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/trade.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/trade)<a href="https://agentmods.dev/commands/gregbaugues/tokenbowl-mcp/trade"><img src="https://agentmods.dev/badge/commands/gregbaugues/tokenbowl-mcp/trade/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/commands/gregbaugues/tokenbowl-mcp/trade"><img src="https://agentmods.dev/badge/commands/gregbaugues/tokenbowl-mcp/trade.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 | $0.00000 | $0.01247 |
| Opus 5 | $0.00000 | $0.00624 |
| Sonnet 5 | $0.00000 | $0.00249 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
trade 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 9d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Analysis & Proposal Generator
Analyze trade opportunities for Bill Beliclaude (Roster ID 2) in the Token Bowl league.
Reference: See .claude/reference/_fantasy_framework.md for evaluation criteria.
Goal
Identify and execute value-creating trades by:
- Analyzing our roster needs and trade assets
- Profiling opponents for exploitable weaknesses
- Constructing 60/40 trades (appear fair, favor us)
- Timing proposals for maximum psychological impact
- Maximizing championship odds through roster optimization
Untouchables: Josh Allen and James Cook are not available for trade.
Execution Phases
Phase 1: Assess Our Roster
Use the roster-analyst subagent to:
- Identify positions with critical needs (0-1 bench depth)
- Identify positions with tradeable surplus (3+ bench depth)
- Evaluate which starters are underperforming
- Determine our expendable assets (bench depth, aging veterans)
Trade Asset Identification:
- Players at overstocked positions (3+ bench)
- Underperformers with name value (sell high on reputation)
- High-floor veterans we can package for high-ceiling players
- Players with unsustainable TD rates (sell before regression)
Phase 2: Scout Opponents
Use the opponent-scout subagent to:
- Analyze all 9 opposing rosters for weaknesses and strengths
- Identify teams with complementary needs (they need what we have)
- Profile vulnerable managers (losing streaks, injuries, bye week crunches)
- Find exploitable psychological angles
Target Identification:
- Teams with 0-1 bench at positions where we have surplus
- Teams dealing with recent injuries or bye week hell
- Managers panicking (0-3, 1-4 starts) who overpay
- Teams with talent stuck on bench (want to "unlock" value)
Phase 3: Research Trade Targets
For top trade target candidates, use the player-researcher subagent to:
- Verify current production is sustainable (or inflated)
- Analyze playoff schedule (weeks 15-17) strength
- Check for hidden injury concerns or usage red flags
- Compare expert ROS projections vs market perception
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.
- 9d ago First seen · 157 lines · 0 tokens per session scan A 6d9e2aba1ac7
trade 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 1,247 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.
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ui-flow-review
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