Contentforge

ContentForge — Enterprise Content Production

Transform a content requirement into a publication-ready, fact-checked, brand-compliant, SEO-optimized piece through a 10-phase autonomous agent pipeline (plus Step 0.5 title curation) with three-layer fact verification and 10 quality gates.

Context efficiency

Pipeline phase. Grep before Read for references/, humanization-patterns.json, brand voice profiles. Hand subagents artifact file paths plus a ≤10-line summary — never reload or inline full drafts (see Context & Handoff Rules). On /contentforge:resume, load run.json plus only the artifacts the next phase contractually needs.

Execution Protocol (CRITICAL — read first)

This skill orchestrates 10 phases plus Step 0.5 (Title Curation). Each numbered phase MUST be executed by invoking its dedicated subagent via the Task tool — DO NOT generate the deliverable yourself in a single inference pass. A single-pass generation skips the quality gates, fact-checking layers, humanizer 29-pattern catalog, and reviewer scoring that define ContentForge.

The one exception is Step 0.5: title curation is performed inline by the orchestrator (no subagent), because it requires user interaction and subagents must never wait on the user. Any subagent that needs a user decision returns a {"status": "needs_user_decision", ...} payload to the orchestrator, which owns all user interaction (including image-generation opt-in/approval).

Step 0 — Initialize the run (orchestrator only, before Step 0.5)

# 1. Create the checkpoint run (returns run_id). Capture run metadata so a
#    cross-session resume can recover keyword, audience, word count, and tone.
RUN_RESULT=$(python ${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py init \
    --brand <brand-slug> --topic "<topic>" --content-type <type> \
    --meta '{"keyword": "<primary keyword>", "audience": "<audience>", "word_count_target": <n>, "tone": "<tone>"}')
# Parse RUN_ID from the JSON result's "run_id" field.

# 2. Initialize the performance tracker for this run.
python ${CLAUDE_PLUGIN_ROOT}/scripts/pipeline-tracker.py --action init \
    --brand <brand-slug> --run-id "$RUN_ID" --content-type <type> --topic "<topic>"

Rules:

• pipeline-tracker.py is called by the orchestrator only — subagents never call it.
• Step 0.5 is exempt from tracker calls (no phase-start/phase-end for 0.5). After the title is confirmed, checkpoint it directly (see contract table).

Required Per-Phase Workflow (Phases 1–8)

For every numbered phase:

1. Mark phase start (orchestrator):

   python ${CLAUDE_PLUGIN_ROOT}/scripts/pipeline-tracker.py --action phase-start --brand <slug> --run-id "$RUN_ID" --phase <N>
   

2. Call Task with the phase's qualified subagent_type (e.g. contentforge:researcher). The Task prompt contains ONLY:

   • the artifact file paths the phase reads (per the Pipeline Contract table),
   • a ≤10-line orchestrator summary of pipeline state,
   • the brand-profile path (required for phases 0.5, 3, 5, 6, 6.5, 7),
   • the original-requirements block (topic, confirmed title, content type, audience, primary keyword, word-count target, tone).

   Subagents Read what they need from those paths. Never inline a full draft into a Task prompt.

3. Verify the quality gate yourself. Gate ownership belongs to the orchestrator: check the returned artifact against the gate criteria in the Pipeline Contract table — count sources, check word count and citation density, and run python ${CLAUDE_PLUGIN_ROOT}/scripts/text-metrics.py for burstiness, Flesch-Kincaid grade, and keyword-placement checks. A subagent's self-reported "PASS" alone is not a gate pass.

4. On gate PASS, checkpoint the artifact so the run is resumable:

   python ${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py save \
       --brand <slug> --run-id "$RUN_ID" --phase <N> --content-file <artifact-path> --extension <md|json|txt>
   

   Phases 3.5 and 6 also produce a companion manifest (phase-3.5-visual-manifest.json, phase-6-structure-manifest.json) — place it at its canonical path inside the same run directory.

