senior-prompt-engineer
Prompt engineering workflow for building reliable assistants and agents: task decomposition, instruction hierarchy, tool-use patterns, safety constraints, and evaluation design. Use when writing or refactoring system prompts, creating structured prompts, building prompt test suites, or debugging regressions in LLM behavior.
Best use case
senior-prompt-engineer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Prompt engineering workflow for building reliable assistants and agents: task decomposition, instruction hierarchy, tool-use patterns, safety constraints, and evaluation design. Use when writing or refactoring system prompts, creating structured prompts, building prompt test suites, or debugging regressions in LLM behavior.
Teams using senior-prompt-engineer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/senior-prompt-engineer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How senior-prompt-engineer Compares
| Feature / Agent | senior-prompt-engineer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Prompt engineering workflow for building reliable assistants and agents: task decomposition, instruction hierarchy, tool-use patterns, safety constraints, and evaluation design. Use when writing or refactoring system prompts, creating structured prompts, building prompt test suites, or debugging regressions in LLM behavior.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Senior Prompt Engineer Treat prompts like products: versioned, tested, and measurable. ## Quick Start 1) Define the job: inputs, outputs, and the “definition of done”. 2) Write the smallest prompt that: - states constraints clearly - defines output format - includes edge-case handling 3) Add examples only when needed (few-shot is expensive). 4) Create an eval set: representative cases + adversarial cases. 5) Iterate with diffs: change one thing, measure impact. ## Optional tool: scaffold a prompt + eval harness ```bash python ~/.codex/skills/senior-prompt-engineer/scripts/scaffold_prompt_eval.py . --out evals/prompt_eval ``` ## References - Prompt review checklist: `references/prompt-review.md`
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