SEO & AI Engine Optimization Framework · May 2026

Core Algorithm Updates: detection, response, and recovery

A comprehensive installation and audit reference for monitoring Google's core algorithm updates, classifying their impact on a website, executing the response protocol, tracking recovery, and…

Google's Core Algorithm Updates — Detection, Response, Recovery, and Prevention

A comprehensive installation and audit reference for monitoring Google's core algorithm updates, classifying their impact on a website, executing the response protocol, tracking recovery, and building structural resilience to future updates. This document is dual-purpose: installation manual and audit document.

1. Document Purpose & How to Use This Document

1.1 What This Document Is

This is the canonical reference for handling Google core algorithm updates — the periodic broad-spectrum changes Google makes to its ranking systems that can dramatically reshape search results across millions of sites simultaneously. Core updates are different from spam updates, helpful content updates (now part of core), product reviews updates, and minor tweaks. Core updates are the largest, most consequential, and most disruptive changes Google deploys.

This document specifies how to detect when a core update is happening, how to assess its impact on a specific site, how to respond constructively (rather than panic-react), how to track recovery over the typical 60-90 day post-update period, and how to build a site that's structurally resilient to future updates.

1.2 Three Operating Modes

Mode A — Install Mode: Building monitoring and response infrastructure into ongoing site operations. Follow Sections 2 → 14.

Mode B — Audit Mode: Evaluating an existing site's preparedness for or response to core updates. Skip to Section 11.

Mode C — Hybrid Mode: Audit current state then install missing infrastructure.

1.3 How Claude Code CLI Should Consume This Document

  1. Read Section 2 — collect client variables and historical update impact data
  2. Read Section 3 — understand what core updates are and aren't
  3. Install monitoring infrastructure — Section 5
  4. Define response protocols — Section 6
  5. Build resilience patterns — Section 7
  6. Validate — Section 11
  7. Generate report — Section 14

1.4 Conflict Resolution Rules

Conflict Rule
Existing post-update remediation in progress Don't reset. Document current state and continue tracking.
Existing recovery efforts that contradict this framework Follow the framework here, but document deviation reasons for client/team.
Knee-jerk responses already deployed (mass content edits) Audit changes for unintended consequences. Document.
Disagreement on what an update targeted Document multiple hypotheses; track which holds up over recovery period.

1.5 Required Tools

2. Client Variables Intake

# ============================================
# CORE UPDATES FRAMEWORK CLIENT VARIABLES
# ============================================

# --- Business Identity (REQUIRED) ---
business_name: ""
primary_domain: ""
business_industry: ""
ymyl_classification: ""              # "full", "partial", "lite", "non" (affects update sensitivity)

# --- Site History (REQUIRED) ---
domain_age_years: 0
historical_traffic_baseline: 0       # Pre-most-recent-update average daily organic traffic
historical_keyword_count: 0          # Number of keywords ranking in top 10
last_known_volatility_event: ""      # Date of last identified algorithm impact

# --- Past Core Update Impacts (REQUIRED) ---
past_update_impacts:
  - update_name: ""                  # e.g., "March 2026 Core Update"
    update_date: ""                  # YYYY-MM-DD
    direction: ""                    # "loss", "gain", "neutral", "mixed"
    magnitude_percent: 0             # Percent change in organic traffic
    pages_most_affected: []          # URLs with biggest changes
    hypothesis_about_target: ""      # What this site/team believes the update targeted
    remediation_actions_taken: []
    recovery_status: ""              # "full", "partial", "none", "still_in_progress"
    recovery_completion_date: ""

# --- Monitoring Infrastructure Status (REQUIRED) ---
has_gsc_property_verified: false
has_ga4_configured: false
has_serp_volatility_monitoring: false
has_ranking_tracker: false           # Ahrefs, Semrush, AWR, etc.
has_brand_alert_monitoring: false    # Google Alerts, Mention, Brand24
has_competitor_tracking: false

