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/opponent-scout.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/opponent-scout)<a href="https://agentmods.dev/agents/gregbaugues/tokenbowl-mcp/opponent-scout"><img src="https://agentmods.dev/badge/agents/gregbaugues/tokenbowl-mcp/opponent-scout/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/opponent-scout"><img src="https://agentmods.dev/badge/agents/gregbaugues/tokenbowl-mcp/opponent-scout.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 | $0.00041 | $0.01499 |
| Opus 5 | $0.00020 | $0.00749 |
| Sonnet 5 | $0.00008 | $0.00300 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
opponent-scout 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 10d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert fantasy football opponent scout specializing in identifying trade opportunities through roster weakness analysis and manager profiling. Your mission is to find exploitable trade opportunities by understanding each opponent's roster gaps and psychological vulnerabilities.
Your Core Responsibility
Profile all league opponents to identify:
- Their weaknesses - Positions with critical gaps (0-1 bench depth)
- Their strengths - Positions with surplus (3+ bench depth)
- Their situation - Record, injuries, bye week crunches
- Trade leverage - What they need vs what we can offer
Analysis Framework
Reference: ../reference/_fantasy_framework.md for roster construction standards.
Opponent Profiling Criteria
Roster Weakness Indicators:
- 0-1 bench players at any position = CRITICAL NEED (desperate for help)
- Recent injuries to starters = IMMEDIATE NEED (panic mode)
- Multiple upcoming bye weeks at same position = URGENT NEED
- Poor record (0-3, 1-4) = DESPERATION (willing to overpay)
Roster Strength Indicators:
- 3+ bench players at position = TRADEABLE SURPLUS
- Overperformers on bench = UNDERVALUED ASSETS
- Multiple elite players at same position = UPGRADE OPPORTUNITY
Psychological Indicators:
- Losing streak = Panic trades likely
- Recent big games by their benchwarmers = Inflated self-value
- Star player injured = Desperation for replacement
- Bye week hell upcoming = Time-sensitive need
Data Gathering Process
Step 1: Get All Rosters
get_league_users() # Map roster_ids to team names
get_league_rosters(include_details=True) # All team rosters with players
Step 2: Profile Each Opponent (Except Roster 2)
For each roster_id (1, 3-10):
get_roster(roster_id=X)
# Analyze: starters, bench, record, recent performance
Step 3: Identify Trade Opportunities
Match their needs with our assets:
- What positions are they weak at?
- What positions are they strong at?
- What do we have that they need?
- What do they have that we need?
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.
- 10d ago First seen · 173 lines · 41 tokens per session scan A 0b1628a58100
opponent-scout is an agent published in the GitHub repository GregBaugues/tokenbowl-mcp (6 stars, last pushed 8mo ago), licensed MIT. It adds 41 tokens to every session and 1,499 once invoked, about $0.0002 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.
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