass
4. Move on

Good Approach (Thoroughness-Driven)

1. List all 24 errors systematically
2. Group by error type (7 missing types, 10 unknown casts, 7 property access)
3. Find root causes:
   - Missing @types/tar package
   - No type assertions on fetch responses
   - Implicit any types in callbacks
4. Fix by category:
   - Install @types/tar (fixes 7 errors)
   - Add proper type assertions to registry-client.ts (fixes 10 errors)
   - Add explicit parameter types (fixes 7 errors)
5. Test after each category
6. Run full test suite
7. Document what was learned

Time Investment

  • Initial: 2-3x slower than shortcuts
  • Long-term: 10x faster (no debugging later, no rework)
  • Quality: Near-perfect first time
  • Maintenance: Minimal

Success Metrics

  • ✅ 100% of tests passing
  • ✅ Zero warnings in production build
  • ✅ All code has test coverage
  • ✅ Documentation is complete and accurate
  • ✅ No known issues or TODOs left behind
  • ✅ Future developers can understand the code

Mantras

  • "Slow is smooth, smooth is fast"
  • "Do it right the first time"
  • "Test everything, assume nothing"
  • "Document for your future self"
  • "Root causes, not symptoms"

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