# --- Response Capability (REQUIRED) ---
has_documented_response_protocol: false
response_team_identified: false
response_team_members: []
emergency_communication_channel: ""  # How team alerts each other to volatility

# --- Site Resilience Posture (RECOMMENDED) ---
eeat_score: 0                        # From framework-eeat.md audit
hcs_score: 0                         # From framework-hcs.md audit
ymyl_score: 0                        # From framework-ymyl.md audit if applicable
sqrg_estimated_rating: ""            # From framework-sqrg.md
content_quality_distribution:        # From HCS audit
  highest_or_high: 0                 # Percentage
  medium: 0                          # Percentage
  low_or_lowest: 0                   # Percentage

# --- Documentation (RECOMMENDED) ---
has_algorithm_update_log: false      # Internal log tracking all updates and impacts
has_post_update_review_process: false
has_quarterly_resilience_audit: false

# --- Recent Update Status (FILLED DURING ACTIVE UPDATE) ---
current_update_active: false
current_update_name: ""
current_update_started: ""           # Detection date
current_update_status: ""            # "rumored", "rolling_out", "completed", "in_recovery"
current_update_traffic_change: 0     # Percentage
current_update_response_phase: ""    # "detect", "assess", "diagnose", "remediate", "recover", "review"

3. What Core Updates Are

Google deploys algorithm changes constantly — hundreds of small updates per year. Most are imperceptible to individual sites. Three categories of updates have outsized impact and are publicly named by Google:

3.1 Core Updates

Core updates are broad changes to how Google's core ranking algorithms evaluate content. They typically:

Google deploys 3-6 named core updates per year. Recent core updates and their characteristic focus:

3.2 What Core Updates Are Not

Core updates are not:

3.3 What Core Updates Reward and Punish

Google's official guidance: "There's nothing wrong with pages that drop after a core update. They haven't violated our spam policies nor are subject to a manual or algorithmic action, as can happen to pages that do violate those policies. In fact, there are no specific actions to take to recover from a core update. Negative rankings are not a signal that you have done anything wrong."

In practice, core updates appear to:

Reward:

Punish:

3.4 The Recovery Truth

Recovery from a core update is possible but not guaranteed. Google's guidance: "Improvements in your content might result in better rankings — though changes might not be reflected until the next core update."

Core updates are evaluative re-baselines. Once Google's evaluation of a site changes negatively, recovery typically requires:

  1. The site genuinely improving (content quality, E-E-A-T, etc.)
  2. Time for Google to recognize the improvement
  3. The next core update incorporating the new evaluation

Sites typically don't "recover" between core updates. They might see modest fluctuation but the major re-baseline happens at the next core update. This means recovery cycles are typically 90+ days minimum, often 6-12 months.

Some sites never recover. If the core update detected fundamental issues with the site's purpose, content strategy, or trust posture, those issues need fundamental remediation. Cosmetic changes don't trigger recovery.

4. Update Detection Protocol

4.1 Signals That an Update Is Happening

Detect updates through multiple signal sources:

4.1.1 Official Google announcements

Google typically announces core updates via:

Set up alerts:

# Monitor SearchLiaison via RSS or alternative
# Monitor Google Search Blog RSS:
# https://developers.google.com/search/blog/feed.xml

4.1.2 SERP volatility tools

Daily volatility tracking:

When volatility spikes 7+ across multiple trackers, an update is likely active.

