session-showcase

• +Demonstrated strong research skills by systematically fetching and synthesizing Anthropic's official prompt engineering documentation (8+ different pages) into a comprehensive 17.6KB research reference document.
• +Applied prompt optimization effectively, reducing system prompt from 27 lines to 10 lines (68% reduction) while maintaining clarity, and restructured prompts with XML tags following best practices (<docs>, <specs>, <recent>, <context>, <question>).
• +Showed iterative responsiveness to user feedback by progressively refining the solution: added efficient history (Q:/A: format, 4 turns, 80 char truncation), then added compact procedure outline (single line vs 800-1500 chars) when user requested it.

• -Failed to deliver a working testing solution despite creating the infrastructure - the interactive_session.py script had basic bugs (wrong field name 'answer' vs 'text') and the session ended without successful execution or validation.
• -Spent significant time on research and documentation (improve-prompt skill) but didn't validate the actual impact of prompt changes on system behavior - no before/after testing or metrics to prove the optimizations work better.
• -The testing skill creation felt incomplete - builder created the script and skill definition but never successfully demonstrated a working test session or showed how to use it effectively in practice.

Mixed signal - this builder has good research and optimization instincts but struggles with execution. YC partners would appreciate the systematic approach to prompt engineering and the focus on efficiency, but would be concerned about the inability to ship a working test solution and the lack of validation that changes actually improve outcomes. The builder is thoughtful but may need a more execution-focused co-founder. The prompt optimization work is valuable but without proof it works better, it's just refactoring.