Drift: Baseline / Compare / History

Position in workflow: v0.8 Monitoring Setup → v0.8 Drift: Baseline / Compare / History → v0.8 Runbook Creation

Execution Mode

Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.

Mode	What this skill produces
quick	One metric / config / dataset baselined; weekly compare schedule; simple threshold alert
standard	3–5 things monitored; baseline + tiered thresholds (warn / critical); compare cadence; history retention
deep	Full portfolio; multi-dimensional comparison (per segment, per environment); regression-cause matrix; auto-baselining after intentional change

What This Does

Generalizes a pattern AgriciDaniel's claude-seo skill encodes for SEO drift — baseline → snapshot → compare → history — into a reusable monitoring shape that works for any metric, config, or dataset that can change over time and needs to be watched.

This is drift monitoring, distinct from alerting on absolute thresholds. Alerting answers "is X over the line right now?" Drift monitoring answers "is X different from last week's normal?" — which catches slow regressions that absolute thresholds miss.

Examples of things worth drift-monitoring:

• KPI: activation rate week-over-week
• AI search position: ChatGPT/Perplexity ranking for target queries
• Config: feature-flag rollout percentages
• Performance: p95 latency by endpoint
• Cost: per-user infra cost
• Marketing: per-channel CAC trend
• Content: changelog post engagement
• Third-party: vendor pricing pages (price hikes), competitor feature pages (parity loss)

How It Works

1. Pick what to monitor — One thing per MON-DRIFT- entry. Must be:
   • Quantifiable (number, percentage, list, configuration value)
   • Snapshotable (captureable at a point in time, ideally automatically)
   • Causally interpretable (when it changes, you know enough to investigate)
2. Capture the baseline — Take a snapshot. Date it. Store in version control or a known location (status/baselines/, monitoring/snapshots/, etc.).
3. Define drift thresholds:
   • Warn: meaningful change (e.g., 10% drift in a KPI; any change in a config value)
   • Critical: serious change (e.g., 25% KPI drop; breaking config change)
   • Recalibrate: intentional change that should refresh the baseline (e.g., after a feature rollout, the baseline is wrong; refresh it)
4. Set compare cadence — How often does this get re-snapshotted?
   • Hot (hourly/daily): production KPIs, AI search positions during a launch
   • Warm (weekly): standard product KPIs, content engagement
   • Cool (monthly): vendor pricing, competitor feature parity, infra cost
5. Build the compare procedure — A script or runbook that:
   • Takes a new snapshot
   • Diffs against baseline
   • Computes drift % per dimension
   • Emits warn/critical signals at thresholds
   • Appends to history log
6. Plan auto-baselining after intentional change [standard+] — When the team makes a deliberate change (ships a feature that should improve activation), the old baseline becomes wrong. Define what triggers a baseline refresh and who approves it.

Example

Monitoring AI search position for the target query "best CRO tool for SaaS founders" across 3 surfaces. (Generalized from AEO Audit re-test cadence.)

Baseline (2026-04-01):

ChatGPT: not mentioned (rank: --)
Perplexity: rank 4 of 5, miscategorized as "analytics"
AI Overviews: not mentioned
Sources cited: [competitor1.com, competitor2.com, reddit.com/r/SaaS]

Thresholds:

• Warn: rank changes by ≥1, sources list changes, miscategorization persists
• Critical: dropped from any surface where previously mentioned

Compare cadence: weekly during launch month, monthly after.

Re-snapshot (2026-04-15):

ChatGPT: rank 3 of 5 (NEW)         ← improved
Perplexity: rank 3 of 5, category corrected (NEW)   ← improved
AI Overviews: rank 5 of 7 (NEW)    ← improved
Sources cited: [+ ourdomain.com (NEW), ...]

Compare output: 3 surfaces improved. Likely cause: alternatives-pages campaign + new CRO-anchored blog post indexed.

History append: 2026-04-01 → 2026-04-15 row added to history log.

Action: No alert; positive drift. Baseline can stay (next compare in 2 weeks). If the trend reverses, the baseline catches it.

What You Get Back

• MON-DRIFT-* entries (one per monitored thing) — Baseline snapshot, thresholds, cadence, compare procedure, history pointer
• History logs in status/drift-history/<id>.jsonl (or similar) — Append-only record of compare results
• Compare scripts (scripts/drift/<id>.sh or equivalent) when automatable

When to Use It

Trigger	Mode
Post-launch — need ongoing watch on KPIs	standard
AEO Audit results to maintain	standard
Competitor feature parity tracking	standard
Vendor / third-party pricing watch	quick
Performance regression hunting	deep
Infrastructure cost trend	quick

Do not use for things that are already covered by threshold-based alerting (RED/USE metrics, MON-* alerts). Drift is for trends; alerting is for absolutes.