4.1.3 Site-specific signals

For each monitored site, set up:

4.1.4 Industry signal aggregation

Monitor:

4.2 Detection Workflow

When potential update is detected:

  1. Confirm via multiple sources — Don't react to single-source rumors
  2. Document detection time — Note the moment volatility appeared
  3. Note Google's communication status — Has Google confirmed officially?
  4. Open algorithm update incident log entry — Begin tracking impact

Algorithm update incident log template at /admin/algorithm-incidents/{{date}}-{{update-name}}.md:

# Update Incident: {{UPDATE_NAME}}

## Detection
- Detected: {{DATETIME}}
- Detection sources: {{LIST}}
- Google confirmation: {{YES/NO/PENDING}}
- Confirmation date: {{DATE_IF_CONFIRMED}}

## Volatility Indicators
- Semrush Sensor: {{SCORE}}/10
- Mozcast: {{SCORE}}
- Other trackers: {{SCORES}}
- Industry sources reporting: {{LIST}}

## Initial Impact Assessment (24 hours post-detection)
- Traffic delta vs 7-day average: {{PERCENTAGE}}
- Top affected pages: {{LIST}}
- Top affected keywords: {{LIST}}

## Working Hypothesis
{{WHAT_WE_THINK_THIS_UPDATE_TARGETS}}

## Status
{{DETECTING / ASSESSING / DIAGNOSING / REMEDIATING / RECOVERING / REVIEWED}}

4.3 The 72-Hour Hold

Critical rule: do not take significant remediation action within 72 hours of detection.

Reasons:

  1. The update is probably still rolling out — early data is incomplete
  2. SERP volatility within an update is normal and self-corrects
  3. Knee-jerk responses cause damage that's hard to reverse
  4. The update may not target what you think it targets
  5. Three days of data lets you distinguish signal from noise

During the 72-hour hold:

After 72 hours, you have enough data to begin assessment.

5. Update Impact Assessment

5.1 Quantitative Impact Analysis

After 72 hours, gather data:

5.1.1 Site-wide traffic delta

Compare: 7-day average post-detection vs 7-day average pre-detection
Compare: Same period year-over-year (control for seasonality)
Compare: Specific weekday-to-weekday (Monday vs Monday)

Calculate:
- Total organic traffic change (%)
- Total organic clicks change (GSC)
- Total impressions change (GSC) — distinguishes ranking from CTR issues
- Average position change (GSC)

5.1.2 Per-page impact

In GSC Performance report:

  1. Filter date range: 7 days post-update vs 7 days pre-update
  2. Sort by traffic change descending and ascending
  3. Identify pages with >25% traffic change in either direction
  4. Document URL, change percentage, page type, primary topic

Build a per-page impact spreadsheet:

url,page_type,primary_topic,traffic_pre,traffic_post,traffic_delta_pct,impressions_pre,impressions_post,impressions_delta_pct,position_pre,position_post,position_delta,ymyl_status,word_count,content_age_days,last_refresh_days_ago,has_credentialed_author,notes
/articles/medication-guide,article,health/medication,1247,621,-50.2,4500,3200,-28.9,3.2,4.8,-1.6,full_ymyl,2400,367,367,no,major_loss_no_credentialed_review
/articles/site-speed-guide,article,seo/technical,892,1108,+24.2,3200,3850,+20.3,4.1,3.6,+0.5,non,1850,121,32,yes,small_gain

5.1.3 Per-query impact

In GSC Performance:

  1. Pivot by query
  2. Identify queries with significant change
  3. Determine if changes cluster around specific topics or page types

5.1.4 Per-content-type impact

Group affected pages by type:

Compare change distribution across types. If one type is dramatically worse hit, the update likely targeted something specific to that type.

5.2 Pattern Recognition

Look for patterns among losing pages:

Content patterns:

Quality patterns:

Technical patterns:

Reputation patterns:

Document patterns in the incident log.

5.3 Hypothesis Formation

After pattern analysis, form 2-3 hypotheses about what the update targeted:

Example hypotheses:

Test each hypothesis against the data:

The hypothesis that explains the most data is most likely correct. If multiple hypotheses fit, multiple changes may have happened.

5.4 External Hypothesis Validation

Compare to industry analysis:

If external analysis matches your internal pattern, confidence is high. If it doesn't match, either your situation is unusual (worth investigating why) or your hypothesis is wrong.

