Boucle d'expérimentation hebdomadaire

VérifiéPrudence

Effectue un suivi hebdomadaire des modifications de contenu sur MD Home Care, mesure leur impact sur le trafic organique et le classement via Google Search Console et PostHog, et applique un cadre de décision (garder/itérer/revenir) avec des délais spécifiques aux contenus YMYL. Aide les gestionnaires de contenu et les spécialistes SEO à prendre des décisions basées sur les données pour les optimisations des pages de services de soins aux personnes âgées et d'aide aux personnes handicapées.

Spar Skills Guide Bot
DeveloppementIntermédiaire
8002/06/2026
Claude CodeCursorWindsurf
#experiment-loop#content-optimization#traffic-analysis#seo-aeo#data-driven-decisions

Recommandé pour

Notre avis

Boucle hebdomadaire de suivi des expériences pour MD Home Care, analysant les modifications de contenu via Git, mesurant l'impact sur le trafic avec PostHog et GSC, et prenant des décisions de conservation, itération ou annulation avec un décalage temporel adapté au contenu YMYL.

Points forts

  • Évaluation ajustée au décalage temporel pour le contenu YMYL (âgé, handicap)
  • Mesure combinée via Google Search Console et PostHog pour une vue complète
  • Cadre décisionnel clair (Keep/Iterate/Revert) basé sur des seuils chiffrés
  • Processus automatisable via scripts Python et commandes Git

Limites

  • Nécessite un accès fonctionnel à PostHog et GSC avec des clés API
  • Les délais d'évaluation sont spécifiques au contenu YMYL et non généralisables
  • L'analyse manuelle des journaux Git peut être chronophage sans intégration CI/CD
Quand l'utiliser

Utilisez cette boucle pour tout site de contenu YMYL subissant des modifications fréquentes, afin de mesurer l'impact SEO et les visites issues des IA avec une latence appropriée.

Quand l'éviter

Évitez pour les sites non YMYL où les délais d'évaluation sont plus courts, ou si les outils d'analyse (PostHog, GSC) ne sont pas disponibles.

Analyse de sécurité

Prudence
Score qualité85/100

The skill employs bash and Python for data collection from Git, PostHog, and Google Search Console. It does not download or execute untrusted code, but the commands are powerful and could affect the user's system if misapplied. Appropriate for a controlled dev environment.

Points d'attention
  • Uses bash commands and Python scripts that require execution in a specific local environment; while legitimate, could inadvertently run unexpected code if the environment differs.

Exemples

Run weekly experiment loop
Run the weekly experiment loop for MD Home Care. Scan all content changes from the last 7 days in git, measure baseline and post-change metrics via GSC and PostHog, and decide whether to keep, iterate, or revert each change using the lag-adjusted evaluation windows.
Evaluate a new service page
Check the lag-adjusted impact of the new service page /services/home-care that was published 3 weeks ago. Get the before/after metrics for organic clicks, impressions, average position, and AI referral visits. Should we keep, iterate, or revert this page?
Decide on FAQ additions
Two weeks ago we added an FAQ section to the blog post about NDIS support. Run the experiment loop for that change and tell me if we should keep, iterate, or revert based on the decision framework with the appropriate 2-week evaluation window.

name: experiment-loop description: Weekly experiment tracking loop for MD Home Care. Scans content changes, measures traffic impact via PostHog and GSC, and makes keep/iterate/revert decisions with lag-adjusted attribution.

Experiment Loop for MD Home Care

Tracks content changes, measures their impact on traffic and rankings, and decides whether to keep, iterate, or revert. Runs weekly.

CRITICAL: Lag Times for YMYL Content

YMYL content (aged care, disability services) has longer lag times than SaaS content. Do not evaluate changes too early.

| Change Type | SEO Lag | AEO Lag | Evaluation Window | |-------------|---------|---------|-------------------| | Service page optimization | 10-21 days | 3-7 days | 3 weeks minimum | | Location page creation | 14-21 days | 7-14 days | 3 weeks minimum | | Blog post publishing | 7-14 days | 3-7 days | 2 weeks minimum | | Provider comparison addition | 7-14 days | 3-7 days | 2 weeks minimum | | Trust signal enhancement | 10-21 days | 7-14 days | 3 weeks minimum | | FAQ addition | 7-14 days | 3-7 days | 2 weeks minimum |


Step 1: Weekly Git Scan

Identify all content changes from the past week:

cd ~/Projects/mdhomecarebuild

# All content changes in last 7 days
git log --since="7 days ago" --name-only --pretty=format:"%h %s" -- "src/content/**/*.md" "src/content/**/*.mdx"

# Summarize by type
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/blog/*.md" | sort -u | head -20
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/services/*.md" | sort -u | head -20
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/providers/*.md" | sort -u | head -20

Categorize each change:

  • New page: Completely new content file
  • Major edit: Structural changes (new sections, comparison tables, rewritten H1/H2)
  • Minor edit: Small fixes (typos, link updates, frontmatter changes)

Step 2: Baseline Measurement

For each changed page, capture the pre-change baseline. If baseline was not captured before the change, use the previous period as proxy.

