trade-analyzer

star 3

Analyzes trade proposals when the user considers trading players, asks 'should I trade X for Y', evaluates a trade offer, or wants to find trade targets. Compares players' stats, evaluates H2H category impact, and considers roster fit. Triggers on: 'trade', 'swap', 'deal', 'give X for Y', 'trade target', 'should I accept', 'trade offer'.

garavitgabriel By garavitgabriel schedule Updated 4/4/2026

name: trade-analyzer description: "Analyzes trade proposals when the user considers trading players, asks 'should I trade X for Y', evaluates a trade offer, or wants to find trade targets. Compares players' stats, evaluates H2H category impact, and considers roster fit. Triggers on: 'trade', 'swap', 'deal', 'give X for Y', 'trade target', 'should I accept', 'trade offer'."

Trade Analyzer

When the user asks about a trade or is evaluating a trade proposal, follow these steps:

Step 1: Understand the Categories

Call get_scoring_categories to know all 14 H2H categories and their direction. This is essential — a trade that looks bad in total points might be great for category balance.

Step 2: Run the Trade Analysis

Call analyze_trade with the players being given and received (comma-separated names). This gives the baseline stat comparison.

Step 3: Deep Player Evaluation

Call get_player_info for each player involved in the trade to get detailed season stats, injury status, and ownership trends.

Step 4: Assess Roster Fit

Call get_my_roster to understand:

  • What positions are covered after the trade?
  • Does the trade create a positional gap?
  • Are there bench players who can fill in?

Step 5: Check Memory Context (if available)

If memory tools are available:

  • Call get_category_trends to see which categories have been consistently strong or weak
  • Call get_matchup_history to see if certain categories have been costing you matchups
  • A trade that hurts a strong category but helps a weak one is often worth it — even if the "point value" goes down

Step 6: Category Impact Analysis

Build a category-by-category impact table:

Category Direction Current Strength Impact Net Effect
HR Higher wins STRONG Lose 5 HR, Gain 2 HR -3 HR (slight decline)
SB Higher wins WEAK Lose 0 SB, Gain 15 SB +15 SB (big improvement)
ERA Lower wins AVERAGE ... ...

For each category:

  • Will this trade help, hurt, or be neutral?
  • Is the affected category one you're already strong/weak in?
  • Could this flip a category from losing to winning in typical matchups?

Critical reminders for reverse categories:

  • B_SO (lower wins): A player with fewer strikeouts is BETTER
  • ERA (lower wins): Lower ERA is BETTER
  • WHIP (lower wins): Lower WHIP is BETTER
  • L (lower wins): Fewer losses is BETTER

Step 7: Verdict

Give a clear recommendation:

  • Accept / Decline / Counter-offer
  • How many categories does this trade help vs hurt?
  • What's the net category swing? (e.g., "You gain an edge in 3 categories but lose ground in 1")
  • Is the positional fit workable?
  • Any injury risks to flag?

Step 8: Save to Memory (if available)

If the user completes a trade:

  • Call save_roster_move with action="TRADE_SEND" for each player given away
  • Call save_roster_move with action="TRADE_RECEIVE" for each player received
  • Update save_category_trend if the trade significantly changes category projections
Install via CLI
npx skills add https://github.com/garavitgabriel/espn-fantasy-claude-openclaw --skill trade-analyzer
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