6. Response Protocol

6.1 Response Phase Decision Matrix

Based on impact assessment, choose response track:

Minimal impact (<10% traffic change):

Moderate loss (10-30% traffic change):

Significant loss (30-50%):

Catastrophic loss (>50%):

6.2 What Not to Do (Immediate)

Within first 30 days post-update, avoid:

6.3 Constructive Response Actions

6.3.1 If E-E-A-T issues identified

Apply framework-eeat.md remediation per pillar:

6.3.2 If HCS issues identified

Apply framework-hcs.md remediation:

6.3.3 If YMYL issues identified

Apply framework-ymyl.md remediation:

6.3.4 If entity issues identified

Apply framework-entitysalience.md and framework-knowledgegraph.md:

6.3.5 If technical issues identified

Apply Tier 1 of the 14-tier framework:

6.4 Remediation Documentation

Track every remediation action in the incident log:

## Remediation Actions Taken

### Week 1 ({{DATE_RANGE}})
- {{DATE}}: {{ACTION}} — {{PAGES_AFFECTED}} — {{REASONING}}
- {{DATE}}: {{ACTION}} — {{PAGES_AFFECTED}} — {{REASONING}}

### Week 2 ({{DATE_RANGE}})
- {{DATE}}: {{ACTION}} — {{PAGES_AFFECTED}} — {{REASONING}}

### Week 3+ ({{DATE_RANGE}})
{{...}}

This documentation is essential for:

6.5 Communication With Stakeholders

During an update, communicate clearly with clients/management:

Initial communication (within 48 hours of detection):

Weekly during active update:

Post-stabilization (4-6 weeks post-update):

7. Recovery Tracking

7.1 Recovery Indicators

Monitor weekly:

Recovery signal indicators:

Continued suppression indicators:

7.2 Recovery Timeline Expectations

Based on impact severity:

Impact Level Initial Recovery Signals Substantial Recovery Full Recovery
Minor loss 2-4 weeks 1-2 months Often by next core update
Moderate loss 4-8 weeks 3-6 months 6-12 months, often at next core update
Significant loss 2-3 months 6-12 months 12-24 months, may require multiple core updates
Catastrophic loss 3-6 months for any signal 12+ months May not fully recover; strategic reset may be needed

Recovery is not linear. Expect ups and downs week-to-week. The trend over 30-60 day windows is what matters.

7.3 Cross-Update Recovery Patterns

Review historical update impact data:

Build institutional memory of what works for this specific site.

7.4 When Recovery Doesn't Happen

If 6-12 months post-update show no recovery signals:

  1. Reassess hypothesis — was the diagnosis correct?
  2. Audit remediation execution — did we actually do what we documented?
  3. Compare to recovered competitors — what did they do differently?
  4. Consider strategic reset — is the site model fundamentally compromised?

Strategic resets may include:

These are major decisions made over months of consideration, not immediate post-update reactions.

8. Building Resilience to Future Updates

The best response to a core update is having built a site that wasn't going to be hit by it in the first place. Resilient sites share characteristics.

8.1 Foundational Resilience

E-E-A-T strong by default — Sites that score 110+/130 on E-E-A-T audit before an update are resilient by default. Apply framework-eeat.md continuously, not reactively.

HCS-aligned content strategy — Sites that publish for users, not for search, weather updates better. Apply framework-hcs.md.

YMYL standards maintained — YMYL content meeting elevated standards rarely loses in updates. Apply framework-ymyl.md.

Entity authority — Sites with strong entity recognition through Wikidata, Wikipedia, schema graph, and topical authority weather updates better. Apply framework-knowledgegraph.md and framework-entitysalience.md.

8.2 Content Portfolio Resilience

Topical focus — Sites covering 3-7 topics deeply outperform sites covering 30 topics shallowly. Don't dilute.

Quality over quantity — Better to have 100 high-quality articles than 1000 mediocre ones. The mediocre ones drag the whole site down.

Genuine first-hand expertise — Content built on actual experience holds value through algorithm changes.

