By name: prompt-engineering
description: 'Create, update, review, or discuss an LLM prompt — system prompt, skill, or agent. State the goal, trust the model, add only what closes a real gap in natural behavior. Use when writing or improving prompts, discussing a skill or agent, diagnosing prompt failures, or when the user says a prompt needs work.'
argument-hint: ''
user-invocable: true
A prompt earns its place where natural model behavior misses what's needed. State the goal and the expected outcome; trust the model for everything else. Lines belong only when they close a real gap — observed gotchas, non-obvious behavior, knowledge the model doesn't have, or edge cases it gets wrong by default. Each line must also hold at the edges of where the prompt runs: name principles and portable capabilities in natural language, not harness-bound primitives; scope rules to the principle's natural reach, not narrower; unify split restatements of the same rule. Length follows the gap, not a number. On update, calibrate both directions: add what closes the new gap, and prune what no longer earns its place. A prompt stays in balance over time; it doesn't accrete.
Branch on intent. Creating: discover the goal, audience, and the specific gap; draft the minimum that closes it. Updating: find each existing line's gap before changing anything around it — patches that replace often beat patches that add. Reviewing: of each line ask both "would the model do this without it?" and "does this hold at the edges?" — flag the no's. Diagnosing a failing prompt: see references/metaprompting.md — find the line driving the symptom before patching.
References load on demand. Load only the ones whose trigger fires:
references/system-prompts.md— when writing a system prompt that ships in a deployment loop and warrants section structure (real degradation paths, real constraints, real stop conditions).references/skills.md— when writing a skill (anything in aSKILL.md+references/folder layout that activates a behavior).references/knowledge-skills.md— when writing a skill whose gap is data the model lacks rather than behavior it gets wrong (API references, schema lookups, internal conventions).references/agents.md— when writing an agent (anything that runs in isolation with its own declared tool set). Default to a skill over an agent unless you need harness-specific frontmatter (restricted tool allow-list, isolated model/subagent type).references/patterns.md— when filling a non-trivial section in any prompt type and a known technique fits the gap (verification, narrate-execute-confirm, tool-call escalation, output contracts, ambiguity handling, high-risk self-check, decision rules over absolutes, emotional tone).references/review.md— when reviewing or updating an existing prompt.references/metaprompting.md— when diagnosing a failing prompt against logged traces.