U0519 Research Self Reflection Error Taxonomist

Research Self-Reflection Error Taxonomist

Why This Skill Exists

We need this skill because research throughput degrades without reproducibility and prioritization. This specific skill classifies recurrent reasoning failures for targeted fixes.

When To Use

Use this skill when the request explicitly needs "Research Self-Reflection Error Taxonomist" outcomes in the Scientific Research Systems domain.

Step-by-Step Implementation Guide

1. Define the scope and success metrics for Research Self-Reflection Error Taxonomist, including at least three measurable KPIs tied to non-reproducible results and wasted cycles.
2. Design and version the input/output contract for hypotheses, experiments, and replication evidence, then add schema validation and failure-mode handling.
3. Implement the core capability using failure-type clustering, and produce error taxonomies 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 non-reproducible results and wasted cycles, 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: general-capability using failure-type clustering to produce error taxonomies.
• Orchestration integration: scientific-research-systems:general-capability routing, approval gates, retries, and rollback controls.
• Validation evidence: unit, integration, simulation, regression-baseline suites and rollout telemetry.

Operational Runbook

Preflight

• Confirm the Research Self-Reflection Error Taxonomist request scope, source evidence, and measurable success criteria before execution.
• Verify feature flag skill_0519_research-self-reflection-error-t, approval gates, and rollback owner before autonomous use.

Execution

• Execute failure-type clustering with deterministic scoring and reproducible trace capture.
• Produce error taxonomies 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 error taxonomies, 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.