Original research and insight — Information Gain (see framework-infogain.md) is durable across updates.

Refresh discipline — Time-sensitive content kept current; outdated content retired.

8.3 Trust Infrastructure Resilience

Reputation infrastructure — Strong reviews, earned media, industry recognition compound over time and protect against reputation-targeting updates.

Author identity stability — Real, credentialed, persistent authors. Not turnover-heavy author rosters.

Technical foundations solid — Fast, secure, accessible, well-structured sites are easier for Google to evaluate accurately.

8.4 Operational Resilience

Algorithm update log maintained — Every update tracked. Learning compounds.

Quarterly resilience audits — Catch drift before it costs.

Foundational framework compliance — These frameworks are not one-time installs. Maintain compliance continuously.

Don't over-optimize — Aggressive optimization creates fragility. Sites that try to game algorithms get hit by algorithm changes.

8.5 Resilience Audit

Quarterly, score the site against:

# Criterion Score
R1 E-E-A-T audit current and 100+/130 __/2
R2 HCS audit current and 45+/54 __/2
R3 YMYL audit (if applicable) current and 40+/48 __/2
R4 SQRG self-rating "High" or "Highest" on top pages __/2
R5 Knowledge Graph entity established __/2
R6 Entity Salience strong on primary topics __/2
R7 Information Gain demonstrable per article __/2
R8 AI Citation indicators positive (see framework-aicitations.md) __/2
R9 Topical focus maintained (3-7 pillars) __/2
R10 Content portfolio: 80%+ pages would rate Medium or higher __/2
R11 Reputation research returns positive results __/2
R12 Trust infrastructure complete (policies, security, schema) __/2
R13 Refresh discipline in place __/2
R14 Algorithm update log maintained __/2
R15 Quarterly audits documented __/2

Score: 30 max. Resilient site: 25+/30. Sites scoring below 20 are at substantial risk in core updates.

9. Learning From Updates

9.1 Post-Update Review Cadence

After each core update completes (typically 4-6 weeks post-rollout):

  1. Conduct full impact analysis
  2. Validate or revise hypothesis
  3. Document what remediation worked or didn't
  4. Update institutional knowledge
  5. Update this framework if patterns suggest framework gaps

9.2 Cross-Update Pattern Analysis

Annually, review all algorithm update incidents:

9.3 Industry Learning Integration

Continuously consume:

But filter heavily — much SEO commentary is speculation. Trust patterns from your own data more than generic guidance.

10. Spam Updates and Manual Actions (Adjacent Issues)

While this document is about core updates specifically, related events require similar protocols.

10.1 Spam Updates

Spam updates target manipulative practices. If a site is hit by a spam update:

10.2 Manual Actions

Manual actions are penalties applied by human reviewers and communicated via Search Console.

10.3 Algorithmic Action Without Public Update

Sometimes sites lose ranking without a publicly-named update. Possible causes:

Investigation:

  1. Check Search Console for security warnings, manual actions
  2. Audit technical health (crawl errors, indexability, redirects)
  3. Audit reputation (new negative content, hacked pages)
  4. Audit content (was something published that violates policies?)
  5. Compare to volatility trackers — was there an unannounced update?

Many "algorithmic actions" without public updates turn out to be technical issues or reputation issues remediable by direct action.

11. Audit Mode

11.1 Algorithm Update Preparedness Audit

Score the site:

# Criterion Severity Pass/Fail
AU1 Resilience score 25+/30 (Section 8.5) Critical
AU2 Algorithm update log maintained Critical
AU3 Response protocol documented and team identified Critical
AU4 Monitoring infrastructure in place (GSC, GA4, volatility, ranking) Critical
AU5 Quarterly resilience audits performed High
AU6 Past update impacts documented and analyzed High
AU7 Foundational frameworks (E-E-A-T, HCS, YMYL, SQRG) installed Critical
AU8 Content quality distribution favorable (80%+ Medium-or-higher PQ) Critical

