content-refresh

Content Refresh Workflow

Re-optimize existing content with updated research, current statistics, new sources, refreshed SEO keywords, and Phase 6.5 humanization — while preserving what's working and maintaining search rankings.

When to Use

Use /contentforge:content-refresh when:

• Content is 6+ months old and needs updated stats/examples
• Search rankings are declining (lost top 10 position)
• Competitor content has surpassed yours
• Product/service features have changed
• Industry landscape has shifted
• Content scored well originally (≥7.0) but needs freshening

What This Command Does

1. Load Existing Content — Read the current .docx from wherever the brand's tracking.backend stores it (local filesystem, Airtable attachment, or Google Drive)
2. Analyze What to Keep — Identify evergreen sections, high-performing segments
3. Research Updates — Find current statistics, new sources, recent examples
4. Selective Rewrite — Update outdated sections, preserve working content
5. Re-run Quality Gates — Fact-check new claims, re-humanize, re-score
6. SEO Preservation — Maintain target keywords, internal links, meta structure
7. Version Control — Save as v1.1, v1.2 (never overwrite v1.0)

Required Inputs

Existing Content:

• Google Drive URL or File ID
• OR: Local .docx file path

Refresh Scope (select one):

• Light Refresh (20%): Update statistics, examples, citations only
• Medium Refresh (50%): Rewrite intro/conclusion, update 3-5 sections, add new research
• Heavy Refresh (80%): Complete rewrite using original as outline, keep only evergreen insights

Optional:

• New target keywords (if pivoting focus)
• Sections to preserve (mark as "DO NOT EDIT")
• Deadline (for priority ranking in batch)

How to Use

Basic Usage

/contentforge:content-refresh https://docs.google.com/document/d/XYZ123

Prompt: "What refresh scope? (light / medium / heavy)"

With Scope Specified

/contentforge:content-refresh https://docs.google.com/document/d/XYZ123 --scope=medium

Batch Refresh (multiple pieces)

Run the refresh sheet through /contentforge:batch-process — point it at a sheet whose rows reference existing documents:

/contentforge:batch-process https://docs.google.com/spreadsheets/d/ABC123

Sheet columns: doc_url, refresh_scope, priority

What Happens

Step 1: Content Analysis (2-3 minutes)

• Load existing content from Google Drive
• Extract metadata (original publish date, current word count, quality score)
• Identify sections: intro, body paragraphs, conclusion, citations
• Evergreen Detection: Flag sections that are timeless (definitions, principles, frameworks)
• Outdated Detection: Flag statistics >12 months old, broken links, deprecated examples
• Calculate "freshness score" (0-100, based on %outdated)

Output:

Content Analysis Report
─────────────────────────────────────────────────────
Title: "AI in Healthcare: 2025 Trends and Predictions"
Original Publish: 2025-03-15
Current Word Count: 2,340 words
Original Quality Score: 8.9/10

Freshness Score: 42/100 (Needs Refresh)

Evergreen Sections (Keep):
✓ Para 2: Definition of AI in healthcare
✓ Para 5: Historical context (2010-2020)
✓ Para 8: Ethical considerations framework

Outdated Sections (Update):
⚠ Para 1: Intro references "2025 predictions" (now outdated)
⚠ Para 3: Statistics from 2024 market report
⚠ Para 6: Example of startup acquired in 2025
⚠ Para 10: Conclusion mentions "upcoming 2025 regulations"
⚠ Citations: 6/15 links are broken (404 errors)

Recommendation: Medium Refresh (50% rewrite)
─────────────────────────────────────────────────────

Step 2: Research Phase (Targeted)

• Run Phase 1 (Research Agent) focused ONLY on outdated sections
• Search for: Current statistics (2026), new case studies, recent regulatory changes
• Find replacement sources for broken citations
• Preserve existing sources for evergreen content

Step 3: Selective Rewrite

• Keep evergreen sections unchanged (no rewrite)
• Update outdated sections with new research
• Rewrite intro/conclusion to reflect current year, updated predictions
• Maintain article structure (same H2/H3 hierarchy)
• Preserve internal links and brand-specific terminology

Step 4: Re-run Quality Pipelines

• Phase 2 (Fact-Checker): Verify ONLY new claims and updated statistics
• Phase 4 (Validator): Check for hallucinations in rewritten sections
• Phase 5 (Structurer): Ensure refreshed content flows naturally with preserved sections
• Phase 6 (SEO): Preserve the original keyword placements (title, H1, first 100 words, 2-3 H2s, conclusion) and meta structure. Density is monitored for drift and reported — it is advisory, never a target to pad toward.
• Phase 6.5 (Humanizer): Re-humanize rewritten sections
• Phase 7 (Reviewer): Re-score (target: ±0.5 points from original score)

Step 5: Version Control

• Original: Article-AI-Healthcare_v1.0.docx (never modified)
• Refresh: Article-AI-Healthcare_v1.1.docx (new version)
• Track changes in metadata: "Refreshed 2026-02-17, updated 6 sections, added 4 new sources"

Refresh Scopes

Light Refresh (~20% rewrite, 8-12 min)

