quality-gates

yonatangross/orchestkit · updated Apr 8, 2026

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$npx skills add https://github.com/yonatangross/orchestkit --skill quality-gates
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summary

This skill teaches agents how to assess task complexity, enforce quality gates, and prevent wasted work on incomplete or poorly-defined tasks.

skill.md

Quality Gates

This skill teaches agents how to assess task complexity, enforce quality gates, and prevent wasted work on incomplete or poorly-defined tasks.

Key Principle: Stop and clarify before proceeding with incomplete information. Better to ask questions than to waste cycles on the wrong solution.


Overview

Auto-Activate Triggers

  • Receiving a new task assignment
  • Starting a complex feature implementation
  • Before allocating work in Squad mode
  • When requirements seem unclear or incomplete
  • After 3 failed attempts at the same task
  • When blocked by dependencies

Manual Activation

  • User asks for complexity assessment
  • Planning a multi-step project
  • Before committing to a timeline

Core Concepts

Complexity Scoring (1-5 Scale)

Level Files Lines Time Characteristics
1 - Trivial 1 < 50 < 30 min No deps, no unknowns
2 - Simple 1-3 50-200 30 min - 2 hr 0-1 deps, minimal unknowns
3 - Moderate 3-10 200-500 2-8 hr 2-3 deps, some unknowns
4 - Complex 10-25 500-1500 8-24 hr 4-6 deps, significant unknowns
5 - Very Complex 25+ 1500+ 24+ hr 7+ deps, many unknowns

Load: Read("${CLAUDE_SKILL_DIR}/references/complexity-scoring.md") for detailed examples and assessment formulas.

Blocking Thresholds

Condition Threshold Action
YAGNI Gate Justified ratio > 2.0 BLOCK with simpler alternatives
YAGNI Warning Justified ratio 1.5-2.0 WARN with simpler alternatives
Critical Questions > 3 unanswered BLOCK
Missing Dependencies Any blocking BLOCK
Failed Attempts >= 3 BLOCK & ESCALATE
Evidence Failure 2 fix attempts BLOCK
Complexity Overflow Level 4-5 no plan BLOCK

WARNING Conditions (proceed with caution):

  • Level 3 complexity
  • 1-2 unanswered questions
  • 1-2 failed attempts

Load: Read("${CLAUDE_SKILL_DIR}/references/blocking-thresholds.md") for escalation protocols and decision logic.


References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

File Content
complexity-scoring.md Detailed Level 1-5 characteristics, quick assessment formula, checklist
blocking-thresholds.md BLOCKING vs WARNING conditions, escalation protocol, gate decision logic, attempt tracking
workflows.md Pre-task gate validation, stuck detection, complexity breakdown (Level 4-5), requirements completeness
gate-patterns.md Gate validation process templates, context system integration, common pitfalls
llm-quality-validation.md LLM-as-judge patterns, quality aspects, fail-open/closed strategies, graceful degradation, triple-consumer artifacts

Quick Reference

Gate Decision Flow

0. YAGNI check (runs FIRST — before any implementation planning)
   → Read project tier from scope-appropriate-architecture
   → Calculate justified_complexity = planned_LOC / tier_appropriate_LOC
   → If ratio > 2.0: BLOCK (must simplify)
   → If ratio 1.5-2.0: WARN (present simpler alternative)
   → Security patterns exempt from YAGNI gate

1. Assess complexity (1-5)
2. Count critical questions unanswered
3. Check dependencies blocked
4. Check attempt count

if (yagni_ratio > 2.0) -> BLOCK with simpler alternatives
else if (questions > 3 || deps blocked || attempts >= 3) -> BLOCK
else if (complexity >= 4 && no plan) -> BLOCK
else if (yagni_ratio > 1.5 || complexity == 3 || questions 1-2) -> WARNING
else -> PASS

Gate Check Template

## Quality Gate: [Task Name]

**Complexity:** Level [1-5]
**Unanswered Critical Questions:** [Count]
**Blocked Dependencies:** [List or None]
**Failed Attempts:** [Count]

**Status:** PASS / WARNING / BLOCKED
**Can Proceed:** Yes / No

Escalation Template

## Escalation: Task Blocked

**Task:** [Description]
**Block Type:** [Critical Questions / Dependencies / Stuck / Evidence]
**Attempts:** [Count]

### What Was Tried
1. [Approach 1] - Failed: [Reason]
2. [Approach 2] - Failed: [Reason]

### Need Guidance On
- [Specific question]

**Recommendation:** [Suggested action]

Integration with Context System

// Add gate check to context
context.quality_gates = context.quality_gates || [];
context.quality_gates.push({
  task_id: taskId,
  timestamp: new Date().toISOString(),
  complexity_score: 3,
  gate_status: 'pass', // pass, warning, blocked
  critical_questions_count: 1,
  unanswered_questions: 1,
  dependencies_blocked: 0,
  attempt_count: 0,
  can_proceed: true
});

Integration with Evidence System

// Before marking task complete
const evidence = context.quality_evidence;
const hasPassingEvidence = (
  evidence?.tests?.exit_code === 0 ||
  evidence?.build?.exit_code === 0
);

if (!hasPassingEvidence) {
  return { gate_status: 'blocked', reason: 'no_passing_evidence' };
}

Best Practices Pattern Library

Track success/failure patterns across projects to prevent repeating mistakes and proactively warn during code reviews.

