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Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Save a page's SEO settings and compare them with later versions to detect changes after updates or deployments. Review differences in titles, headings, indexing controls, structured data, and available performance metrics, with severity levels and stored comparison history.

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• Compare SEO settings before and after deployment
• Check for unintended title or heading changes
• Investigate page changes after a traffic drop

Establish baseline → snapshot → compare → history monitoring for any KPI, config, or metric that can drift during PRD v0.8 Deployment & Ops. Triggers on requests to monitor drift, baseline a value for later comparison, or when user asks "how do we track if X changes?", "baseline this", "config drift", "performance regression", "metric drift", "compare to last week", "is this getting worse?". Outputs MON-DRIFT-* entries with baseline + comparison rules.

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Detect unexpected changes in running containers by comparing them with their original images and monitoring execution and file activity. Use Falco or Microsoft Defender for Containers to investigate changes and guide isolation, replacement, and preventive configuration.

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• Finding unexpected binaries executed inside running containers
• Investigating package installations and file changes at runtime
• Checking whether containers still match their approved images

Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘

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Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘

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The re-measure and standing report of the canonical data model's live DB conformance. Use when asked 'where are we at' on DB drift or conformance, to update the drift numbers, when a drift-audit brief is re-issued, or on /drift-audit. NOT the per-table fix recipe (use matrx-frontend's db-canonicalize-table).

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用一条命令比较已保存的审计与当前仓库的新审计，检测结构相似度低于指定阈值的变化，并通过比较报告和退出码告知结果。

• 检查每周审计后的结构变化
• 与至少七天前的审计比较
• 将结构变化检测用于CI判定

Monitor other agent teams for abandoned work, process drift, off-task activity, and excessive token use. It guides evidence checks after alerts and chooses a proportionate response, from notifying the team’s coordinator to escalating to a human.

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• Check whether unfinished work was abandoned
• Detect process drift or off-task activity
• Investigate excessive token use

Build falling leaves in Three.js and similar 3D scenes, with visible front, edge, and back views as they tumble and drift. Coordinate wind, scene depth, camera-aware recycling, and performance checks for a convincing autumn effect.

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• Adding falling autumn leaves to a 3D scene
• Coordinating leaf tumble, sideways drift, and wind
• Managing leaves around buildings and camera movement

Save a baseline of a site's SEO state, then compare later runs to find changes in rankings, indexed pages, titles, and search directives. Use it to spot regressions after redesigns, migrations, or redeployments.

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• Saving an SEO baseline before site changes
• Checking SEO changes after a migration or redeployment
• Comparing current SEO with a previous snapshot

Detects scope creep by quantifying drift percentage. Auto-triggered by L1 orchestrators when files exceed the original plan. Compares git changes against plan, classifies drift into 4 tiers: ON_TRACK, MINOR_DRIFT, SIGNIFICANT_DRIFT, OUT_OF_CONTROL.

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Design drift alerts and escalation — thresholds, runbooks, and retrain triggers. Use when asked to "alert on model drift", "when should we retrain", or "write a drift escalation runbook".

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Monitors AWS CloudFormation stacks for configuration drift using the AWS SDK DetectStackDrift and DescribeStackResourceDrifts APIs. Generates remediation templates and integrates with AWS Config rules for continuous compliance.

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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

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Detect documentation drift, stale references, and cross-document inconsistencies in any project. Scans for code-doc drift (API/function changes not reflected in docs), cross-doc drift (conflicting information across documents), and stale references (broken links, deleted files, outdated versions). Use when checking "doc freshness", "stale docs", "documentation drift", "broken links", "outdated documentation", "doc accuracy", "docs out of date", "doc audit", "doc health", or "verify documentation".

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Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

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tespit etme
unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

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Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

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Detect silent drift between what the docs claim and what the repo actually contains — drift check, health check, audit, watch, sync check, /mxm-watch. Runs 13 drift classes and reports gaps before they ship.

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Adopt a virtual Drift AI-native pet at animalhouse.ai. Wanders between states. Location is never the same twice. Feeding every 6 hours. Common tier creature.

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Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

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在本地验证候选代码，与基准版本比较，分别报告阻断性失败、诊断性差异和证据不足，并明确列出尚未完成的CI检查。

• 维护者预先检查候选合并请求
• 将失败与准确的基准版本比较
• 区分阻断性失败与诊断性差异

Reproduce timing and ordering bugs in a CTF sandbox using controlled concurrent requests. Compare normal and racing runs, trace reads and writes, and preserve a minimal sequence that demonstrates duplicate actions or inconsistent state.

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• Reproduce bugs triggered by simultaneous requests
• Investigate duplicate actions and ineffective duplicate prevention
• Trace stale reads and gaps in locking