Premium Article Generator

Premium Article Generator

Target: produce articles that beat sirsadalot's Reddit posts in depth, citation density, mechanism explanation, and visual quality — while using tiered model routing to keep token costs down.

The sirsadalot Quality Bar

For anyone unfamiliar: sirsadalot is the nootropics Reddit gold standard. Their posts consistently feature:

Bottom line: sirsadalot posts feel like a very smart friend who read every paper on the topic and won't bullshit you. The tone is authoritative, slightly informal, and never corporate.

Token-Efficient Tiered Workflow

The biggest token waste in article generation is using the expensive model for everything. Here's the routing:

Total: ~6,800 tokens on MiMo (cheap) + ~4,000 on DeepSeek (quality) = ~10,800 tokens per article

Compared to running the whole pipeline on DeepSeek: ~15,000+ tokens. Savings: ~30%.

How to Route

Start each phase with /model xiaomi/mimo-v2.5 for phases 1-5, then switch to /model deepseek/deepseek-v4-pro for phases 6-7. Or, for single-session execution, use delegate_task for phases 1-4 (MiMo subagents) and run phases 6-7 in the main session.

Pre-compression Rules

Before any LLM call in this pipeline:

1. Research is pre-filtered. Don't dump 20 arXiv abstracts into context. Parse them to: title, year, one-line finding, DOI. That's 80 chars per paper instead of 800.

2. Citations are key-only. During drafting, use citation keys like @berman2000 instead of full footnotes. Resolve at the end in one pass.

3. Draft in plain text, format at the end. No bold/italics/links during drafting — they add tokens and get reformatted anyway. Add formatting in the final assembly pass.

4. Charts are data-only. Pass numeric data to the chart script, not chart descriptions. The script generates the visual; the LLM only needs to provide the numbers.

Phase 1: Research at sirsadalot Depth

sirsadalot doesn't cite 3 papers and call it done. They cite 15-30. Here's the research depth target:

Required Source Categories

For any pharmacology/nootropics article, collect:

Primary mechanisms (5-8 papers):

• The original discovery paper(s)
• Key follow-ups that mapped the pathway
• Contradictory or complicating findings

Clinical data (3-5 papers):

• Human trials with effect sizes
• Meta-analyses with NNTs
• Comparative studies against standard treatments

Safety/tolerability (2-3 papers):

• Adverse event data
• Long-term safety where available
• Drug-drug interactions

Practical/recreational (community sources):

• Reddit experience reports (use reddit-data-extraction skill)
• Erowid/Bluelight trip reports for subjective effects
• r/nootropics protocol discussions

Research Output Format

# Research Dossier: [Topic]

## Primary Mechanism
| # | Author (Year) | Journal | Key Finding | Score |
|---|--------------|---------|-------------|-------|
| 1 | Last et al. (20XX) | Nature | X mediates Y via Z pathway | 10/10 |
| 2 | ... | ... | ... | ... |

## Clinical Evidence (Human)
| # | Author (Year) | Design | N | Effect Size | Key Finding | Score |
|---|--------------|--------|---|-------------|-------------|-------|

## Safety Data
| # | Author (Year) | Finding | Relevance |
|---|--------------|---------|-----------|

## Community Reports
| Source | Key Quote | Relevance |
|--------|----------|-----------|

## Contrarian Papers
Papers that complicate the story or show null results:
| # | Author (Year) | Finding | How We Handle It |
|---|--------------|---------|-----------------|

Phase 2: Structure the Article (sirsadalot Pattern)

The sirsadalot structure is distinctive. Don't deviate from this template unless the topic demands it:

1. BOTTOM LINE BOX (100 words)
   - Verdict in 30 seconds
   - Who this is for / not for
   - The one thing to remember

2. THE CONTEXT (200-300 words)
   - Why this topic matters right now
   - What everyone gets wrong about it
   - The tension or debate

3. THE MECHANISM (800-1,200 words)
   - Walk through each step with citations
   - Include a mechanism diagram (generated separately)
   - Compare to related compounds/treatments

4. THE EVIDENCE (500-800 words)
   - Human trials with actual numbers
   - Meta-analysis data
   - What the studies actually measured vs what people claim they measured

5. THE PROTOCOLS (300-500 words)
   - Dosing comparison table
   - Route of administration differences
   - Cycling/tolerance management
   - Include a protocol cheat-sheet table

6. THE CAVEATS (200-400 words)
   - What we don't know
   - Who shouldn't do this
   - The bias in the literature

7. FURTHER READING (curated list)
   - 5-10 papers ranked by importance
   - Brief annotation on each

Phase 3: Visual Generation (No LLM Tokens)

Generate all visuals in a single batch call. Never iterate with the LLM on charts.

Visual #1: Mechanism Pathway Diagram

Use architecture-diagram skill for dark-themed SVG pathway maps. Show the molecular cascade from receptor to effect.

For ketamine: NMDAR blockade → disinhibition → glutamate surge → AMPA → Ca²⁺ → BDNF/TrkB → Akt → mTORC1 → synaptic proteins → spine density

Visual #2: Comparison Chart

Use matplotlib (as we did for the ketamine article). Compare treatments on key metrics: onset, response rate, effect size, half-life, cost.

