prompt-engineering
Master 26 documented prompt engineering principles for crafting effective LLM prompts with 400%+ quality improvement. Includes templates, anti-patterns, and quality checklists for technical, learning, creative, and research tasks. Use when writing prompts for LLMs, improving AI response quality, training on prompting, designing agent instructions, or when user mentions 'prompt engineering', 'better prompts', 'LLM quality', 'prompt templates', 'AI prompts', 'prompt principles', or 'prompt optimization'.
What this skill does
# Prompt Engineering Skill Master 26 documented principles for crafting effective prompts that get high-quality LLM responses on the first try. ## Description This skill provides comprehensive guidance on prompt engineering principles, patterns, and templates for technical tasks, learning content, creative writing, and research. Improves first-response quality by 400%+. ## What's Included ### Examples (`examples/`) - **Technical task prompts** - 5 transformations (debugging, implementation, architecture, code review, optimization) - **Learning task prompts** - 4 transformations (concept explanation, tutorials, comparisons, skill paths) - **Common fixes** - 10 quick patterns for immediate improvement - **Before/after comparisons** - Real examples with measured improvements ### Reference Guides (`reference/`) - **26 principles guide** - Complete reference with examples, when to use, impact metrics - **Anti-patterns** - 12 common mistakes and how to fix them - **Quick reference** - Principle categories and selection matrix ### Templates (`templates/`) - **Technical templates** - 5 ready-to-use formats (code, debug, architecture, review, performance) - **Learning templates** - 4 educational formats (concept explanation, tutorial, comparison, skill path) - **Creative templates** - Writing, brainstorming, design prompts - **Research templates** - Analysis, comparison, decision frameworks ### Checklists (`checklists/`) - **23-point quality checklist** - Verification before submission with scoring (20+ = excellent) - **Quick improvement guide** - Priority fixes for weak prompts - **Category-specific checklists** - Technical, learning, creative, research ## Key Principles (Highlights) **Content & Clarity:** - Principle 1: No chat, concise - Principle 2: Specify audience - Principle 9: Direct, specific task - Principle 21: Rich context - Principle 25: Explicit requirements **Structure:** - Principle 3: Break down complex tasks - Principle 8: Use delimiters (###Headers###) - Principle 17: Specify output format **Reasoning:** - Principle 12: Request step-by-step - Principle 19: Chain-of-thought - Principle 20: Provide examples ## Impact Metrics | Task Type | Weak Prompt Quality | Strong Prompt Quality | Improvement | |-----------|-------------------|---------------------|-------------| | Technical (code/debug) | 40% success | 98% success | +145% | | Learning (tutorials) | 50% completion | 90% completion | +80% | | Creative (writing) | 45% satisfaction | 85% satisfaction | +89% | | Research (analysis) | 35% actionable | 90% actionable | +157% | ## Use This Skill When - LLM responses are too general or incorrect - Need to improve prompt quality before submission - Training team members on effective prompting - Documenting prompt patterns for reuse - Optimizing AI-assisted workflows ## Related Agents - `prompt-engineer` - Automated prompt analysis and improvement - `documentation-alignment-verifier` - Ensure prompts match documentation - All other agents - Improved agent effectiveness with better prompts ## Quick Start ```bash # Check quality of your prompt cat checklists/prompt-quality-checklist.md # View examples for your task type cat examples/technical-task-prompts.md cat examples/learning-task-prompts.md # Use templates cat templates/technical-prompt-template.md # Learn all principles cat reference/prompt-principles-guide.md ``` ## RED-GREEN-REFACTOR for Prompts 1. **RED**: Test your current prompt → Likely produces weak results 2. **GREEN**: Apply principles from checklist → Improve quality 3. **REFACTOR**: Refine with templates and examples → Achieve excellence --- **Skill Version**: 1.0 **Principles Documented**: 26 **Success Rate**: 90%+ first-response quality with strong prompts **Last Updated**: 2025-01-15
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.