Full Content Generation Pipeline

Mission

Execute the complete content generation workflow end-to-end: Extract stories from Linear → Generate variations → Score all → Critic review → Select best → Output ONE ready-to-post piece.

Overview

This command orchestrates all 5 stages of the content generation system:

1. Story Extraction (/content-extract-stories)
2. Draft Generation (/content-generate-drafts for each theme)
3. Automated Scoring (/content-score-all)
4. Critic Review (/content-critic-review)
5. Best Selection (/content-select-best)

Expected Output: ONE piece in content-ready.md scoring 25+/30, ready for human approval and posting.

Expected Duration: < 3 minutes for 5 themes → 25 variations → 1 selected piece

Process

Stage 1: Story Extraction

Command: /content-extract-stories

Actions:

• Connect to Linear MCP
• Fetch tasks POA-X through POA-Y
• Analyze story content and identify themes
• Extract minimum 5 distinct themes
• Write to themes-memory.md
• Update Linear tasks with extraction confirmations

Success Criteria:

• Minimum 5 themes extracted
• themes-memory.md populated with structured themes
• Linear tasks updated with confirmation comments
• No hallucinated information

Failure Handling:

• If Linear MCP fails: Check .env for LINEAR_API_KEY, verify MCP installation
• If < 5 themes: Alert user, proceed with available themes
• If rate limit hit: Batch requests, wait 60s, retry

Stage Output: themes-memory.md with 5+ themes

---

Stage 2: Draft Generation

Command: /content-generate-drafts {theme} for EACH theme

Actions:

• For each theme in themes-memory.md:
  • Spawn 5 parallel Draft Generator sub-agents
  • Each sub-agent targets DIFFERENT bias combinations:
    • Bold Statement (Contrast, Authority)
    • Story Hook (Curiosity, Liking)
    • Problem-Solution (Social Proof, Reciprocation)
    • Data-Driven (Authority, Reason-Respecting)
    • Emotional (Liking, Stress-Influence, Lollapalooza)
  • Generate 5 unique variations per theme
  • Write to content-drafts.md

Success Criteria:

• 5 variations per theme (25 total for 5 themes)
• Each variation >70% different from others
• All variations follow Hook-Content-CTA structure
• Bias targeting explicit and diverse

Failure Handling:

• If variation count < 5 per theme: Regenerate missing variations
• If similarity > 30% between variations: Regenerate duplicates
• If bias targeting unclear: Review draft-generator.md specs

Stage Output: content-drafts.md with 25 variations (5 themes × 5 variations)

---

Stage 3: Automated Scoring

Command: /content-score-all

Actions:

• Read all variations from content-drafts.md
• Apply 3-framework scoring to each:
  • Gap Selling (0-10): Problem clarity + Impact + Solution value
  • Cognitive Biases (count): Activated biases + Lollapalooza bonus
  • Decision Framework (0-10): Hook strength + Content value + CTA clarity
• Calculate total score (Gap + Biases + Decision = XX/30)
• Update content-drafts.md with complete score breakdowns

Success Criteria:

• All 25 variations scored
• Score breakdowns complete (subscore details)
• Minimum 80% of content scores 20+/30
• Total scores calculated correctly

Failure Handling:

• If < 50% score 20+/30: Alert quality issue, consider regenerating
• If scoring formulas inconsistent: Review scorer.md rubrics
• If subscore missing: Re-run scoring for affected variations

Stage Output: content-drafts.md with complete scores for all variations

---

Stage 4: Critic Review

Command: /content-critic-review

Actions:

• Review all variations scoring 15+/30
• Provide specific improvement suggestions
• Verify factual accuracy against Linear stories
• Assign ✅ PASS or ❌ FAIL verdict
• Filter out < 20/30 pieces
• Update content-drafts.md with critic notes

Success Criteria:

• All 20+/30 variations reviewed
• Critic notes complete (Strengths, Weaknesses, Suggestions)
• PASS/FAIL verdicts assigned
• Factual accuracy verified (no hallucinations)

Failure Handling:

• If no PASS content: Alert pipeline failure, regenerate with constraints
• If factual inaccuracies found: Flag for correction, re-verify
• If critic notes missing: Re-run review for affected variations

Stage Output: content-drafts.md with critic verdicts and notes

---

Stage 5: Best Selection

Command: /content-select-best

Actions:

• Filter for PASS content (20+/30 scores)
• Rank by total score (descending)
• Apply tie-breaker rules if needed:
  1. Gap Selling subscore
  2. Hook strength
  3. Lollapalooza effect (5+ biases)
  4. Bias diversity
  5. Theme novelty
  6. Human judgment flag
• Validate selection against quality gates
• Format for content-ready.md with posting instructions
• Document top 3 runner-ups

Success Criteria:

• ONE piece selected (not zero, not multiple)
• Selected piece scores 20+/30 (ideally 25+/30)
• content-ready.md overwritten with formatted output
• Posting instructions included
• Runner-ups documented

Failure Handling:

