Confidence Calibration Framework

When This Activates

This skill activates when:

•Expressing uncertainty about a suggestion
•Working in a domain with past errors
•User asks "how confident are you?"
•Making predictions or recommendations

Domain Tracking

The system tracks prediction accuracy across domains:

Domain Category	Examples
Infrastructure	docker, kubernetes, nginx, ci/cd
Frontend	react, react-native, nextjs, expo
Languages	typescript, javascript, python
Backend	firebase, firestore, authentication
Operations	testing, git, database, api
Optimization	performance, security, caching

Calibration Data Structure

json

{
  "domain_stats": {
    "docker": {
      "correct": 12,
      "incorrect": 3,
      "partial": 2,
      "accuracy": 0.71
    }
  },
  "overall": {
    "correct": 145,
    "incorrect": 23,
    "partial": 18
  }
}

How to Express Calibrated Confidence

High Confidence (>85% domain accuracy)

code

"This approach should work well - it follows established patterns."

Medium Confidence (60-85% accuracy)

code

"This is my best assessment, though you may want to verify [specific aspect]."

Low Confidence (<60% accuracy, or past errors in domain)

code

"I've had some misses in [domain] before. Let me double-check this..."
"I'm less certain here - consider testing thoroughly before proceeding."

Unknown Domain

code

"I don't have much track record in [area]. Proceed with appropriate caution."

Self-Awareness Triggers

When working in a domain with past errors:

•Check track record before making recommendations
•Acknowledge past mistakes if relevant: "I've gotten Docker networking wrong before..."
•Suggest verification for uncertain areas
•Ask clarifying questions rather than guessing

Recording Outcomes

When the user indicates an outcome:

Success signals:

•"That worked!"
•"Perfect"
•"Thanks, it's fixed"

Failure signals:

•"That didn't work"
•"Still broken"
•"Wrong"

Partial signals:

•"Almost"
•"Partly fixed"
•"One issue remaining"

Domain Detection Keywords

python

DOMAIN_KEYWORDS = {
    "docker": ["docker", "container", "dockerfile", "compose"],
    "react": ["react", "component", "jsx", "hooks", "useState"],
    "react-native": ["react native", "expo", "metro"],
    "nextjs": ["next.js", "nextjs", "getServerSideProps"],
    "typescript": ["typescript", "type", "interface"],
    "firebase": ["firebase", "firestore"],
    "authentication": ["auth", "login", "token", "jwt"],
    "testing": ["test", "jest", "mock", "coverage"],
    "git": ["git", "commit", "branch", "merge"],
    "performance": ["slow", "optimize", "cache", "memory"]
}

Integration with Learning System

Confidence data feeds into:

•<semantic-memory> context injection
•ReasoningBank for pattern matching
•Preference learner for style calibration

Example Workflow

code

User: "Set up Docker networking between containers"

1. Detect domain: docker
2. Check calibration: docker accuracy = 71%
3. Check past corrections: "Docker can't use Metal GPU on Mac"
4. Respond with calibrated confidence:

"For container networking, you'll want a bridge network.
Note: I've had some edge cases with Docker networking before,
so if this doesn't work immediately, the issue is usually
DNS resolution between containers."