158 lines
4.3 KiB
Markdown
158 lines
4.3 KiB
Markdown
---
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description: "Generate 5 content variations for a theme using parallel multi-agent approach"
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---
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# Generate Content Drafts
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## Arguments
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`$ARGUMENTS` - Theme name from themes-memory.md
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## Mission
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Generate 5 unique content variations for the specified theme by spawning 5 parallel sub-agents, each targeting different cognitive bias combinations and content structures.
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## Process
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Follow the Draft Generator agent instructions (`agents/draft-generator.md`) to:
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1. **Read theme details** from themes-memory.md
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2. **Spawn 5 parallel sub-agents** (CRITICAL: simultaneous, not sequential)
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3. **Collect sub-agent outputs** (5 variations)
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4. **Write to content-drafts.md** with proper formatting
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## Execution Steps
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### Step 1: Read Theme from themes-memory.md
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**Target Theme**: $ARGUMENTS
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**Extract**:
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- Theme name and problem statement
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- Emotional hook and key insight
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- All 5 content angles
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- Source story excerpts
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**If theme not found**: List available themes and ask user to retry with correct name.
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### Step 2: Spawn 5 Parallel Sub-Agents
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**CRITICAL**: Use single message with 5 Task tool calls to spawn all sub-agents simultaneously.
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Each sub-agent receives self-contained prompt with:
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- Full theme details
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- Specific bias activation strategy
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- Content structure requirements
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- Hook-Content-CTA framework
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- Character limits (280 single or 4-6 tweet thread)
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**Sub-Agent Assignments**:
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1. **Bold Statement Generator**
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- Biases: Contrast-Misreaction + Authority-Misinfluence
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- Structure: Shocking opening → Contrast → Authority evidence → CTA
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2. **Story Hook Generator**
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- Biases: Curiosity Tendency + Liking/Loving Tendency
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- Structure: Open-loop question → Personal story → Vulnerability → Resolution
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3. **Problem-Solution Generator**
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- Biases: Social-Proof + Reciprocation + Reward/Punishment
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- Structure: Problem statement → Social proof → Solution value → Free insight
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4. **Data-Driven Generator**
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- Biases: Authority + Reason-Respecting + Availability-Misweighing
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- Structure: Surprising stat → Reasoning → Concrete example → Implication
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5. **Emotional Lollapalooza Generator**
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- Biases: Liking + Stress-Influence + 5+ biases converging
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- Structure: Emotional hook → Stress creation → Relief → Multi-bias activation
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### Step 3: Quality Check Each Variation
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Verify:
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- [ ] Content factually accurate (matches source stories)
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- [ ] Target biases clearly activated
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- [ ] Structure follows assigned format
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- [ ] Character limits respected
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- [ ] No meta-commentary (pure content)
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### Step 4: Write to content-drafts.md
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**Location**: `/home/rpiplewar/fast_dot_ai/poasting/content-drafts.md`
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**Format**:
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```markdown
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## Theme: {Theme Name}
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**Source:** {Linear Task ID}
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### Variation 1: Bold Statement
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**Content:**
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{Generated content}
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**Biases Targeted:** Contrast-Misreaction, Authority-Misinfluence
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**Scores:**
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[To be filled by Scorer agent]
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---
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### Variation 2: Story Hook
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...
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```
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**Action**: APPEND to file (don't overwrite existing content from other themes)
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## Validation Checklist
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Before marking generation complete:
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- [ ] All 5 variations generated
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- [ ] Each variation targets DIFFERENT bias combinations
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- [ ] All content factually accurate
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- [ ] Variations >70% structurally different
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- [ ] content-drafts.md updated successfully
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- [ ] No meta-commentary in output
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- [ ] Character limits respected
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- [ ] Hook-Content-CTA structure followed
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## Example Output
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```
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✅ Draft Generation Complete
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Theme: First Money From Code
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Variations Generated: 5
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1. Bold Statement (Contrast + Authority)
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2. Story Hook (Curiosity + Liking)
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3. Problem-Solution (Social Proof + Reciprocation)
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4. Data-Driven (Authority + Reason-Respecting)
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5. Emotional Lollapalooza (6 biases)
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Output File: content-drafts.md
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Next Step: Run /content-score-all to apply framework scoring
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```
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## Error Handling
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**If theme not found**:
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- List available themes from themes-memory.md
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- Ask user to specify correct theme name
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**If sub-agent fails**:
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- Retry that specific sub-agent only
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- Don't re-run all 5
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**If variations too similar**:
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- Regenerate similar variation with stronger bias differentiation
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## Next Steps
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After successful generation:
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1. Review variations in content-drafts.md
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2. Run `/content-score-all` to apply automated scoring
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3. Or continue with full pipeline if running `/content-full-pipeline`
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