GSC Baseline

cd ~/Projects/mdhomecarebuild

# For each changed page, get keyword data
python3 src/scripts/advanced_gsc_analyzer.py --page "/services/[slug]"
python3 src/scripts/advanced_gsc_analyzer.py --page "/blog/[slug]"

Record:

  • Top 10 keywords by clicks
  • Average position for primary keyword
  • Total impressions and clicks (last 7 days)

PostHog Baseline

# Page traffic
python3 src/scripts/posthog_analytics.py --page "/services/[slug]" --days 7

# AI referral traffic
python3 src/scripts/posthog_analytics.py --ai-referrals --days 7

Record:

  • Total pageviews (last 7 days)
  • AI referral visits to that page
  • Traffic sources breakdown

Step 3: Post-Change Measurement

After the evaluation window has passed (see lag times table), measure again.

# GSC: same page analysis
python3 src/scripts/advanced_gsc_analyzer.py --page "/services/[slug]"

# PostHog: same page traffic
python3 src/scripts/posthog_analytics.py --page "/services/[slug]" --days 7
python3 src/scripts/posthog_analytics.py --ai-referrals --days 7

Step 4: Attribution and Decision

Compare Metrics

For each experiment, calculate:

| Metric | Before | After | Change | |--------|--------|-------|--------| | Organic clicks (7d) | X | Y | +/- % | | Impressions (7d) | X | Y | +/- % | | Avg position (primary KW) | X | Y | +/- positions | | AI referral visits (7d) | X | Y | +/- % | | Total pageviews (7d) | X | Y | +/- % |

Decision Framework

KEEP if:

  • Organic clicks increased >10%
  • OR average position improved by 2+ positions
  • OR AI referral visits increased >20%
  • OR impressions increased >15% (leading indicator)
  • AND no negative impact on other pages (cannibalization check)

ITERATE if:

  • Mixed signals (some metrics up, some flat)
  • OR small positive movement (<10% clicks) that suggests potential
  • OR evaluation window has not fully elapsed
  • Action: Make targeted refinements and re-evaluate after another cycle

REVERT if:

  • Organic clicks decreased >15%
  • AND average position dropped by 3+ positions
  • AND no compensating AI referral increase
  • Action: Restore previous version via git, document what went wrong

WAIT if:

  • Change is too recent (within lag window)
  • Action: Re-evaluate next week

Step 5: Log to Playbook

Record every experiment result in PLAYBOOK.md:

## [Date] - [Experiment Name]

**Category:** [Service page optimization / Location page / Blog post / Comparison / Trust signal / FAQ]
**Page:** [URL path]
**Change:** [Brief description of what was changed]
**Hypothesis:** [What we expected to happen]

**Baseline (pre-change):**
- Organic clicks (7d): X
- Avg position (primary KW): X
- AI referrals (7d): X

**Result (post-change, measured [date]):**
- Organic clicks (7d): Y (+/- %)
- Avg position (primary KW): Y (+/- positions)
- AI referrals (7d): Y (+/- %)

**Decision:** KEEP / ITERATE / REVERT / WAIT
**Lesson:** [What we learned]

Experiment Categories

Service Page Optimizations

  • Adding comparison tables
  • Rewriting H1/byline
  • Adding trust signal sections
  • Expanding FAQ sections
  • Adding AI differentiation paragraphs

Location Page Creation

  • New suburb-specific service pages
  • Measure: local keyword rankings, location-specific traffic

Blog Post Publishing

  • New informational content
  • Template/download posts
  • Provider comparison posts
  • Measure: organic clicks, keyword coverage expansion

Provider Comparison Additions

  • New comparison tables on existing pages
  • New "vs" blog posts
  • Measure: comparison keyword rankings, AI referral traffic

Trust Signal Enhancements

  • Adding registration numbers
  • Adding testimonials
  • Adding clinical governance sections
  • Measure: overall page authority signals, position changes

FAQ Additions

  • New FAQ sections
  • Expanding existing FAQs with PAA questions
  • Measure: featured snippet captures, PAA appearances

Weekly Routine

Every week:

  1. Run git scan (Step 1)
  2. For changes past their evaluation window, measure results (Step 3)
  3. Make keep/iterate/revert decisions (Step 4)
  4. Log results to PLAYBOOK.md (Step 5)
  5. Capture baselines for new changes (Step 2)

Usage

/experiment-loop

Runs the full weekly cycle: scan, measure, decide, log.

/experiment-loop --check "/services/sil-services"

Check status of a specific page experiment.

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