5. Mark phase end with the output word count:

   python ${CLAUDE_PLUGIN_ROOT}/scripts/pipeline-tracker.py --action phase-end --brand <slug> --run-id "$RUN_ID" --phase <N> --content-words <count>
   

6. Emit the audit line (so users can see real-time progress):

   [PHASE-AUDIT] phase=<N> name=<name> status=<PASS|FAIL> output_summary="<one line>" gate=<PASS|FAIL>
   

7. On gate FAIL, loop per the contract table's loop-target column. Before looping, record the loop and check limits:

   python ${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py loop --brand <slug> --run-id "$RUN_ID" --edge phase_<N>_to_<target>
   

   Limits: max 2 loops per edge, max 5 loops total per run. If a limit is reached: do NOT loop — mark the run for human review, finalize --status failed, and halt. When Phase 7 orders rework, the pending_rework field in run.json records the target phase and the reviewer's feedback so /contentforge:resume continues the rework instead of skipping it. Do not overwrite the saved checkpoint of an upstream phase that already passed — re-save only the looped phase when it passes.

Image approval (orchestrator-owned, after Phase 3.5)

The visual-asset-annotator never waits on the user. It generates candidates (when image generation is opted in) and records each in phase-3.5-visual-manifest.json with approved_by_user: false. After Gate 3.5 passes, the orchestrator presents the generated visuals to the user for approval:

1. Show each generated asset (path + description + placement) and ask approve / regenerate / drop.
2. Update approved_by_user in the manifest for approved assets.
3. For rejects, re-invoke contentforge:visual-asset-annotator with the rejection feedback (counts as a Phase 3.5 re-run, not a loop edge).
4. Phase 8 embeds only assets with approved_by_user: true — unapproved assets stay out of the .docx.

If the annotator returns {"status": "needs_user_decision", ...} (e.g., image-generation opt-in was never given), ask the user, then re-invoke with the answer.

Pipeline Contract (inputs → outputs → gate → loop target)

All artifacts live in the canonical run directory ~/.claude-marketing/{brand-slug}/runs/{run_id}/ alongside run.json (the manifest).

Phase	subagent_type	Reads (paths)	Writes (artifact)	Quality gate (orchestrator-verified)	Gate-FAIL loop target
0.5	— inline (orchestrator)	brand profile, requirements	phase-0.5-title.txt	User-confirmed title (or --title bypass) — user checkpoint, not a numbered quality gate	Regenerate title options
1	contentforge:researcher	phase-0.5-title.txt, requirements, brand profile	phase-1-research.md	Gate 1: 12–15 sources collected; ≥10 citable; ≥5 with reliability ≥8; differentiated angle documented	Re-run Phase 1 with broader search
2	contentforge:fact-checker	phase-1-research.md	phase-2-factcheck.md	Gate 2: ≥80% of claims verified; zero UNRESOLVED flags (flagged claims must be removed or re-sourced); ≤3 unverified tolerated; all cited URLs live	Phase 1 (find alternative sources)
3	contentforge:content-drafter	phase-1-research.md, phase-2-factcheck.md, brand profile, requirements	phase-3-draft.md	Gate 3: word count ±10% of target; all outline sections covered; ≥1 citation per 300 words	Re-run Phase 3
3.5	contentforge:visual-asset-annotator	phase-3-draft.md, phase-2-factcheck.md	phase-3.5-visuals.md + phase-3.5-visual-manifest.json	Gate 3.5: every chart traceable to a verified statistic; manifest complete (placement, alt text, data source); human-action TODOs marked	Re-run Phase 3.5
4	contentforge:scientific-validator	phase-3-draft.md, phase-3.5-visual-manifest.json, phase-2-factcheck.md	phase-4-validation.md	Gate 4: zero hallucinations; every claim traceable to a cited source; logic consistent	Phase 3 (with the specific claims to fix)
5	contentforge:structurer-proofreader	phase-3-draft.md, phase-4-validation.md, brand profile	phase-5-structured.md	Gate 5: zero grammar/spelling errors on re-scan; readability within ±0.5 grade of the content-type target (text-metrics.py); brand terminology compliance	Re-run Phase 5
6	contentforge:seo-geo-optimizer	phase-5-structured.md, brand profile, requirements (keyword)	phase-6-seo.md + phase-6-structure-manifest.json	Gate 6: keyword PLACEMENTS present — title, first 100 words, ≥2 H2s, conclusion, meta description (density is advisory, ~1–2%); meta title + description generated	Re-run Phase 6
6.5	contentforge:humanizer	phase-6-seo.md, phase-6-structure-manifest.json, brand profile	phase-6.5-humanized.md	Gate 6.5: AI patterns removed; burstiness ≥0.7 (text-metrics.py); keyword placements preserved per structure manifest	Re-run Phase 6.5 with the violated constraint stated (incl. structure-manifest mismatch)
7	contentforge:reviewer	ALL prior artifact paths, brand profile, requirements, config/scoring-thresholds.json	phase-7-review.json	Gate 7: reviewer decision tree per config/scoring-thresholds.json — approve ≥7.0 (industry-adjusted); all dimension minimums met	5.0–6.9 → loop to responsible phase (recorded as pending_rework); <5.0 → human review, halt
8	contentforge:output-manager	phase-6.5-humanized.md, phase-7-review.json, phase-3.5-visual-manifest.json, run.json	phase-8-output.json + .docx	Gate 8: .docx generated; Appendices A/B/C present; delivery location verified	Re-run Phase 8; if generation still fails, save markdown + reports locally and report the failure

