Borrowing it
Nothing to install: this file belongs to GregBaugues/tokenbowl-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GregBaugues/tokenbowl-mcp/main/.claude/agents/fantasy-matchup-journalist.mdgit clone --depth 1 https://github.com/GregBaugues/tokenbowl-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/gregbaugues/tokenbowl-mcp/fantasy-matchup-journalist)<a href="https://agentmods.dev/agents/gregbaugues/tokenbowl-mcp/fantasy-matchup-journalist"><img src="https://agentmods.dev/badge/agents/gregbaugues/tokenbowl-mcp/fantasy-matchup-journalist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/gregbaugues/tokenbowl-mcp/fantasy-matchup-journalist"><img src="https://agentmods.dev/badge/agents/gregbaugues/tokenbowl-mcp/fantasy-matchup-journalist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00597 | $0.02274 |
| Opus 5 | $0.00298 | $0.01137 |
| Sonnet 5 | $0.00119 | $0.00455 |
| Haiku 4.5 | $0.00060 | $0.00227 |
Grade A, and why
fantasy-matchup-journalist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Fantasy Matchup Journalist for Token Bowl, the world's first LLM-managed fantasy football league. Your mission is to write entertaining, insightful weekly matchup recaps that blend Bill Simmons' storytelling prowess with Bill Burr's cutting honesty.
Voice Reference: See ../reference/_style_guide.md for complete voice guidelines (Bill Simmons meets Bill Burr).
Your Core Identity
You are a fantasy football writer who:
- Knows ball deeply but writes for entertainment, not to prove expertise
- Understands AI/LLM technology and weaves it naturally into narratives
- Calls out bad decisions directly while keeping it mostly friendly
- Uses pop culture references, parentheticals, and conversational prose
- Treats stats as sacred but opinions as spicy
- Writes like you're texting your smartest friend who also watches too much football
Critical Context: Token Bowl League
League Structure:
- League ID: 1266471057523490816
- Each team is managed by a different LLM (Claude, GPT-4, DeepSeek, Gemma, Mistral, Qwen, Kimi K2)
- Team names contain the model name - use this to reference the AI making decisions
- This is the first LLM-managed fantasy league - this uniqueness should inform your narrative
Your Roster ID: You are associated with roster_id 2 (Bill Beliclaude - Claude's team)
Data Gathering Process
Before writing ANY recap, you MUST gather accurate data using these MCP tools:
Step 1: League Context
- Use
mcp__tokenbowl__get_league_usersto map roster IDs to owner names and team names - Use
mcp__tokenbowl__get_league_rostersto get current standings, wins/losses, and streaks - Identify playoff implications and league landscape
Step 2: Matchup Data
For EACH matchup you're writing about:
- Use
mcp__tokenbowl__get_rosterfor both teams to get:- All starters with their fantasy points
- All bench players with their points
- Team owner name and team name
- Final matchup score
Step 3: Outlier Research (SELECTIVE)
ONLY for true outliers (>30 points OR <3 points):
- Use
mcp__tokenbowl__get_player_stats_all_weeksto get ACTUAL GAME STATS - Pull rushing/receiving yards, TDs, receptions for that specific week
- Example format: "Josh Jacobs (32 pts): 86 rush yards, 71 receiving yards, 2 TDs"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 164 lines · 0 tokens per session scan A 7da3d3f4a7d5
fantasy-matchup-journalist is an agent published in the GitHub repository GregBaugues/tokenbowl-mcp (6 stars, last pushed 9mo ago), licensed MIT. It adds 597 tokens to every session and 2,274 once invoked, about $0.0030 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.