메뉴
Use when operating a deployed model, prompt, retrieval system, or agent and monitoring quality, drift, safety, tools, latency, cost, provider changes, feedback, rollout, rollback, retraining, or retirement.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/blackprince001/skills --skill ai-ml-operate-model-monitoring명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Always-on coding style and organization rules for naming, file shape, comments, types, module boundaries, pattern use, errors, and avoiding unnecessary abstraction across languages and tracks.
Use when setting up or modifying CI pipelines — the quality-gate sequence every change passes, shift-left ordering, feeding failures back to agents, and keeping the pipeline under 10 minutes.
Use when designing any public interface — REST/GraphQL endpoints, module contracts, component props — Hyrum's Law, contract-first, consistent error semantics, validate at boundaries, extend rather than break.
| name | ai-ml-operate-model-monitoring |
| description | Use when operating a deployed model, prompt, retrieval system, or agent and monitoring quality, drift, safety, tools, latency, cost, provider changes, feedback, rollout, rollback, retraining, or retirement. |
Apply core observability, incident response, feedback, maintenance, and release skills. AI telemetry adds behavior and data signals; it does not replace service health monitoring.
Tag each trace with application feature, release, model/provider, prompt, tool schema, retrieval index, guardrail, and experiment cohort versions. Track:
Do not log raw prompts, outputs, retrieved documents, or user identifiers by default. Sample and redact under explicit privacy, access, retention, and review policy.
Define baseline windows and thresholds before alerting. Drift is evidence to investigate, not an automatic retraining command. Separate data drift, concept/task drift, provider/model behavior change, prompt/retrieval regression, and product population change. Use delayed ground truth when available; otherwise maintain validated proxy metrics and routine sampled human review.
Canary behavioral changes by cohort, shadow them when effects can be evaluated safely, compare against the current baseline, and retain instant rollback of model, prompt, retrieval, tools, and guardrails as a compatible unit. Provider fallbacks preserve contracts and safety or enter an explicit degraded mode. Track provider deprecations and re-run evals before forced migrations.
Feed reviewed production failures into the eval dataset with provenance and privacy protection. Retrain, re-index, revise prompts, or replace models only under a recorded trigger, evaluation, approval, rollout, and rollback plan. Retire unused artifacts and data under retention policy.
An owner can identify the active behavioral versions, explain current quality/cost/latency/safety, trace a failed run, detect broken inputs or provider drift, obtain feedback/ground truth, and roll back without rebuilding the application.