That is 10 quality gates — one for each of phases 1, 2, 3, 3.5, 4, 5, 6, 6.5, 7, and 8.

Single source of truth for numbers: approval thresholds, loop bands, dimension weights, dimension minimums, and industry overrides live in config/scoring-thresholds.json. Prose in this document references those values; if they ever disagree, the config wins.

Context & Handoff Rules

• Subagents receive artifact paths, a ≤10-line orchestrator summary, the brand-profile path (phases 0.5, 3, 5, 6, 6.5, 7), and the original-requirements block. They Read what they need.
• Never inline a full draft into a Task prompt, and never reload a full draft into the orchestrator's context when a path reference will do.
• The reviewer (Phase 7) must receive the paths of all prior artifacts, not just the Phase 6.5 output.

Final Output Requirements

After Phase 8 completes, the output-manager subagent must produce a Microsoft Word .docx file by calling:

python ${CLAUDE_PLUGIN_ROOT}/scripts/generate-docx.py \
    --content <article.md> \
    --output <local-path>.docx \
    --reports <reports.json> \
    --brand "<brand>" \
    --content-type <type>

The .docx must contain: title page, full article body, sources/citations, Appendix A (SEO Scorecard), Appendix B (Quality Scorecard), Appendix C (Production Details).

Dual-copy save: the .docx is written into the run directory (~/.claude-marketing/{brand-slug}/runs/{run_id}/) AND copied to the user-visible folder ~/Documents/ContentForge/{Brand}/. Always tell the user the ~/Documents/ContentForge/ path. If the brand has Google Drive configured (tracking.backend == "google"), additionally upload the .docx via drive-uploader.py.

Then finalize the run:

python ${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py finalize --brand <slug> --run-id "$RUN_ID" --status completed

Why This Matters

Skipping the Task-tool orchestration means: no real fact-checking, no real humanizer (29-pattern AI removal won't fire), no real reviewer scoring — the pipeline becomes single-pass content generation labeled with fake phase names. The audit trail (run.json, the per-phase checkpoint artifacts, [PHASE-AUDIT] lines, real reviewer score) is the proof of execution. If those artifacts don't exist after a run, the pipeline didn't actually run.

When to Use

Use /contentforge when you need:

• Single high-quality content piece (article, blog, whitepaper, FAQ, research paper)
• Research-backed content with verified citations
• Brand-compliant content for regulated industries (Pharma, BFSI, Healthcare, Legal)
• SEO-optimized content with keyword targeting and meta tags
• Natural-sounding content with AI patterns removed (Phase 6.5 Humanizer)

For multiple pieces, use /contentforge:batch-process — a prioritized, checkpointed queue that runs the same pipeline per piece.

What This Command Does

Runs your content through 10 specialized agents, each behind a quality gate:

1. Research Agent — SERP analysis, source mining, competitive analysis, structured outline
2. Fact Checker — URL verification, claim validation, confidence scoring
3. Content Drafter — First draft with brand voice, SME calibration via industry knowledge packs
4. Visual Asset Annotator — Chart generation from verified stats, visual markers, asset manifest
5. Scientific Validator — Hallucination detection, domain-specific validation, logic validation
6. Structurer & Proofreader — Grammar/spelling correction, readability optimization, brand compliance
7. SEO/GEO Optimizer — Keyword placements, meta tag generation, internal linking markers
8. Humanizer — AI pattern removal, sentence variety (burstiness), brand personality
9. Reviewer — 5-dimension quality scoring (weights per config/scoring-thresholds.json)
10. Output Manager — .docx with embedded charts and internal links, dual-copy save, optional Drive upload

Quality Gates: 10 gates (phases 1–8 including 3.5 and 6.5). On failure the pipeline loops per the contract table — max 2 loops per edge, max 5 total, then human escalation.