Visual #3: Protocol Table (Markdown)

| Protocol | Dose | Frequency | Duration | Response Rate | Best For |
|----------|------|-----------|----------|---------------|----------|
| IV Clinic | 0.5 mg/kg | 2x/week x 3 weeks, then weekly | 40 min infusion | 65-70% | TRD, acute SI |
| IM Clinic | 0.5-1.0 mg/kg | Weekly | 40 min session | 55-65% | TRD (lower cost) |
| Intranasal Rx | 56-84 mg | 2x/week x 4 weeks, then weekly | Self-admin in clinic | 50-55% | Maintenance |
| Sublingual (grey) | 100-200 mg | Every 3-7 days | 60-90 min hold | Unknown | Self-guided |

Visual #4: Excalidraw Concept Map (Optional)

For complex topics, add an Excalidraw concept map showing how different mechanisms relate. Load the excalidraw skill.

Phase 4: Write the Draft (MiMo V2.5)

Feed the Model:

1. The structured research dossier
2. The sirsadalot structure template
3. The Scott Alexander voice profile from professional-writer

The MiMo V2.5 model is perfectly capable of producing a solid first draft from structured data. It doesn't need to reason deeply — it's slotting research findings into a template structure.

Prompt template:

You are writing a sirsadalot-quality article for Research Collective.
Voice: Scott Alexander. Tone: authoritative, informal, no corporate speak.

Structure: Follow the 7-section template exactly.
Citations: Use @key format, resolve later.
Data: Every claim must have a citation from the dossier.

Research dossier:
[PASTE COMPRESSED RESEARCH]

Write the full article now.

Phase 5: Humanization Pass (DeepSeek V4 Pro)

This is where the quality model earns its cost. Feed it the MiMo draft and apply:

1. Load professional-writer humanization pipeline (Phase 4)
2. Strip all AI tells using humanizer skill patterns
3. Inject burstiness — vary sentence length deliberately
4. Add one slightly controversial opinion
5. Add one "I don't know what to make of this" moment
6. Add personal authority markers ("Having read all 15 papers on this...")
7. Ensure every paragraph passes the information density test

Phase 6: Final Assembly (DeepSeek V4 Pro)

• Resolve all @key citations to full Author (YEAR), Journal format
• Insert visual references at correct positions
• Add the Bottom Line box at the top
• Add Further Reading section
• Format for the target platform (Substack markdown, Reddit markdown, PDF)
• Read aloud mentally — fix any remaining AI rhythm

Quality Gate Checklist

Before calling an article done, verify:

• Every factual claim has a citation with Author (YEAR) and Journal
• At least 10 unique sources cited (sirsadalot minimum: 15)
• Mechanism section walks through each molecular step in order
• At least one contrarian paper is cited that complicates the story
• Dosing table has at least 4 rows comparing protocols
• At least 2 different visual types included
• Bottom Line box at the top — verdict under 100 words
• No AI vocabulary survived (check: "crucial role," "rapidly evolving," "in today's," etc.)
• One sentence somewhere that sounds like it was written at 2am
• Further Reading section with 5-10 annotated papers

Token Cost Comparison

Cost estimates based on OpenRouter pricing. Actual costs vary with prompt caching and context length.

Pre-Flight Checklist

Before starting any article with this skill:

1. /model xiaomi/mimo-v2.5 — set cheap model for phases 1-4
2. Load professional-writer for voice guidelines
3. Load article-production-toolchain for citation pipeline
4. Verify gh CLI is authenticated for GitHub searches
5. Verify matplotlib is installed for chart generation
6. Create project directory: ~/writing/{topic-slug}/

PITFALLS

1. MiMo hallucinates citations enthusiastically. When using MiMo for drafting, it will invent plausible-sounding paper titles and author names. Never trust MiMo citations. All citations MUST come from the verified research dossier, not from the draft. The DeepSeek V4 Pro humanization pass should strip and replace any unverified citation.

2. The quality cliff between models is real. MiMo drafts will read slightly flat. That's by design — the DeepSeek pass fixes it. Don't try to make MiMo's draft "better" with more prompting; that just burns tokens. Let it be mediocre and fix it in Phase 5.

3. Chart generation is fragile. matplotlib will crash on edge cases. Generate charts FIRST (Phase 3), before the MiMo draft, so chart failures don't waste LLM tokens. If a chart fails, fix the Python script and re-run — zero LLM cost.

4. Citation resolution can eat tokens. Resolving @key citations one at a time with LLM calls wastes tokens. Instead, pass the full citation mapping to DeepSeek in one go: @berman2000 = Berman et al. (2000), Biol Psychiatry. DOI: 10.1016/s0006-3223(99)00230-9 etc. One call resolves all.

5. The model switch flag. Between Phase 4 and Phase 5, explicitly switch models with /model deepseek/deepseek-v4-pro. Forgetting this means the humanization pass runs on MiMo, which will make the article worse, not better.