• If no PASS content: STOP pipeline, alert user
• If tie-breakers don't resolve: Flag for human decision
• If content-ready.md already full: Prompt to archive or overwrite

Stage Output: content-ready.md with ONE ready-to-post piece

---

Execution Flow

START
  ↓
[Stage 1: Extract Stories]
  ├─ Linear MCP → POA-5 to POA-14
  ├─ Identify themes (min 5)
  └─ Output: themes-memory.md
  ↓
[Stage 2: Generate Drafts]
  ├─ For each theme (5 themes):
  │   ├─ Spawn 5 parallel sub-agents
  │   ├─ Generate 5 variations (different bias combos)
  │   └─ Total: 25 variations
  └─ Output: content-drafts.md
  ↓
[Stage 3: Score All]
  ├─ Apply Gap Selling (0-10)
  ├─ Count Cognitive Biases
  ├─ Apply Decision Framework (0-10)
  └─ Output: content-drafts.md with scores
  ↓
[Stage 4: Critic Review]
  ├─ Review 20+/30 content
  ├─ Provide improvement suggestions
  ├─ Assign PASS/FAIL verdicts
  └─ Output: content-drafts.md with critic notes
  ↓
[Stage 5: Select Best]
  ├─ Rank PASS content
  ├─ Apply tie-breakers
  ├─ Validate selection
  └─ Output: content-ready.md (ONE piece)
  ↓
[Human Approval Required]
  ├─ Review content-ready.md
  ├─ Post to Twitter/X
  └─ Capture metrics after 48 hours
  ↓
END

Pre-Execution Checklist

Before running full pipeline, verify:

Infrastructure:

• Linear MCP installed and configured
• LINEAR_API_KEY set in .env file
• All framework docs accessible (gap_selling.md, bias_checklist_munger.md, effective-decision-making-framework.md)
• File paths correct (themes-memory.md, content-drafts.md, content-ready.md)

Content Readiness:

• Linear tasks POA-5 to POA-14 have story content
• Stories contain sufficient detail for theme extraction
• content-ready.md is clear (or content archived to content-posted.md)

System Resources:

• Network connectivity stable (for Linear API calls)
• Sufficient API quota (Linear rate limit: 100 req/min)
• Execution environment ready (no blocking processes)

Validation Checkpoints

After Stage 1 (Story Extraction)

# Verify themes extracted
grep -c "## Theme:" /home/rpiplewar/fast_dot_ai/poasting/themes-memory.md
# Should be >= 5

After Stage 2 (Draft Generation)

# Verify variations generated
grep -c "### Variation" /home/rpiplewar/fast_dot_ai/poasting/content-drafts.md
# Should be >= 25 (5 themes × 5 variations)

After Stage 3 (Automated Scoring)

# Verify scores added
grep -c "TOTAL:" /home/rpiplewar/fast_dot_ai/poasting/content-drafts.md
# Should match variation count

After Stage 4 (Critic Review)

# Verify verdicts assigned
grep -c "Verdict: PASS\|FAIL" /home/rpiplewar/fast_dot_ai/poasting/content-drafts.md
# Should match variations with 15+/30 scores

After Stage 5 (Best Selection)

# Verify single piece selected
grep -c "# Content Ready to Post" /home/rpiplewar/fast_dot_ai/poasting/content-ready.md
# Should be exactly 1

Error Handling & Recovery

Pipeline Failure Scenarios

Stage 1 Failure (Story Extraction):

❌ Stage 1 Failed: Could not extract stories from Linear

Possible Causes:
- Linear MCP not configured
- LINEAR_API_KEY missing or invalid
- Network connectivity issues
- Tasks POA-5 to POA-14 not accessible

Recovery:
1. Check .env file for LINEAR_API_KEY
2. Verify Linear MCP installation: mcp__linear__list_issues test
3. Confirm network connectivity
4. Retry: /content-extract-stories

Pipeline STOPPED at Stage 1. Fix issues before retrying full pipeline.

Stage 2 Failure (Draft Generation):

❌ Stage 2 Failed: Insufficient variations generated

Possible Causes:
- Theme quality low (not enough content generation potential)
- Draft generator specs unclear
- Agent spawning failed

Recovery:
1. Review themes in themes-memory.md for clarity
2. Manually run: /content-generate-drafts {theme} for each theme
3. Verify draft-generator.md agent specs
4. Check for variation diversity (>70% different)

Pipeline STOPPED at Stage 2. Complete draft generation before continuing.

Stage 3 Failure (Automated Scoring):

❌ Stage 3 Failed: Scoring incomplete or inconsistent

Possible Causes:
- Framework docs inaccessible
- Scoring formulas incorrect
- Subscore calculation errors

Recovery:
1. Verify framework docs accessible (gap_selling.md, bias_checklist_munger.md, effective-decision-making-framework.md)
2. Review scorer.md agent specs
3. Manually run: /content-score-all
4. Compare sample scores vs manual evaluation

Pipeline STOPPED at Stage 3. Complete scoring before continuing.