Required Inputs

Minimum Required:

• Topic — What the content is about (e.g., "AI in Healthcare", "remote work productivity")
• Content Type — article, blog, whitepaper, faq, research_paper
• Brand — Which brand profile to use (create with /contentforge:cf-style-guide if new brand). See No-Brand Mode below if none exists.

Pre-Flight Validation: After gathering inputs, validate the brand profile for completeness (voice, guardrails, audience, industry pack). For regulated industries (pharma, BFSI, healthcare, legal), guardrails are required — warn if they're empty and ask whether to proceed or update the profile first.

Optional:

• Target Audience — Who this content is for (e.g., "Healthcare CIOs")
• Word Count — Target length (defaults to content type standard)
• Primary Keyword — Main SEO keyword to optimize for
• Tone — Overrides brand default (authoritative, conversational, technical, witty)
• --sources=<urls or file> — User-supplied reference URLs (required in No-Web Mode; otherwise merged into Phase 1 research)
• --title="Exact Title" — Non-interactive title bypass (see Title Curation)

No-Brand Mode

If the user has no brand profile and declines to create one:

• Proceed with generic defaults (neutral professional voice, no terminology enforcement).
• Phase 5 skips the brand-compliance sub-check.
• Phase 7 scores the Brand Compliance dimension as SKIPPED and flags the run for manual review; the Quality Scorecard notes "no brand profile configured."
• Regulated-industry topics (pharma, BFSI, healthcare, legal) must NOT run in no-brand mode — require a profile with guardrails first.

No-Web Mode

If web research is unavailable (offline, no search tool, MCP down):

• Require user-supplied sources via --sources=.
• Skip SERP analysis; build the outline from the provided sources.
• Mark every citation "user-provided, unverified" in the fact-check ledger.
• Phase 7 caps Citation Integrity at 6.0 and notes the cap in the scorecard.
• If neither web access nor user sources are available, stop and tell the user research is impossible — do not fabricate sources.

How to Use

Interactive Mode (Recommended for First-Time Users)

/contentforge

Prompts you for:

1. Topic (the subject — NOT the final title)
2. Content Type (select from 5 options)
3. Brand (select from existing profiles)
4. Target Audience
5. Word Count (or use default)
6. Primary Keyword

Then generates 4-5 title options (different angles: benefit-driven, how-to, data-driven, question-based, contrarian). You select, modify, or provide your own title. Pipeline starts only after title confirmation.

Quick Mode (Topic Provided)

/contentforge "AI in Healthcare" --type=article --brand=acmemed --audience="Healthcare CIOs" --keyword="AI healthcare trends"

Even in quick mode, the system generates title options and asks you to select before starting Phase 1 — unless you pass --title.

Non-Interactive Mode (evals, batch, CI)

/contentforge "AI in Healthcare" --type=article --brand=acmemed --title="How AI Is Reshaping Hospital Diagnostics"

--title skips option generation and uses the given title verbatim. The bypassed title is still checkpointed as phase-0.5-title.txt.

Use Existing Google Sheet Requirement

/contentforge --sheet-url=https://docs.google.com/spreadsheets/d/ABC123 --row=5

Reads requirement from Row 5 of the sheet.

What Happens

Step 0.5: Title Curation — MANDATORY, inline

Before the pipeline starts, the orchestrator (inline — no subagent, no tracker calls) generates 4-5 SEO-optimized title options using the topic, content type, brand voice, audience, and primary keyword. Each title takes a different angle:

• Benefit-driven — leads with reader value
• How-to / Tactical — actionable, instructional
• Data-driven / Stat-led — opens with a number or trend
• Question-based / Curiosity — provokes the reader
• Contrarian / Unexpected — challenges convention

You select, modify, or provide your own title. The confirmed title becomes the anchor for the entire pipeline — research, outline, SEO, and final output all flow from it.

Do NOT auto-select a title. The only exception is an explicit --title="..." bypass, which uses the supplied title verbatim (for non-interactive runs and evals). Either way, checkpoint the confirmed title as phase-0.5-title.txt before Phase 1.

Phases 1–8 at a glance

Gate criteria and loop targets for every phase are defined once, in the Pipeline Contract table above.