What Changes:

• Update statistics to current year
• Replace 1-2 outdated examples
• Fix broken citation links
• Refresh intro sentence ("As of 2026..." instead of "In 2025...")
• Re-run Phase 6.5 Humanizer only

What Stays:

• All structure (H2/H3 headings)
• 80% of original paragraphs
• All evergreen sections
• Target keywords unchanged

Use Case: Content is 6-12 months old, mostly accurate, just needs stats updated

Medium Refresh (~50% rewrite, 15-20 min)

What Changes:

• Rewrite intro and conclusion completely
• Update 40-60% of body paragraphs
• Add 3-5 new sections for emerging trends
• Replace 50% of citations with current sources
• Re-run Phases 2, 4, 5, 6, 6.5, 7

What Stays:

• Article structure (same H2 sections, order may change)
• Evergreen definitions, frameworks, principles
• Target keywords (may add 2-3 new secondary keywords)

Use Case: Content is 12-24 months old, core thesis is valid but needs significant updates

Heavy Refresh (~80% rewrite, 22-30 min)

What Changes:

• Complete rewrite using original as outline only
• New research from scratch (Phase 1 full run)
• Update target keywords based on current search intent
• Add 5-10 new sections
• Replace 80% of citations
• Full 10-phase pipeline (same as new content)

What Stays:

• Core topic and brand voice
• 1-2 evergreen sections (definitions, historical context)
• SEO URL slug (to preserve backlinks)

Use Case: Content is 24+ months old, industry has changed significantly, needs near-complete overhaul

SEO Preservation Strategies

Keyword Density Maintenance

Original keyword density: 2.3% for "AI in healthcare"
Target for refresh: 2.0-2.6% (±0.3%)
Action: Phase 6 monitors and adjusts rewritten sections

URL Slug Preservation

Original: /blog/ai-in-healthcare-2025-trends
Refreshed: /blog/ai-in-healthcare-2025-trends (SAME URL)
Title updates to: "AI in Healthcare: 2026 Trends and Predictions"

Internal Link Preservation

• All internal links from original content are preserved
• Add new internal links to related updated content
• Never break existing internal link structure

Meta Description Update

Original: "Explore AI in healthcare trends for 2025..."
Refreshed: "Explore AI in healthcare trends for 2026..." (year updated)

Quality Scoring (Refresh vs. Original)

Target: Refresh score should be within ±0.5 points of original

Example:

• Original: 8.9/10 (Content Quality: 9.2, Citations: 8.5, Brand: 9.0, SEO: 8.8, Readability: 9.0)
• Refreshed: 9.1/10 (Content Quality: 9.3, Citations: 9.0, Brand: 9.0, SEO: 8.9, Readability: 9.2)
• Result: ✓ Within acceptable range (+0.2 improvement)

If refresh scores <8.4 (<0.5 below original):

• Flag for human review
• Identify which dimension dropped (likely Citations or SEO)
• Rerun Phase 2 (Fact-Checker) or Phase 6 (SEO Optimizer)

Version Tracking

Metadata in .docx

Document Properties:
  Title: AI in Healthcare: 2026 Trends and Predictions
  Version: 1.1
  Original Publish Date: 2025-03-15
  Last Refresh: 2026-02-17
  Refresh Scope: Medium (50%)
  Sections Updated: 6/12
  New Sources Added: 4
  Quality Score: 9.1/10 (was 8.9/10)
  Refreshed By: ContentForge

Filename Convention

Original: Article-AI-Healthcare_v1.0.docx
1st Refresh: Article-AI-Healthcare_v1.1.docx
2nd Refresh: Article-AI-Healthcare_v1.2.docx
Major Rewrite: Article-AI-Healthcare_v2.0.docx (Heavy Refresh)

Google Drive Organization

ContentForge Output/
└── Article-AI-Healthcare/
    ├── Article-AI-Healthcare_v1.0.docx (Original, 2025-03-15)
    ├── Article-AI-Healthcare_v1.1.docx (Refresh, 2026-02-17)
    └── refresh-comparison-report.txt

Comparison Report

After refresh, generate side-by-side comparison:

═══════════════════════════════════════════════════════════════
Content Refresh Comparison Report
═══════════════════════════════════════════════════════════════
Article: AI in Healthcare: 2026 Trends and Predictions
Refresh Date: 2026-02-17
Scope: Medium (50%)

Metrics Comparison:
─────────────────────────────────────────────────────────────
Metric                 │ Original (v1.0) │ Refreshed (v1.1) │ Change
─────────────────────────────────────────────────────────────
Word Count             │ 2,340           │ 2,485            │ +145 (+6%)
Quality Score          │ 8.9/10          │ 9.1/10           │ +0.2
Citations              │ 15              │ 19               │ +4
Broken Links           │ 6 (40%)         │ 0 (0%)           │ -6 (fixed)
Keyword Density        │ 2.3%            │ 2.4%             │ +0.1%
Readability (Grade)    │ 11.2            │ 10.8             │ -0.4 (easier)
Freshness Score        │ 42/100          │ 95/100           │ +53

