prompting

name: prompting description: | Guide for writing effective system prompts for LLM agents. Use when creating or editing system prompts for applications, agent configurations, or development tools.

LLMs are intelligent by default. System prompts set direction and impose constraints, not explain reasoning.

Start minimal. Observe failures. Add targeted fixes. Every instruction must justify its token cost by solving a real problem.

Do not explain existing capabilities, list obvious practices, add preventive instructions, or repeat information.

Use markdown sections and paragraphs. Each section describes one behavior or constraint.

State what to do or avoid. Explain why if non-obvious. Show correct behavior with examples.

Headings up to level 3. Plain paragraphs. No bold, italics, or emojis. Code blocks for commands. Lists only for distinct enumerable items.

Wrap examples in <example> tags with user/assistant prefixes. One pair per tag.

<example>
user: What's the capital of France?
assistant: Paris
</example>

Use brackets for tool actions instead of showing invocations:

<example>
user: Find all TODO comments
assistant: [searches codebase]
Found 3 TODOs: ...
</example>

Behaviors the model gets wrong by default. Domain constraints. Output format requirements. Safety boundaries. Tool integrations.

Reasoning instructions. Problem-solving approaches. Common sense behaviors. Ethical guidelines. Capability descriptions.

Start minimal. Test with real inputs. Identify failures. Add targeted fixes. Remove unnecessary instructions.

Track which instructions prevent which failures. If you cannot identify the specific problem an instruction solves, remove it.

Consult references/ for model-specific patterns:

references/claude.md - XML structure, countering sycophancy, trigger words, parallel execution
references/gpt.md - Contradiction sensitivity, role hierarchy, verbosity control, metaprompting
references/gemini.md - Conciseness, tool explanations, library checks, context placement
references/codex.md - OpenAI Codex models, tool implementations, autonomy patterns, compaction