• Phase 1: Research — SERP analysis anchored on the confirmed title; mines 12–15 authoritative sources; competitor analysis; structured outline. Gate 1.
• Phase 2: Fact Checking — verifies all URLs are live, validates claims against sources, assigns confidence tiers, flags unverifiable claims for removal or re-sourcing. Gate 2.
• Phase 3: Content Drafting — first draft in brand voice with inline citations (APA format), targeting word count ±10%. Gate 3.
• Phase 3.5: Visual Assets — charts from verified stats, visual markers, asset manifest with placement, alt text, and data source. Gate 3.5.
• Phase 4: Scientific Validation — hallucination scan, claim traceability, logic validation. Gate 4; failures loop to Phase 3.
• Phase 5: Structure & Proofread — grammar/spelling, readability to the content-type target (±0.5 grade), brand terminology and style. Gate 5.
• Phase 6: SEO/GEO — keyword placements (title, first 100 words, ≥2 H2s, conclusion, meta description), meta tags, URL slug, AI-answer-engine readiness, structure manifest. Density is advisory (~1–2%), not a gate. Gate 6.
• Phase 6.5: Humanizer — 29-pattern AI-telltale removal, burstiness ≥0.7, brand personality, SEO-placement preservation validated against the structure manifest. Gate 6.5.
• Phase 7: Reviewer — 5-dimension weighted scoring per config/scoring-thresholds.json; approve ≥7.0 (industry-adjusted), loop 5.0–6.9, human review <5.0. Gate 7.
• Phase 8: Output — .docx generation with appendices, dual-copy save, optional Drive upload, tracking update. Gate 8.

If a phase loops back: the system shows which phase failed, why, and what it's fixing. Loops are automatic — you don't need to do anything unless it escalates to human review.

Output Example

Every pipeline run ends with a Completion Card showing scores, stats, and delivery status. This card is mandatory — it's shown in the conversation AND added as an appendix in the .docx file.

Example Completion Card (SYNTHETIC EXAMPLE — fabricated for illustration; never reuse these numbers):

CONTENTFORGE — COMPLETION CARD

Content:  "AI in Healthcare: Emerging Trends" | AcmeMed | Article | ✅ APPROVED

Quality Score: 9.2/10 (Grade A+)
  Content Quality:    9.5/10 ✅
  Citation Integrity: 9.0/10 ✅
  Brand Compliance:   9.5/10 ✅
  SEO Performance:    8.8/10 ✅
  Readability:        9.0/10 ✅

Content Stats:
  Words: 1,947 (target 1,500-2,000) ✅ | Citations: 14 sources ✅
  Keyword placements: all critical positions ✅ | Readability: Grade 11.2 ✅
  Burstiness: 0.78 ✅ | AI Patterns: 0 remaining ✅ | Hallucinations: 0 ✅

SEO Package:
  Meta Title: 58 chars ✅ | Meta Description: 152 chars ✅
  Internal Links: 4 applied | Feature Image: generated (user-approved)

Pipeline: 0 loops | Guardrails: verified | Run: {run_id}

Delivery:
  .docx: ✅ Generated
  Local: ✅ ~/Documents/ContentForge/AcmeMed/AI-in-Healthcare-Emerging-Trends_v1.0.docx
  Drive: ✅ uploaded (if configured) | Tracking: ✅ updated

Next: /contentforge:publish | /contentforge:social-adapt | /contentforge:translate | /contentforge:cf-variants

Content Types & Specifications

Type	Word Count	Readability	Citations
Article	1,500-2,000	Grade 10-12	8-12
Blog	800-1,500	Grade 8-10	5-8
Whitepaper	2,500-5,000	Grade 12-14	15-25
FAQ	600-1,200	Grade 8-10	3-5
Research Paper	4,000-8,000	Grade 14-16	25-50

Readability is gated at ±0.5 grade of the content-type target (verified via text-metrics.py).

Brand Profile Setup

Before using ContentForge, create a brand profile:

/contentforge:cf-style-guide

Provide your brand name, industry, voice guidelines (or share existing documents/URLs), and ContentForge generates the profile JSON automatically.

Alternatively, copy config/brand-registry-template.json and fill in manually.

Canonical profile location: ~/.claude-marketing/{brand-slug}/Brand-Guidelines/{BrandName}-brand-profile.json, where the brand slug is lowercase alphanumerics + hyphens. Resolution order: local file first, then Drive cache (Cowork).

