Competing

Improve systematically by analyzing losses, studying winners, and tracking progress against rivals in any competitive domain.

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Install skill "Competing" with this command: npx skills add ivangdavila/competing

Core Framework

Competition is a learning accelerator. Every loss contains the lesson that wins don't.

  1. Analyze the Loss — Don't just lose, understand WHY
  2. Study the Winner — What did they do that you didn't?
  3. Track the Delta — Measure the gap, watch it shrink
  4. Iterate — Apply lessons, compete again, repeat

The Post-Loss Protocol

After any competitive loss, extract value:

QuestionPurpose
At what moment did the outcome shift?Find the decision point
What did they do that I didn't?Identify the winning move
What would I do differently?Formulate the lesson
Is this a pattern?Check history for repeats

Don't rationalize. Don't blame externals. Extract the actionable insight.


Tracking (What to Measure)

Create a tracking folder in the user's workspace:

~/competing/
├── domains/           # Per-domain tracking
├── rivals.md          # Opponent profiles
├── log.md             # Win/loss log with lessons
└── progress.md        # Metrics over time

For each domain, track:

  • Win/loss record with dates
  • Specific losses analyzed (who, why, lesson)
  • Patterns identified (recurring weaknesses)
  • Progress metrics (are lessons translating to wins?)

Rival Intelligence

Know your competition:

  • Profile rivals — Their strengths, weaknesses, tendencies
  • Monitor changes — When they improve or change strategy
  • Find their edge — What specifically makes them beat you?
  • Study up — Find examples of them losing, analyze what worked

Quick Reference

SituationAction
Just lostRun post-loss protocol, add to log
Pattern emergingDocument it, create drill/fix
Preparing for known rivalReview their profile, past matches
Plateau in progressAnalyze recent losses for new patterns
Won against usual winnerDocument what changed, replicate

Load Reference

NeedFile
Domain-specific strategiesdomains.md
Deep loss analysis frameworkanalysis.md
Progress tracking templatestracking.md
Feedback loop mechanicsfeedback.md

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