From core
Broadly and deeply analyze user intent (avoiding XY problems) and evaluate multiple solution approaches (default 5) with scores from 0 to 100.
How this skill is triggered — by the user, by Claude, or both
Slash command
/core:problem-solvingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill enables a systematic and thorough evaluation of potential solutions for a given issue. It goes beyond the stated problem to identify the user's true underlying intent, avoiding the "XY Problem" (asking for a solution to an intermediate step rather than the root goal). It ensures that multiple perspectives are considered and that the final recommendation is backed by a structured scor...
This skill enables a systematic and thorough evaluation of potential solutions for a given issue. It goes beyond the stated problem to identify the user's true underlying intent, avoiding the "XY Problem" (asking for a solution to an intermediate step rather than the root goal). It ensures that multiple perspectives are considered and that the final recommendation is backed by a structured scoring process.
assets/templates/analysis-report.md template to present your findings.
Input: "We need to migrate our legacy monolith to microservices. Analyze the approaches." Output: A report identifying the intent (e.g., "Improve scalability and deployment speed"). The XY check might note that microservices are a means, not the end. Approaches might include "Modular Monolith" or "Serverless" alongside traditional microservices.
Input: "How do I fix this regex for parsing nested HTML tags in my custom scraper?" Output: A report identifying the Stated Problem (Regex fix) and the Underlying Intent (Extracting data from HTML). The XY check would note that regex is unsuitable for nested HTML. Approaches would include "Use BeautifulSoup/Cheerio", "Use a dedicated HTML parser library", etc., scoring them much higher than the "Fix Regex" approach.
npx claudepluginhub yu-iskw/agent-heroes --plugin corePresents structured options with trade-offs when users ask for alternatives, need help deciding between approaches, or are uncertain about the best path forward.
Explores solution spaces using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, and detecting when to abandon a strategy. Use when multiple approaches exist with no clear winner or when debugging with no obvious root cause.