Rule File Key Pattern
YAGNI Gate rules/yagni-gate.md Pre-implementation scope check, justified complexity ratio, simpler alternatives
Pattern Library rules/practices-code-standards.md Success/failure tracking, confidence scoring, memory integration
Review Checklist rules/practices-review-checklist.md Category-based review, proactive anti-pattern detection

Pattern Confidence Levels

Level Meaning Action
Strong success 3+ projects, 100% success Always recommend
Mixed results Both successes and failures Context-dependent
Strong anti-pattern 3+ projects, all failed Block with explanation

Common Pitfalls

Pitfall Problem Solution
Skip gates for "simple" tasks Get stuck later Always run gate check
Ignore WARNING status Undocumented assumptions cause issues Document every assumption
Not tracking attempts Waste cycles on same approach Track every attempt, escalate at 3
Proceed when BLOCKED Build wrong solution NEVER bypass BLOCKED gates


Related Skills

  • ork:scope-appropriate-architecture - Project tier detection that feeds YAGNI gate
  • ork:architecture-patterns - Enforce testing standards as part of quality gates
  • llm-evaluation - LLM-as-judge patterns for quality validation
  • ork:golden-dataset - Validate datasets meet quality thresholds

Key Decisions

Decision Choice Rationale
Complexity Scale 1-5 levels Granular enough for estimation, simple enough for quick assessment
Block Threshold 3 critical questions Prevents proceeding with too many unknowns
Escalation Trigger 3 failed attempts Balances persistence with avoiding wasted cycles
Level 4-5 Requirement Plan required Complex tasks need upfront decomposition

Capability Details

complexity-scoring

Keywords: complexity, score, difficulty, estimate, sizing, 1-5 scale Solves: How complex is this task? Score task complexity on 1-5 scale, assess implementation difficulty

blocking-thresholds

Keywords: blocking, threshold, gate, stop, escalate, cannot proceed Solves: When should I block progress? >3 critical questions = BLOCK, Missing dependencies = BLOCK

critical-questions

Keywords: critical questions, unanswered, unknowns, clarify Solves: What are critical questions? Count unanswered, block if >3

stuck-detection

Keywords: stuck, failed attempts, retry, 3 attempts, escalate Solves: How do I detect when stuck? After 3 failed attempts, escalate

gate-validation

Keywords: validate, gate check, pass, fail, gate status Solves: How do I validate quality gates? Run pre-task gate validation

pre-task-gate-check

Keywords: pre-task, before starting, can proceed Solves: How do I check gates before starting? Assess complexity, identify blockers

complexity-breakdown

Keywords: breakdown, decompose, subtasks, split task Solves: How do I break down complex tasks? Split Level 4-5 into Level 1-3 subtasks

requirements-completeness

Keywords: requirements, incomplete, acceptance criteria Solves: Are requirements complete enough? Check functional/technical requirements

escalation-protocol

Keywords: escalate, ask user, need help, human guidance Solves: When and how to escalate? Escalate after 3 failed attempts

llm-as-judge

Keywords: llm as judge, g-eval, aspect scoring, quality validation Solves: How do I use LLM-as-judge? Evaluate relevance, depth, coherence with thresholds

yagni-gate

Keywords: yagni, over-engineering, justified complexity, scope check, too complex, simplify Solves: Is this complexity justified? Calculate justified_complexity ratio against project tier, BLOCK if > 2.0, surface simpler alternatives

how to use quality-gates

How to use quality-gates on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add quality-gates
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/yonatangross/orchestkit --skill quality-gates

The skills CLI fetches quality-gates from GitHub repository yonatangross/orchestkit and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/quality-gates

Reload or restart Cursor to activate quality-gates. Access the skill through slash commands (e.g., /quality-gates) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.745 reviews
  • Zara Park· Dec 28, 2024

    quality-gates is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Charlotte Wang· Dec 28, 2024

    Useful defaults in quality-gates — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Dec 20, 2024

    Keeps context tight: quality-gates is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • James Shah· Dec 12, 2024

    Solid pick for teams standardizing on skills: quality-gates is focused, and the summary matches what you get after install.

  • James Sanchez· Nov 19, 2024

    quality-gates reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kaira Johnson· Nov 19, 2024

    quality-gates has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Piyush G· Nov 11, 2024

    Registry listing for quality-gates matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Wang· Nov 7, 2024

    quality-gates fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mei Sharma· Nov 3, 2024

    I recommend quality-gates for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Charlotte Gupta· Oct 26, 2024

    We added quality-gates from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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