11.2 Active Update Response Audit

If currently in an active update response, score:

# Criterion Pass/Fail
AR1 Detection occurred within 24 hours of update start
AR2 72-hour hold respected — no panic actions in first 3 days
AR3 Quantitative impact assessment completed within 7 days
AR4 Pattern analysis completed
AR5 Hypothesis formed and validated against external sources
AR6 Constructive remediation underway based on hypothesis
AR7 Documentation maintained throughout
AR8 Stakeholder communication occurring on schedule
AR9 Recovery monitoring weekly

11.3 Post-Update Review Audit

After update completes (4-6 weeks post-rollout), score:

# Criterion Pass/Fail
AP1 Final impact assessment documented
AP2 Remediation actions and outcomes documented
AP3 Hypothesis validation or revision documented
AP4 Lessons integrated into framework
AP5 Resilience audit re-run
AP6 Remediation gaps identified and scheduled

12. Common Mistakes & Anti-Patterns

12.1 Panic Reaction

Anti-pattern: Within 48 hours of detecting an update, mass-deleting content or rewriting articles.

Why it fails: Premature action based on incomplete data causes more damage than the update did.

Fix: Respect the 72-hour hold. Gather data before acting.

12.2 Misdiagnosis

Anti-pattern: Assuming the update targeted thing X without checking the data, then remediating for thing X.

Why it fails: If the update actually targeted thing Y, your remediation doesn't help — and it may hurt by introducing new issues.

Fix: Data-driven hypothesis formation. Validate hypothesis against patterns and external sources before acting.

12.3 Treating Updates as Penalties

Anti-pattern: Believing the site was "punished" and looking for what to "fix" to make Google "stop punishing."

Why it fails: Updates re-evaluate quality holistically. Looking for a specific "violation" misses the structural nature of the change.

Fix: Treat updates as evaluative re-baselines. Improve the site genuinely, not as remediation theater.

12.4 Expecting Quick Recovery

Anti-pattern: Running emergency remediation hoping for recovery within weeks.

Why it fails: Recovery typically takes 90+ days minimum, often 6-12 months. Expecting faster sets up false hope and premature additional changes.

Fix: Realistic recovery expectations. Plan for the long game.

12.5 Single Update Focus

Anti-pattern: Treating each update as isolated event, not learning across them.

Why it fails: Misses pattern recognition. Sites that don't learn keep getting hit by similar update types.

Fix: Cross-update pattern analysis. Build institutional learning.

12.6 Tracker Chasing

Anti-pattern: Reacting to daily volatility tracker fluctuations as if every spike is a major update.

Why it fails: Most volatility is noise. Reacting to noise causes thrashing.

Fix: Trust multi-source confirmation before treating something as a major update.

12.7 Ignoring Foundational Frameworks

Anti-pattern: Trying to recover from an update without ever auditing E-E-A-T, HCS, YMYL, or SQRG compliance.

Why it fails: Updates reward sites that comply with foundational frameworks. Skipping them limits recovery.

Fix: Use update events as forcing function for foundational framework audits.

12.8 Stakeholder Pressure-Driven Decisions

Anti-pattern: Making changes because a client or executive demands action, not because data supports it.

Why it fails: Pressure-driven changes often hurt more than help.

Fix: Educate stakeholders on the 72-hour hold, the diagnostic process, and realistic recovery timelines. Hold the line on data-driven response.

12.9 Ignoring Spam Update vs Core Update Distinction

Anti-pattern: Treating a spam update like a core update or vice versa.

Why it fails: Different remediation protocols. Spam violations need direct remediation; core update issues need quality improvement.

Fix: Identify which type of update is occurring before responding.

12.10 Over-Documenting While Under-Acting

Anti-pattern: Spending all energy documenting impact and analyzing patterns, never actually executing remediation.

Why it fails: Analysis paralysis prevents recovery.