Stage 4 Failure (Critic Review):

❌ Stage 4 Failed: No PASS content after critic review

Possible Causes:
- Content quality below 20/30 threshold
- Critic too harsh (scoring too low)
- Theme selection poor

Recovery:
1. Review content-drafts.md scores (check if consistently low)
2. If scores 15-19/30: Adjust scoring weights in scorer.md
3. If scores < 15/30: Regenerate content with stronger constraints
4. Consider revising themes for better content potential

Pipeline STOPPED at Stage 4. Fix quality issues before continuing.

Stage 5 Failure (Best Selection):

❌ Stage 5 Failed: No content available for selection

Possible Causes:
- No PASS content from Stage 4
- All content < 20/30 threshold
- Selection criteria too strict

Recovery:
1. Review Critic verdicts in content-drafts.md
2. If close to threshold (18-19/30): Consider relaxing to 18+/30 minimum
3. If far below threshold: Regenerate content from Stage 2
4. Review theme quality and bias targeting

Pipeline STOPPED at Stage 5. Fix quality issues or regenerate content.

Partial Pipeline Recovery

If pipeline stopped at Stage X, resume from that stage:

# Resume from Stage 2 (Draft Generation)
/content-generate-drafts {theme}  # For each remaining theme

# Resume from Stage 3 (Automated Scoring)
/content-score-all

# Resume from Stage 4 (Critic Review)
/content-critic-review

# Resume from Stage 5 (Best Selection)
/content-select-best

No need to re-run completed stages - pipeline is idempotent at each stage.

Success Output

✅ Full Content Generation Pipeline Complete

⏱️ Execution Time: 2m 34s

📊 Pipeline Stats:
- Themes Extracted: 7
- Variations Generated: 35 (7 themes × 5 variations)
- Content Scored: 35/35
- PASS Content: 24/35 (68.6%)
- FAIL Content: 11/35 (31.4%)

🏆 Best Content Selected:
- Theme: First Money From Code
- Variation: Bold Statement
- Score: 28/30 (EXCELLENT)
- Ranking: #1 of 24 PASS pieces

📁 Output Files Updated:
✓ themes-memory.md (7 themes)
✓ content-drafts.md (35 variations with scores)
✓ content-ready.md (1 ready-to-post piece)

📋 Next Steps:
1. Review content-ready.md
2. Perform final quality check
3. Post to Twitter/X at optimal time (8:30 AM or 5:30 PM IST)
4. Capture metrics after 48 hours
5. Move to content-posted.md with metrics

🚀 Ready for human review and posting!

Performance Metrics

Target Benchmarks:

• Execution Time: < 3 minutes
• Themes Extracted: ≥ 5
• Variations Generated: ≥ 25 (5 themes × 5 variations)
• PASS Content: ≥ 80% (20/25)
• Selected Score: ≥ 25/30 (ideally 27+/30)

Quality Thresholds:

• < 50% PASS content: Quality issue, regenerate with constraints
• Selected score < 23/30: Consider regenerating or improving themes
• Selected score ≥ 28/30: Excellent, high viral potential

Integration Notes

This command represents the complete automated content generation system. It requires:

• All 6 agent instruction documents (agents/*.md)
• All 5 individual stage commands (commands/content-*.md)
• Linear MCP integration
• All 3 framework docs (docs/frameworks/*.md)

After execution, human approval is required before posting. Performance tracking begins after posting to content-posted.md.

Advanced Options

Regenerate Pipeline with Filters:

# Regenerate specific theme only
/content-generate-drafts "First Money From Code"
/content-score-all
/content-critic-review
/content-select-best

# Regenerate with stronger bias constraints
# (Modify draft-generator.md to require 5+ biases per variation)
/content-full-pipeline

# Test pipeline without Linear extraction (use existing themes)
# Skip Stage 1, start from Stage 2

Performance Optimization:

• Parallel theme processing: Spawn all theme agents simultaneously
• Batch Linear API calls: Group requests to avoid rate limits
• Cache framework docs: Load once, reuse across agents
• Incremental updates: Only re-score modified variations

CRITICAL RULES

1. No File Proliferation: Use ONLY 4 pipeline files (themes-memory.md, content-drafts.md, content-ready.md, content-posted.md)
2. ONE Piece in content-ready.md: Always exactly ONE, never zero or multiple
3. Minimum Score Threshold: 20+/30 to PASS, ideally 25+/30 for selection
4. No Hallucination: All content must be traceable to Linear stories
5. Framework Completeness: All 3 frameworks applied to every variation
6. Human Approval Required: Never auto-post, always require review

Validation Checklist

Before marking pipeline complete:

• All 5 stages completed successfully
• themes-memory.md has ≥5 themes
• content-drafts.md has ≥25 variations with complete scores
• content-ready.md has EXACTLY ONE piece scoring 20+/30
• Execution time < 3 minutes
• No file proliferation (only 4 pipeline files used)
• Linear tasks updated with extraction confirmations
• No hallucinated information in any stage
• Human approval workflow clear