Brand Profile Includes:

• Voice & Tone (authoritative, conversational, technical, witty)
• Terminology (approved terms, banned phrases)
• Style Guide (formatting preferences, citation style)
• Guardrails (topics to avoid, compliance requirements)
• Industry Context (Pharma, BFSI, Healthcare, Legal)
• Personality Profile (authoritative, conversational, technical, witty)

Brand profiles are cached (SHA256 hash) so repeat runs skip re-parsing.

See the User Guide for detailed setup instructions.

Quality Assurance

Three-Layer Fact Verification

1. Phase 2 (Fact Checker): URL verification, claim validation
2. Phase 4 (Scientific Validator): Hallucination detection
3. Phase 7 (Reviewer): Final citation integrity scoring

Feedback Loop Management

• Max 2 loops per edge (e.g., Phase 4 → Phase 3, or Phase 7 → Phase X)
• Max 5 loops total per run before human escalation
• Loop counts are recorded in run.json via checkpoint-manager.py loop and survive resume.

Human Review Escalation

Content is flagged for human review if:

• Quality score <5.0/10 (per config/scoring-thresholds.json)
• Critical brand violations detected
• Loop limits reached without passing
• User explicitly requests review
• Run executed in No-Brand Mode (Brand Compliance = SKIPPED)

Flagged content is NEVER auto-published.

Integration with Other Skills

Before ContentForge:

• /contentforge:cf-style-guide — Create brand profile if new brand
• /contentforge:content-brief — Generate research-backed content brief with keyword analysis

Instead of ContentForge (for scale):

• /contentforge:batch-process — Queue 10-50+ pieces through the same pipeline

After ContentForge:

• /contentforge:content-refresh — Update content 6-12 months later with fresh data
• /contentforge:cf-variants — Create A/B test headline/hook/CTA variations
• /contentforge:publish — Publish to Webflow or WordPress via MCP
• /contentforge:social-adapt — Transform article into LinkedIn, Twitter/X, Instagram, Facebook, Threads posts
• /contentforge:translate — Translate preserving brand voice (15+ languages)
• /contentforge:cf-video-script — Generate timestamped video scripts from the article
• /contentforge:cf-analytics — Record quality scores for trend tracking

Requirements

MCP Integrations (Optional)

• Google Sheets — Requirement intake for batch processing, quality tracking
• Google Drive — Brand knowledge vault, output .docx storage
• Webflow/WordPress — Direct CMS publishing via /contentforge:publish

Run /contentforge:cf-integrations to check your connector status. Run /contentforge:cf-connect <name> for setup guides.

Environment

• Claude Code or Cowork (latest version)
• Internet connection for Phase 1 web research — or --sources= in No-Web Mode

Troubleshooting

"Brand profile not found"

When: You run /contentforge with a brand that doesn't have a profile yet.

Fix:

1. Create a brand profile (recommended):

   /contentforge:cf-style-guide
   

   Answer 3 questions (name, tone, industry) and you're ready.
2. Or specify a different brand:

   /contentforge "your topic" --brand=existing-brand
   

3. Or proceed in No-Brand Mode (non-regulated topics only — see Required Inputs).

"Quality score <5.0, flagged for review"

When: Content didn't meet the minimum quality threshold after all feedback loops.

Common causes and fixes:

• Topic too vague → Be more specific: "AI in healthcare" → "AI diagnostic tools for rural hospitals"
• Sources behind paywalls → Provide accessible reference URLs with --sources=
• Brand profile incomplete → Run /contentforge:cf-style-guide --update [brand] to add guardrails and terminology
• Niche topic with few sources → Consider a broader angle or provide your own source URLs

"Max loops exceeded"

When: The pipeline hit a loop limit (2 per edge or 5 total) without reaching the quality threshold.

Fix:

1. Check which dimension scored lowest in phase-7-review.json (Content Quality? Citations? Brand Compliance?)
2. If Content Quality is low → topic needs more depth or the angle is too broad
3. If Citation Integrity is low → sources are weak or behind paywalls
4. If Brand Compliance is low → brand profile may be incomplete
5. Re-run with adjustments: more specific topic, better keywords, or updated brand profile

"Pipeline appears stalled"

API rate limits or network latency cause delays; ContentForge auto-retries with backoff. If it persists:

1. Check internet connection
2. Run /contentforge:cf-integrations to verify MCP servers are responding
3. If the session died, run /contentforge:resume — every gate-passed phase is checkpointed
4. Long content types (whitepaper, research paper) legitimately take much longer than blogs

"Guardrails empty — compliance skipped"

When: Your brand profile doesn't have prohibited claims or required disclaimers defined.

