U0115 Memory Community Feedback Harvester

Memory Community Feedback Harvester

Why This Skill Exists

We need this skill because agents lose performance when lessons are not retained and reused. This specific skill integrates lived user feedback into planning cycles.

When To Use

Use this skill when the request explicitly needs "Memory Community Feedback Harvester" outcomes in the Memory and Knowledge Operations domain.

Step-by-Step Implementation Guide

1. Define the scope and success metrics for Memory Community Feedback Harvester, 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 feedback normalization and clustering, and produce theme-prioritized feedback digests 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: normalization-engine using feedback normalization and clustering to produce theme-prioritized feedback digests.
• Orchestration integration: memory-and-knowledge-operations:normalization-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 Community Feedback Harvester request scope, source evidence, and measurable success criteria before execution.
• Verify feature flag skill_0115_memory-community-feedback-harves, approval gates, and rollback owner before autonomous use.

Execution

• Execute feedback normalization and clustering with deterministic scoring and reproducible trace capture.
• Produce theme-prioritized feedback digests 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 theme-prioritized feedback digests, 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.