Fix: Documentation supports action, doesn't replace it. After 7-14 days of analysis, execute remediation in parallel with continued monitoring.

13. Maintenance Schedule

13.1 Daily

13.2 Weekly

13.3 Monthly

13.4 Quarterly

13.5 Annually

13.6 During Active Update

14. Implementation/Audit Report Templates

14.1 Post-Update Analysis Report Template

# Post-Update Analysis: {{UPDATE_NAME}}

## Update Identification
- Update name: {{NAME}}
- Detected: {{DATETIME}}
- Confirmed by Google: {{DATETIME}}
- Rollout completed: {{DATETIME}}
- Industry consensus on focus: {{SUMMARY}}

## Impact on This Site
- Total traffic change: {{PERCENTAGE}}
- Direction: {{LOSS/GAIN/MIXED}}
- Pages with >25% loss: {{COUNT}}
- Pages with >25% gain: {{COUNT}}
- Average position change: {{NUMBER}}
- Magnitude classification: {{MINIMAL/MODERATE/SIGNIFICANT/CATASTROPHIC}}

## Pages Most Affected
### Largest Losses
{{TABLE_OF_TOP_10_LOSSES}}

### Largest Gains
{{TABLE_OF_TOP_10_GAINS}}

## Pattern Analysis
{{IDENTIFIED_PATTERNS_ACROSS_AFFECTED_PAGES}}

## Hypothesis
**Primary hypothesis**: {{H1_DESCRIPTION}}
**Evidence supporting**: {{EVIDENCE}}
**Counter-evidence**: {{COUNTER_EVIDENCE}}

**Alternative hypothesis**: {{H2_DESCRIPTION}}
**Evidence supporting**: {{EVIDENCE}}

**Hypothesis confidence**: {{LOW/MEDIUM/HIGH}}

## Remediation Plan
### Phase 1 (Immediate)
{{ACTIONS}}

### Phase 2 (30 days)
{{ACTIONS}}

### Phase 3 (90 days)
{{ACTIONS}}

## Recovery Tracking
- 30-day check-in: {{SCHEDULED_DATE}}
- 60-day check-in: {{SCHEDULED_DATE}}
- 90-day check-in: {{SCHEDULED_DATE}}

## Lessons for Future Updates
{{INSIGHTS_TO_INTEGRATE}}

## Sign-Off
{{ANALYST_NAME}}, {{DATE}}

14.2 Algorithm Update Audit Report Template

# Algorithm Update Preparedness Audit

**Site**: {{BUSINESS_NAME}}
**Audit Date**: {{TODAY}}

## Resilience Score
{{X}}/30 — {{RESILIENT/AT_RISK/VULNERABLE}}

## Foundational Framework Status
- E-E-A-T: {{SCORE}}/130
- HCS: {{SCORE}}/54
- YMYL: {{SCORE}}/48 (if applicable)
- SQRG: {{ESTIMATED_RATING}}

## Monitoring Infrastructure Status
{{LIST_WITH_STATUS}}

## Response Capability Status
{{LIST_WITH_STATUS}}

## Past Update Impact History
{{TABLE_OF_PAST_UPDATES}}

## Risk Assessment
{{ASSESSMENT_OF_LIKELY_VULNERABILITIES}}

## Recommended Resilience Improvements
{{PRIORITIZED_LIST}}

## Sign-Off

End of Framework Document

Document version: 1.0 Last updated: 2026-04-29 Maintained by: ThatDeveloperGuy

Core updates are the most disruptive periodic events in SEO. The frameworks in this document — combined with foundational compliance with E-E-A-T, HCS, YMYL, SQRG, and entity-based authority — create resilience. Sites that build resilience proactively spend less time in reactive recovery. Sites that wait for updates to expose weaknesses spend significant time in recovery cycles.

The most important truth about core updates: the best response is not having needed one in the first place. Build sites that earn their rankings through genuine quality. Audit continuously. Improve continuously. Don't wait for updates to reveal what was always going to be revealed.

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