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/commands/wrapup.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/commands/gregbaugues/tokenbowl-mcp/wrapup)<a href="https://agentmods.dev/commands/gregbaugues/tokenbowl-mcp/wrapup"><img src="https://agentmods.dev/badge/commands/gregbaugues/tokenbowl-mcp/wrapup.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01124 |
| Opus 5 | $0.00000 | $0.00562 |
| Sonnet 5 | $0.00000 | $0.00225 |
| Haiku 4.5 | $0.00000 | $0.00112 |
Grade A, and why
wrapup 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 8d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Bowl Weekly Slop-Up
Generate the weekly fantasy football recap for Token Bowl, the first LLM-managed fantasy football league.
League Context: Each team is managed by a different LLM (Claude, GPT-4, DeepSeek, Gemma, Mistral, Qwen, Kimi K2). Team names contain the model name.
Voice: Bill Simmons meets Bill Burr - storytelling with cutting honesty.
Reference: See .claude/reference/_style_guide.md for complete voice guidelines.
Goal
Create entertaining, insightful matchup recaps that:
- Tell compelling stories about each matchup
- Highlight outlier performances with game stats
- Weave in AI/model personality naturally
- Balance humor with technical accuracy
- Build running narratives across weeks
Execution Phases
Phase 1: League Context
get_league_info() # Season, week, league status
get_league_users() # Map roster IDs to team names
get_league_rosters() # Current standings, win/loss records
Phase 2: Matchup Data
get_league_matchups(week) # All matchups, scores, player points
Phase 3: Assign Matchup Journalists
For EACH matchup, use the fantasy-matchup-journalist subagent to write the recap.
Run these subagents in parallel to maximize efficiency.
Each journalist will:
- Gather detailed roster data for both teams
- Research outliers (>30 or <3 points) for game stats
- Investigate injuries for low-scoring players
- Write 4-6 line recap in Simmons/Burr style
Phase 4: Edit & Assemble
Once all matchup recaps returned:
- Edit for cohesion: Light edits to avoid templated language
- Remove repetition: Each matchup should feel unique
- Vary sentence structure: No two matchups should open the same way
- Maintain voice: Keep Simmons/Burr balance throughout
Phase 5: Add Context Sections
Opening Hook (3-5 sentences):
- Start with "What if I told you..." or "There are three levels..." setup
- Build to the week's biggest moment
- Use pop culture reference if it fits
- Make it conversational
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.
- 8d ago First seen · 137 lines · 0 tokens per session scan A 23ee28b475f9
wrapup is a command published in the GitHub repository GregBaugues/tokenbowl-mcp (6 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,124 tokens. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.