goals
✓Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space.
Installation
SKILL.md
Process goals (controllable intermediate actions) provide dense feedback signals; outcome goals (end-result demands) provide sparse, delayed feedback. This asymmetry explains why behavioral prompting dominates direct output demands.
| Type | Effect Size | Prompt Analog | Signal Density | Failure Mode |
| Outcome | d=0.09 | "Give the correct answer" | Sparse | Hallucination, reward hacking | | Performance | d=0.44 | "Achieve high accuracy" | Proxy | Goodhart's Law misalignment | | Process | d=1.36 | "Think step-by-step" | Dense | Over-specification (rare) |
Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space. Source: zpankz/mcp-skillset.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/zpankz/mcp-skillset --skill goals- Source
- zpankz/mcp-skillset
- Category
- </>Dev Tools
- Verified
- ✓
- First Seen
- 2026-02-01
- Updated
- 2026-02-18
Quick answers
What is goals?
Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space. Source: zpankz/mcp-skillset.
How do I install goals?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/zpankz/mcp-skillset --skill goals Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/zpankz/mcp-skillset
Details
- Category
- </>Dev Tools
- Source
- skills.sh
- First Seen
- 2026-02-01