Sections Changed:
─────────────────────────────────────────────────────────────
✓ Introduction (completely rewritten)
✓ Section 2: Statistics updated (2024 → 2026 data)
✓ Section 4: New case study added (2026 example)
✓ Section 7: Regulatory update (new FDA guidelines)
✓ Section 9: Predictions updated (2026-2028 outlook)
✓ Conclusion (completely rewritten)

Sections Preserved (Evergreen):
─────────────────────────────────────────────────────────────
  Section 1: AI Healthcare Definition
  Section 3: Historical Context (2010-2020)
  Section 6: Ethical Framework

SEO Impact Assessment:
─────────────────────────────────────────────────────────────
Target Keyword: "AI in healthcare"
  Density: 2.3% → 2.4% ✓ (within target)
  First mention: Para 1, Sentence 2 ✓ (unchanged)

Meta Title: "AI in Healthcare: 2026 Trends..."
  Length: 42 chars ✓ (optimal)
  Updated year: 2025 → 2026 ✓

URL Slug: /blog/ai-in-healthcare-2025-trends
  Preserved: ✓ (maintains backlinks)

Internal Links: 8 preserved, 3 added ✓

Estimated SEO Impact: +5-10% traffic (fresher content, fixed broken links)

═══════════════════════════════════════════════════════════════
Recommendation: Publish refreshed version, monitor rankings for 2 weeks
═══════════════════════════════════════════════════════════════

Batch Content Refresh

Use Case: Quarterly Content Audit

Agency has 50 blog posts, wants to refresh top 20 performers that are 12+ months old.

Step 1: Prepare Refresh Sheet

doc_url,refresh_scope,priority,notes
https://docs.google.com/.../article-1,medium,1,Rankings dropped from #3 to #7
https://docs.google.com/.../article-2,light,2,Just needs stat updates
https://docs.google.com/.../article-3,heavy,3,Topic outdated, needs rewrite
...

Step 2: Run Batch Refresh

/contentforge:batch-process https://docs.google.com/spreadsheets/d/ABC123

Step 3: Process (standard batch orchestration)

• Sequential — one refresh pipeline at a time. There is no concurrency.
• Queue order is set by the priority column
• Every phase of every piece is checkpointed, so an interrupted batch resumes where it stopped rather than restarting
• Completion report with before/after scores

Total time scales roughly with the number of pieces and their refresh scope; batching buys queue management and resumability, not a speedup.

Integration with Other Skills

Before Refresh:

• /contentforge:cf-audit — Identify which content needs refreshing (freshness scores, declining candidates). v4.0 file contract: when the user says "refresh what the audit found" (or --from-audit), read the recorded candidates rather than remembering them:

  python ${CLAUDE_PLUGIN_ROOT}/scripts/audit-ledger.py latest --brand <slug>
  

  Take pieces ranked by refresh_priority; each row's recommended_scope (light/medium/heavy/retire) is this skill's scope input. Exit 1 means no recorded audit exists — say so and point at /contentforge:cf-audit; never invent candidates.
• /contentforge:cf-brief — Re-run keyword + competitor research on the topic to see what the SERP now rewards and what competitors have added since you published

After Refresh:

• /contentforge:publish — Push updated content to WordPress/Webflow
• /contentforge:cf-analytics — Track refresh quality scores over time

Limitations

• Content not originally created by ContentForge has no baseline quality score, so the refresh is graded absolutely rather than as a delta — the "±0.5 of original" criterion below does not apply to imported pieces handed over from /contentforge:cf-audit
• Heavy Refresh (80%) is almost same time as new content (use sparingly)
• Requires the original .docx to be retrievable from the brand's configured tracking backend (can't refresh from published URLs alone)

Success Criteria

Good Refresh:

• Quality score within ±0.5 of original
• Freshness score improves to 85-100
• All broken links fixed
• Keyword placements preserved (density drift reported as advisory)
• SEO rankings stable or improve within 2-4 weeks

Bad Refresh (requires redo):

• Quality score drops >1.0 point
• A keyword placement slot (title, H1, first 100 words, H2s, conclusion) lost its primary keyword
• Internal links broken
• Brand voice inconsistency

Agents Used

The refresh reuses the canonical 10-phase pipeline agents (Reviewer is Phase 7, Output Manager is Phase 8):

• Heavy Refresh — full pipeline, same as new content (Step 0.5 + Phases 1-8)
• Medium Refresh — Phases 2 (Fact-Checker), 4 (Scientific Validator), 5 (Structurer), 6 (SEO/AEO/GEO), 6.5 (Humanizer), 7 (Reviewer), 8 (Output Manager)
• Light Refresh — Phases 6.5 (Humanizer), 7 (Reviewer), 8 (Output Manager)

Related Skills

• /contentforge:batch-process — Create or refresh content as a sequential, checkpointed queue
• /contentforge:cf-variants — A/B test refreshed vs. original elements
• /contentforge:cf-audit — Find refresh candidates across the whole library

Value: Preserves SEO equity (URL, internal links, keyword placements) while extending content lifespan