U0085 Memory Confidence Calibration Engine

Memory Confidence Calibration Engine

Why This Skill Exists

We need this skill because agents lose performance when lessons are not retained and reused. This specific skill aligns reported confidence with actual uncertainty.

When To Use

Use this skill when the request explicitly needs "Memory Confidence Calibration Engine" outcomes in the Memory and Knowledge Operations domain.

Step-by-Step Implementation Guide

1. Define the scope and success metrics for Memory Confidence Calibration Engine, including at least three measurable KPIs tied to repeated mistakes and context loss.
2. Design and version the input/output contract for episodic logs, knowledge nodes, and retrieval metadata, then add schema validation and failure-mode handling.
3. Implement the core capability using calibration curves and error bins, and produce calibrated confidence scores with deterministic scoring.
4. Integrate the skill into swarm orchestration: task routing, approval gates, retry strategy, and rollback controls.
5. Add unit, integration, and simulation tests that explicitly cover repeated mistakes and context loss, then run regression baselines.
6. Deploy behind a feature flag, monitor telemetry/alerts for two release cycles, and iterate thresholds based on observed outcomes.

Required Deliverables

• Capability contract: input schema, deterministic scoring, output schema, and failure modes.
• Runtime profile: optimization-engine using calibration curves and error bins to produce calibrated confidence scores.
• Orchestration integration: memory-and-knowledge-operations:optimization-engine routing, approval gates, retries, and rollback controls.
• Validation evidence: unit, integration, simulation, regression-baseline suites and rollout telemetry.

Operational Runbook

Preflight

• Confirm the Memory Confidence Calibration Engine request scope, source evidence, and measurable success criteria before execution.
• Verify feature flag skill_0085_memory-confidence-calibration-en, approval gates, and rollback owner before autonomous use.

Execution

• Execute calibration curves and error bins with deterministic scoring and reproducible trace capture.
• Produce calibrated confidence scores plus scorecard, assumptions, and unresolved-risk notes.

Recovery

• Fail closed when required signals, evidence, or approval gates are missing.
• Rollback to the last stable baseline when posture is critical or validation fails.

Handoff

• Publish calibrated confidence scores, validation evidence, and telemetry links to downstream owners.
• Queue follow-up tasks for unresolved risks, threshold tuning, or approval review.

Guardrails

• [quality] Require deterministic scoring and validation evidence before promotion.
• [reliability] Preserve retries, rollback controls, and failure-mode evidence for every run.
• [safety] Route critical posture or missing approval gates to human review before autonomous action.