Imported from th-efool/SKILLS (scout-pro/SKILL.md). Install upstream with npx skills add th-efool/SKILLS --skill scout-pro. Copyright stays with the author.

Scout Pro

Advanced meta-agent that analyzes full conversation context, maps working patterns, recommends multi-skill workflows (not just single skills), and maintains a learning log of what works.

Contents

• references/skill-inventory.md - how to scan the skills directory and the category snapshot
• references/chains.md - chain design principles, notation, library, and custom-chain builder
• references/patterns-and-logging.md - pattern recognition, usage log schema, learning, proactive tips
• references/response-format.md - required response structure, context templates, edge cases, carryover

Workflow

1. Deep context scan. Read the full conversation from start to current message. Identify the primary goal, sub-goals, dependencies, blockers, and past attempts. Check session history in ~/.claude/. Use the context template in references/response-format.md.
2. Inventory skills. Scan the live /Users/gabe/claude-skills/ directory and read each SKILL.md frontmatter. Never recommend a skill without verifying it exists. See references/skill-inventory.md.
3. Design chains. Where the task has multiple steps, design a multi-skill workflow so each step feeds the next. Use the notation, library, and builder protocol in references/chains.md.
4. Recognize patterns. Read ~/.claude/rules/session-context.md and ~/.claude/projects/ memory. Detect recurring tasks, workflow gaps, and underutilized skills. See references/patterns-and-logging.md.
5. Log usage. Read the existing log at ~/.claude/scout-pro-usage-log.json, factor past outcomes into current recommendations, then append the new recommendation. Update entries when the user reports an outcome. See references/patterns-and-logging.md.
6. Recommend proactively. Surface valuable unsolicited suggestions grounded in observed patterns.
7. Respond. Emit the analysis using the structure in references/response-format.md.

Rules

1. Never recommend a skill without verifying it exists; scan the directory first, every run.
2. Always explain the "why" behind a recommendation. Do not just list skills.
3. Prefer chains over individual skills when the task has multiple steps.
4. Respect the user's time. If a one-skill solution works, do not recommend a five-skill chain.
5. Be honest about limitations. If no skill is a great fit, say so.
6. Update the usage log on every recommendation.
7. Do not hallucinate skills. Only recommend skills that exist in the directory or as known slash commands.