prompt-library
SolidCurated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
Install
Quality Score: 93/100
Skill Content
Details
- Author
- davila7
- Repository
- davila7/claude-code-templates
- Created
- 11 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content โ not just same category
prompt-library
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
prompt-library
A comprehensive collection of battle-tested prompts inspired by [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts) and community best practices.
prompt-craft
Use when writing, tightening, evaluating, or repairing an LLM prompt or reusable prompt template for completion, agent dispatch, grading, structured extraction, tool use, or prompt-engineered workflows. Covers instruction hierarchy, message roles, context placement, few-shot examples, structured output, positive constraints, reasoning guidance, prompt-injection resistance, provider differences, and eval-driven iteration. Do NOT use for whole context-system design (use context-engineering), eval dataset or grader design (use agent-eval-design), reviewing generated code (use code-review), authoring SKILL.md files (use skill-scaffold), choosing which skill or agent should activate (use skill-router), or root-causing a deployed failure after outputs already exist (use debugging).
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs โ generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
prompt-engineer
Expert guidance for writing and optimizing LLM prompts. Use when creating or updating AGENTS.md, CLAUDE.md, SKILL.md, system prompts, or custom instructions.