Consumes

• MON-* monitoring infrastructure (from v0.8 Monitoring Setup) — Data sources for KPI/performance drift; existing dashboards
• KPI-* metric definitions (from v0.3 + v0.9) — What's worth watching
• CFD-* competitor research (from v0.2 + ongoing) — For competitor-parity drift
• GTM-AEO-* Coverage Matrix (from v0.9 AEO Audit) — For AI search position drift
• BR-PRICING-* (from v0.3 + v0.9 Offer) — For pricing drift on our side; vendor-pricing drift watches their side
• DEP-* release entries — Intentional changes that may trigger baseline refresh

Produces

• MON-DRIFT-* entries with Type=Drift-Baseline — One per monitored thing
• History logs in status/drift-history/ — Append-only diff history
• Compare scripts in scripts/drift/ (when automatable)
• Baseline-refresh approvals — Recorded when an intentional change resets a baseline

Output Template

MON-DRIFT-XXX: Baseline — [What is being monitored]
Type: Drift-Baseline
Category: [KPI | AI-Search | Config | Performance | Cost | Vendor | Competitor]
Owner: [Person / role]
Status: Active

Subject: [Specific thing — e.g., "ChatGPT rank for query 'best CRO tool for SaaS'"]

Baseline (YYYY-MM-DD):
  [Snapshot — values, list, config dump, etc.]

Thresholds:
  Warn: [Condition — e.g., "rank change ≥1 OR sources list changes"]
  Critical: [Condition — e.g., "dropped from any surface previously mentioned"]
  Recalibrate-trigger: [What intentional change resets the baseline]

Compare cadence: [Hourly | Daily | Weekly | Monthly | Triggered]
Compare procedure: [Script path OR manual runbook reference]

History: status/drift-history/<id>.jsonl

Re-snapshot history:
  - YYYY-MM-DD: [summary of last compare result]
  - YYYY-MM-DD: ...

Linked IDs: MON-XXX (data source), KPI-YYY or CFD-ZZZ or GTM-AEO-AAA (subject)

Anti-Patterns

Pattern	Signal	Fix
Stale baseline	Comparing today's value against a 12-month-old baseline that no longer reflects "normal"	Add recalibrate-trigger conditions; refresh after intentional changes
Drift alerting on noise	Critical alert fires every week for 2% movement	Threshold is too tight; widen warn, tighten critical
No history log	Snapshot, compare, alert, forget	Append-only history is the whole point; without it, you can't see trends
Monitoring everything	50 MON-DRIFT-* entries, nobody reads any	Pick the 3–5 things that matter; archive the rest
No causal interpretation	"Drift detected" with no investigation path	Each drift signal should map to candidate causes (recent release, competitor move, AI surface change)
Drift instead of alerting	Using drift for things that need absolute thresholds	Drift = trends; absolutes (latency > 500ms) = alerting

Quality Gates

Before activating drift monitoring:

• Subject is quantifiable and snapshotable
• Baseline captured with explicit date
• Warn and critical thresholds defined
• Compare cadence committed
• Compare procedure (script or runbook) exists
• History log path defined and writable
• Recalibrate-trigger documented (when does baseline get refreshed?)
• Owner assigned

Downstream Connections

Consumer	What it uses	Example
Runbook Creation (RUN-)	Drift criticals trigger investigation runbooks	MON-DRIFT-AEO critical → RUN-investigate-aeo-loss
Launch Metrics	Drift on launch KPIs informs go/no-go decisions	KPI-activation drift ≥10% → re-look at GTM
Feedback Loop Setup	Drift in customer-facing metrics drives CFD- investigation	Activation drift down → CFD- interview cohort
v0.9 AEO Audit (re-run)	Drift in AI search positions triggers re-audit	AEO drift → re-run aeo-audit
v1.0 Continuous Discovery	Drift patterns inform discovery questions	"Why is X drifting?" → Teresa Torres discovery

Detailed References

• AgriciDaniel's seo-drift skill — original baseline/compare/history pattern for SEO
• Site Reliability Engineering (Google) — SLO-burn-rate alerting (related but different)
• (No bundled references/ — pattern is reusable; the content lives in each MON-DRIFT- entry)