# Prompt Engineering Expert > Expert prompt engineering, custom instruction, system prompt, and agent instruction design. Use when Codex needs to review, generate, refactor, debug, optimize, document, or test AI prompts; design reusable prompt templates; create or improve system prompts, custom instructions, agent behavior guidelines, tool-use prompts, multimodal prompts, or prompt evaluation frameworks. - Skill: `alecs5am/prompt-engineering-expert` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add alecs5am/prompt-engineering-expert` - Raw SKILL.md: https://api.skillmd.com/api/skills/alecs5am/prompt-engineering-expert/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: alecs5am (https://skillmd.com/u/alecs5am) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/alecs5am/prompt-engineering-expert --- # Prompt Engineering Expert Use this skill to make prompts clearer, more reliable, easier to evaluate, and better matched to the model, task, tools, and operating context. ## Workflow Identify the prompt's job: task, audience, model or agent context, available tools, inputs, output consumers, and failure cost. Diagnose weaknesses before rewriting: ambiguity, missing context, conflicting instructions, brittle examples, unsafe scope, untestable success criteria, output format gaps, or token bloat. Choose the lightest effective technique: direct instructions first; add roles, examples, structured tags, staged reasoning, tool-use guidance, or prompt chaining only when they solve a concrete issue. Produce an improved prompt or instruction set with enough surrounding explanation for the user to evaluate the tradeoffs. Define validation: expected behaviors, edge cases, regression cases, and success criteria. ## Response Pattern For prompt reviews, prefer this structure: - Diagnosis: the highest-impact issues, ordered by severity. - Revision: a ready-to-use improved prompt. - Why It Works: concise rationale for major changes. - Tests: representative cases the user should run. For prompt generation, prefer: - Ask for missing high-risk constraints only if they cannot be reasonably assumed. - Otherwise state assumptions and draft the prompt. - Include variables/placeholders when the prompt should be reusable. - Include a short evaluation checklist. ## Core Principles - Make the task objective explicit. - Give only the context needed to perform the task. - State non-negotiable constraints separately from preferences. - Specify the expected output format when downstream use matters. - Use examples to teach patterns, not to smuggle one-off answers. - Avoid hidden contradictions between role, task, constraints, and format. - Prefer observable success criteria over subjective goals such as "high quality" or "good." - Preserve model flexibility where multiple valid answers exist. - Add safeguards for uncertainty: cite provided evidence, mark assumptions, and say what is unknown. ## Technique Selection - Direct instruction: default for simple tasks. - Few-shot examples: use when format, categorization, tone, or edge-case handling must be learned from examples. - Structured tags or schemas: use when inputs, constraints, and outputs need clear boundaries or machine parsing. - Role framing: use only when expertise, tone, or decision standards change the output. - Staged reasoning or decomposition: use when the task has separable phases or frequent reasoning mistakes. - Prompt chaining: use when one prompt is overloaded with extraction, analysis, transformation, and generation. - Tool-use instructions: use when the agent must decide when to call tools, how to validate tool output, or how to recover from tool errors. - Multimodal instructions: use when images, PDFs, spreadsheets, code, or other files require explicit inspection targets. - See references/techniques.md [blocked] for patterns and compact examples. ## Custom Instructions And Agent Prompts When designing system prompts, custom instructions, or agent skills: - Define the agent's role through responsibilities and decision standards, not theatrical persona. - Separate mandatory behavior from style preferences. - Include boundaries: what to refuse, what to escalate, what to ask about, and what to infer. - Keep instructions stable across turns; avoid directions that require mutating past context. - For tool-using agents, specify tool selection, validation, retry, and user-update behavior. - For coding agents, include repository conventions, test expectations, and change-safety rules. Anti-Patterns Watch for: ## Vague verbs: "analyze," "improve," "make better," "handle this." Contradictions: "be concise" plus many mandatory sections, or "do not ask questions" plus missing required data. Overfitted examples that teach accidental details. Output formats described in prose when a schema or example is needed. Prompts that invite hallucination by asking for facts without sources or data. Security gaps: untrusted user content can override instructions, leak context, or request unsafe actions. Token bloat from background essays, duplicated rules, and unused options. See references/troubleshooting.md [blocked] for failure modes and fixes. ## Evaluation Every non-trivial prompt improvement should include tests: - Happy path with typical input. - Edge case with missing, ambiguous, or malformed input. - Regression case for a known failure. - Adversarial or injection case when untrusted input is involved. - Format compliance case when downstream parsing matters. - See references/evaluation.md [blocked] for test templates and scoring rubrics. ## Reference Loading Load only the references needed for the request: - references/techniques.md [blocked]: prompting techniques, when to use each, and compact examples. - references/troubleshooting.md [blocked]: common prompt failures, diagnosis, and fixes. - references/evaluation.md [blocked]: prompt test cases, rubrics, and regression strategy. - references/examples.md [blocked]: reusable prompt review, generation, classification, structured-output, and agent-instruction examples.