Impact: Phase 5 reports brand compliance "SKIPPED" instead of actually checking content. Phase 7 applies the empty-guardrails penalty per config/scoring-thresholds.json.

Fix:

/contentforge:cf-style-guide --update [brand]

Add at minimum: 3-5 prohibited claims, any required legal disclaimers, and industry-specific restrictions.

For regulated industries (pharma, BFSI, healthcare, legal): This is critical. Empty guardrails mean no compliance verification.

Pipeline phase explanations

During content production, you'll see updates as each phase completes:

Phase	What's Happening	What You'll See
Step 0.5: Title Curation	Generating 4-5 title options	Title options with character counts
Phase 1: Research	SERP analysis, source mining, outline	Source count, outline sections
Phase 2: Fact Check	URL verification, claim validation	Verified %, flagged claims
Phase 3: Draft	First draft with brand voice	Word count, citation density
Phase 3.5: Visuals	Charts, image generation (if opted in)	Visual count, chart specs
Phase 4: Validation	Hallucination detection	Zero hallucinations confirmed
Phase 5: Structure	Grammar, readability, brand compliance	Compliance status
Phase 6: SEO	Keyword placements, meta tags	Placement checklist, GEO score
Phase 6.5: Humanize	AI pattern removal, personality	Burstiness score
Phase 7: Review	5-dimension quality scoring	Score breakdown, pass/fail
Phase 8: Output	.docx generation, tracking, delivery	Output location, final metrics

Example Workflow

(SYNTHETIC EXAMPLE — fabricated for illustration; never reuse these numbers.)

Scenario: Create 1 thought leadership article for the AcmeMed brand

Step 1: Create Brand Profile (One-Time Setup)

/contentforge:cf-style-guide

Provide: Brand name (AcmeMed), Industry (Healthcare), Voice (Authoritative), Tone (Professional), Terminology, Guardrails

Step 2: Start Content Production

/contentforge "AI-Powered Diagnostics in Precision Medicine" --type=article --brand=acmemed --audience="Healthcare Executives" --keyword="AI diagnostics precision medicine"

Step 3: Select Title

ContentForge generates 4-5 title options:

1. "AI-Powered Diagnostics: The Future of Precision Medicine"
2. "How AI Diagnostics Are Transforming Precision Medicine for Healthcare Leaders"
3. "5 AI Diagnostic Breakthroughs Reshaping Precision Medicine Right Now"
4. "The Executive's Guide to AI-Powered Precision Medicine Diagnostics"
5. "Why AI Diagnostics in Precision Medicine Are Finally Delivering on the Promise"

You select Option 1 → Pipeline starts with that title as the anchor.

Step 4: Review Output

• Quality Score: 9.1/10 ✅
• Word Count: 1,922 ✅
• Citations: 12 sources ✅
• SEO: all keyword placements hit ✅

Step 5: Publish

/contentforge:publish --platform=webflow

Limitations

• Sequential phases — the pipeline is strictly ordered; each gate consumes the previous phase's artifact
• Depth takes time — the pipeline cannot be rushed without compromising quality (use --title and --sources to shave the interactive steps)
• Best with brand profile — No-Brand Mode works but the Brand Compliance dimension is SKIPPED and the run is flagged for manual review

Related Skills

• /contentforge:batch-process — Queue 10-50+ pieces through the same pipeline
• /contentforge:content-refresh — Update old content with fresh data
• /contentforge:cf-variants — A/B test headline/hook/CTA variations
• /contentforge:cf-analytics — Track quality scores and performance
• /contentforge:social-adapt — Transform article into social media posts
• /contentforge:publish — Publish to Webflow/WordPress
• /contentforge:translate — Translate preserving brand voice
• /contentforge:content-brief — Generate research-backed content briefs

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Pipeline: 10 phases plus Step 0.5 (Title Curation); 10 quality gates. Phase agents are defined in agents/*.md and enumerated by scripts/plugin-metadata.py --section pipeline. Post-pipeline agents include Batch Orchestrator, Social Adapter, and Translator.

Quality target: composite Reviewer score ≥7.0 to pass (industry-adjusted per config/scoring-thresholds.json); max 2 loops per edge, 5 total; three-layer verification (Fact Checker → Scientific Validator → Reviewer).