القائمة
Iterative quality improvement — generate, evaluate, feedback, re-generate until threshold met
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/fabioc-aloha/alex-cognitive-architecture --skill ralph-loopيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Run the 7-step Artificial Critical Thinking pass — Materiality → Hypothesise → Alternatives → Disconfirmers → Audit priors → Severity → Commit-with-marker
Challenge what you think is right — alternative hypotheses, missing data, evidence quality, bias detection, falsifiability, and adversarial review
Step-back protocol — restate, generalise, specialise, invert, ask why, pre-mortem, check stakeholders, and audit framings before solving
| name | ralph-loop |
| description | Iterative quality improvement — generate, evaluate, feedback, re-generate until threshold met |
| applyTo | **/*quality*,**/*eval*,**/*iterate*,**/*ralph* |
| domain | Quality Engineering |
| category | Iterative Improvement |
| tier | standard |
| dependencies | ["testing-strategies","code-review"] |
| created | 2026-04-14T00:00:00.000Z |
| author | Alex |
| source | microsoft/skills (Sensei technique by Shayne Boyer) |
| currency | 2026-04-22T00:00:00.000Z |
Generate → Evaluate → Feedback → Re-generate until quality threshold met.
Inspired by the Sensei iterative quality improvement patterns from GitHub Copilot for Azure:
"LLMs improve dramatically when given structured feedback about what went wrong and specific guidance on how to fix it."
— Shayne Boyer (@spboyer), Microsoft
┌─────────────────────────────────────────────────────────────┐
│ RALPH LOOP │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Generate │────▶│ Evaluate │────▶│ Analyze │ │
│ └─────────┘ └──────────┘ └──────────┘ │
│ ▲ │ │
│ │ ┌──────────────────────┘ │
│ │ ▼ │
│ │ ┌──────────┐ ┌───────────┐ │
│ └────│ Feedback │◀────│ Threshold │ │
│ └──────────┘ │ Met? │ │
│ └───────────┘ │
│ │ Yes │
│ ▼ │
│ ┌────────┐ │
│ │ Done │ │
│ └────────┘ │
└─────────────────────────────────────────────────────────────┘
| Parameter | Default | Description |
|---|---|---|
maxIterations | 5 | Maximum iterations before stopping |
qualityThreshold | 80 | Score (0-100) to consider quality met |
improvementThreshold | 5 | Minimum improvement required per iteration |
earlyStopOnPerfect | true | Stop immediately when score reaches 100 |
includeFeedback | true | Include feedback in re-generation prompts |
qualityThresholdimprovementThreshold for 2+ iterationsmaxIterationsCriteria define what "good" looks like:
{
"skillName": "cosmos-db-patterns",
"language": "typescript",
"correctPatterns": [
{
"code": "container.items.query()",
"description": "Use container.items.query() for parameterized queries",
"section": "query-patterns"
}
],
"incorrectPatterns": [
{
"code": "SELECT * FROM c",
"description": "Avoid SELECT * — specify required fields",
"section": "query-patterns"
}
],
"rules": [
{
"name": "error-handling",
"requiredPatterns": ["try {", "catch ("],
"forbiddenPatterns": ["// eslint-disable"]
}
]
}
Transforms evaluation findings into LLM-actionable feedback:
{
"score": 65,
"passed": false,
"findings": [
{
"severity": "error",
"rule": "pattern:query-patterns",
"message": "Incorrect pattern found: Avoid SELECT * — specify required fields"
}
],
"matchedIncorrect": ["query-patterns"]
}
## Issues Found in Generated Content
### CRITICAL ERRORS (Must Fix)
- **pattern:query-patterns**: Incorrect pattern found: Avoid SELECT * — specify required fields
- Suggestion: Review acceptance criteria for correct usage
### INCORRECT PATTERNS DETECTED
Found incorrect patterns in sections: **query-patterns**
Review the acceptance criteria for these sections and use correct patterns instead.
### SUGGESTED CORRECTIONS
Based on the acceptance criteria, consider:
- Use: Use container.items.query() for parameterized queries
# Evaluate content against criteria
node ralph-loop.cjs --evaluate --content "SELECT * FROM c" --criteria criteria.json
# Evaluate from file
node ralph-loop.cjs --evaluate --content-file output.ts --criteria criteria.json
# Extract criteria from SKILL.md
node ralph-loop.cjs --extract-criteria --skill .github/skills/cosmos-db/SKILL.md
# Build feedback from findings
node ralph-loop.cjs --feedback --findings '[{"severity":"error","rule":"syntax","message":"Missing semicolon"}]'
# Stdin mode (for extension integration)
echo '{"content":"...","criteria":{...}}' | node ralph-loop.cjs --stdin
import { ContentEvaluator, FeedbackBuilder, RalphLoopController } from './ralph-loop';
// Create evaluator with criteria
const criteria = loadSkillCriteria('cosmos-db-patterns');
const controller = new RalphLoopController(criteria, {
maxIterations: 3,
qualityThreshold: 85,
});
// Run loop with Copilot LLM
const result = await controller.run(
async (prompt) => await copilot.generate(prompt),
"Write a Cosmos DB query for user lookup",
"user-query-scenario"
);
console.log(`Final score: ${result.finalScore}`);
console.log(`Converged: ${result.converged}`);
console.log(`Iterations: ${result.iterations.length}`);
const evaluator = new ContentEvaluator(criteria);
const feedbackBuilder = new FeedbackBuilder();
const result = evaluator.evaluate(generatedCode, 'my-scenario');
const feedback = feedbackBuilder.buildFeedback(result, criteria);
if (!result.passed) {
console.log(feedback); // Give to LLM for improvement
}
Track these across iterations:
| Metric | Description |
|---|---|
score | Overall quality score (0-100) |
errorCount | Number of critical errors |
warningCount | Number of warnings |
matchedCorrect | Correct patterns found |
matchedIncorrect | Incorrect patterns found |
improvement | Score change from first to last iteration |
converged | Whether quality threshold was met |
| Don't | Do |
|---|---|
Set maxIterations too high (>5) | Usually 3-5 iterations sufficient |
Set qualityThreshold at 100 | 80-90 is realistic for most use cases |
| Include vague criteria | Be specific with patterns and rules |
| Skip feedback on failure | Always provide structured feedback |