Vibe Check MCP
by PV-Bhat
Verified
# Advanced Integration Techniques
For optimal metacognitive oversight, these advanced integration strategies leverage the full power of Vibe Check as a pattern interrupt system, recalibration mechanism, and self-improving feedback loop.
## Progressive Confidence Levels
Start with lower confidence values (e.g., 0.5) during planning phases and increase confidence (e.g., 0.7-0.9) during implementation and review phases. This adjusts the intensity of pattern interrupts to match the current stage of development.
```javascript
// Planning phase - lower confidence for more thorough questioning
vibe_check({
phase: "planning",
confidence: 0.5,
userRequest: "...",
plan: "..."
})
// Implementation phase - higher confidence for focused feedback
vibe_check({
phase: "implementation",
confidence: 0.7,
userRequest: "...",
plan: "..."
})
// Review phase - highest confidence for minimal, high-impact feedback
vibe_check({
phase: "review",
confidence: 0.9,
userRequest: "...",
plan: "..."
})
```
## Feedback Chaining
Incorporate previous vibe_check feedback in subsequent calls using the `previousAdvice` parameter to build a coherent metacognitive narrative. This creates a more sophisticated pattern interrupt system that builds on past insights.
```javascript
const initialFeedback = await vibe_check({
phase: "planning",
userRequest: "...",
plan: "..."
});
// Later, include previous feedback
const followupFeedback = await vibe_check({
phase: "implementation",
previousAdvice: initialFeedback,
userRequest: "...",
plan: "..."
});
```
## Strategic Recalibration Points
Use vibe_distill as a meta-thinking anchor at key points in complex workflows to prevent drift and maintain alignment. This creates essential recalibration opportunities for the agent.
```javascript
// Initial planning
const plan = "...detailed complex plan...";
// When complexity increases, create a recalibration anchor
if (measureComplexity(plan) > THRESHOLD) {
const distilledPlan = await vibe_distill({
plan: plan,
userRequest: "..."
});
// Use the distilled plan as a new anchor point
updatedPlan = distilledPlan;
}
```
## Self-Improving Feedback Loop
Use vibe_learn consistently to build a pattern library specific to your agent's tendencies. This creates a self-improving system that gets better at identifying and preventing errors over time.
```javascript
// After resolving an issue
vibe_learn({
mistake: "Relied on unnecessary complexity for simple data transformation",
category: "Complex Solution Bias",
solution: "Used built-in array methods instead of custom solution"
});
// Later, the pattern library will improve vibe_check's pattern recognition
// allowing it to spot similar issues earlier in future workflows
```
## Hybrid Oversight Model
Combine automated pattern interrupts at predetermined checkpoints with ad-hoc checks when uncertainty or complexity increases.
```javascript
// Scheduled checkpoint at the end of planning
const scheduledCheck = await vibe_check({
phase: "planning",
userRequest: "...",
plan: "..."
});
// Ad-hoc check when complexity increases
if (measureComplexity(currentPlan) > THRESHOLD) {
const adHocCheck = await vibe_check({
phase: "implementation",
userRequest: "...",
plan: "...",
focusAreas: ["complexity", "simplification"]
});
}
```
## Complete Integration Example
Here's a comprehensive implementation example for integrating Vibe Check as a complete metacognitive system:
```javascript
// During planning phase
const planFeedback = await vibe_check({
phase: "planning",
confidence: 0.5,
userRequest: "[COMPLETE USER REQUEST]",
plan: "[AGENT'S INITIAL PLAN]"
});
// Consider feedback and potentially adjust plan
const updatedPlan = adjustPlanBasedOnFeedback(initialPlan, planFeedback);
// If plan seems complex, create a recalibration anchor point
if (planComplexity(updatedPlan) > COMPLEXITY_THRESHOLD) {
const simplifiedPlan = await vibe_distill({
plan: updatedPlan,
userRequest: "[COMPLETE USER REQUEST]"
});
// Use the simplified plan as a new anchor point
finalPlan = simplifiedPlan;
} else {
finalPlan = updatedPlan;
}
// During implementation, create pattern interrupts before major actions
const implementationFeedback = await vibe_check({
phase: "implementation",
confidence: 0.7,
previousAdvice: planFeedback,
userRequest: "[COMPLETE USER REQUEST]",
plan: `I'm about to [DESCRIPTION OF PENDING ACTION]`
});
// After completing the task, build the self-improving feedback loop
if (mistakeIdentified) {
await vibe_learn({
mistake: "Specific mistake description",
category: "Complex Solution Bias", // or appropriate category
solution: "How it was corrected"
});
}
```
This integrated approach creates a complete metacognitive system that provides pattern interrupts when needed, recalibration anchor points when complexity increases, and a self-improving feedback loop that gets better over time.