# Monthly Bouts Index Production

> Produce the Bouts AI Agent Index monthly report including the full 5-section structure (leaderboard, model family, challenge family, failure archetypes, industry implications), production timeline, and distribution checklist. Use every month to create the flagship authority content piece that drives media coverage, lab outreach, and enterprise sales conversations.

- Skill: `nickgallick/monthly-bouts-index-production` (Agent Skill)
- Install (CLI): `npx skillmds add nickgallick/monthly-bouts-index-production`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nickgallick/monthly-bouts-index-production/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: nickgallick (https://skillmd.com/u/nickgallick)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/nickgallick/monthly-bouts-index-production

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# Monthly Bouts Index Production

## Report structure
```
THE BOUTS AI AGENT INDEX — [Month] 2026

EXECUTIVE SUMMARY
[3-4 sentences: top findings, biggest movers, key trends]

SECTION 1: LEADERBOARD STATE
- Top 20 agents with ELO, weight class, model family
- Biggest movers (up and down)
- New entrants of note

SECTION 2: MODEL FAMILY PERFORMANCE
- Claude vs GPT vs Gemini vs open-source
- Per-judge dimension comparison (radar chart)
- Trend vs last month
- Statistical significance notes

SECTION 3: CHALLENGE FAMILY ANALYSIS
- Performance by family (solve rate, avg score, CDI)
- Trending families
- New families introduced / retired

SECTION 4: FAILURE ARCHETYPE REPORT
- Distribution of 15 archetypes
- Trending archetypes (increasing/decreasing)
- Model-family-specific patterns
- New archetypes if discovered

SECTION 5: INDUSTRY IMPLICATIONS
- What this data means for AI agent deployment
- Where the frontier is improving
- Where gaps remain
- Predictions for next month

METHODOLOGY NOTE
[Brief scoring description]
[Link to full judging transparency page]

DATA NOTE
Based on [N] challenge completions across [N] agents in [Month] 2026.
```

## Production timeline
- Month end → 3 days to compile data
- Day 4-5: draft report
- Day 6: review (check for accuracy, legal scan for claims)
- Day 7: publish

## Distribution checklist
- [ ] Full PDF report → email to subscribers + blog post
- [ ] Executive summary → LinkedIn + X thread
- [ ] Key charts → individual social posts (spread over the month)
- [ ] Press release → AI journalists with 24h embargo for exclusive
- [ ] Lab-specific sections → send directly to relevant labs

## What makes a good monthly report
- Specific numbers in every section (no vague "models improved")
- At least one surprising finding (this drives press and social sharing)
- At least one actionable insight for builders
- Honest methodology note (this builds trust over time)

## The exclusive play
Before publishing, offer one journalist early access (24h embargo):
"We're publishing our March AI Agent Index tomorrow. The most surprising finding: [X]. Want first access for your newsletter?"

This builds media relationships and ensures coverage each month.