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Agent skill

Senior Prompt Engineer

by borghei in borghei/Claude-Skills

Prompt engineering and LLM evaluation. An agent skill from borghei/Claude-Skills.

MITAuto-check passedAI & LLM Engineering

Install Senior Prompt Engineer

skills CLI
$ npx skills add borghei/Claude-Skills --skill senior-prompt-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills senior-prompt-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/senior-prompt-engineer .claude/skills/senior-prompt-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
senior-prompt-engineer
GitHub stars
886
Token cost
~1.5k tokens
SKILL.md length
560 words
Files
8 (incl. scripts, references)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

Prompt engineering and LLM evaluation. An agent skill from borghei/Claude-Skills.

  • Optimizing prompts
  • SKILL.md covers Core Capabilities, When to Use, Tools and References, plus 2 more sections
  • Runs Python scripts from its folder; calls python
  • Designing prompt templates

What it does

Senior Prompt Engineer is an agent skill from borghei/Claude-Skills. Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/agentic_system_design.md`, `references/llm_evaluation_frameworks.md` and `references/prompt_engineering_patterns.md`).

It sits in AI & LLM Engineering, covering Prompt engineering. It works with Python. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Optimizing prompts
  • Designing prompt templates
  • Evaluating LLM outputs
  • Building agentic systems

Example prompts

  • “/senior-prompt-engineer”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Senior Prompt Engineer loads about 1.5k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 560 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 560 words, ~1,489 tokens.

Download SKILL.mdSave it as .claude/skills/senior-prompt-engineer/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
senior-prompt-engineer
description
Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.
license
MIT + Commons Clause
metadata.version
1.1.0
metadata.author
borghei
metadata.category
engineering
metadata.domain
prompt-engineering
metadata.updated
2026-06-17
metadata.tags
prompt-optimization, llm-evaluation, agents, prompt-engineering

Senior Prompt Engineer

Prompt engineering patterns, LLM evaluation frameworks, and agentic system design. Provides static (deterministic) analysis tools to optimize prompts, evaluate RAG retrieval and generation quality, and validate/visualize agent workflows — plus deep reference libraries of prompt patterns, evaluation metrics, and agent architectures.

Core Capabilities

  • Prompt optimization — token counting and cost estimation, clarity/structure scoring, ambiguity and redundancy detection, and generation of optimized prompt versions.
  • Few-shot & structured output design — extract/manage few-shot examples, design diverse example sets (simple/edge/complex/negative), and enforce reliable JSON/XML schema outputs.
  • RAG evaluation — context relevance, answer faithfulness, groundedness (ROUGE-L), and retrieval metrics (Precision@K, MRR, NDCG) over pre-retrieved contexts.
  • Agentic system design — validate agent configs, visualize flows (ASCII/Mermaid), estimate token cost per run, and apply ReAct / Plan-Execute / Tool-Use / multi-agent patterns.
  • Pattern library — 10 prompt patterns, evaluation frameworks (A/B testing, benchmarks, human eval), and agent architectures with pseudocode.

When to Use

  • Optimizing an existing prompt's performance or reducing token costs.
  • Designing prompt templates, few-shot examples, or structured-output workflows.
  • Evaluating LLM outputs or RAG retrieval/generation quality.
  • Building or validating agentic systems and tool-calling workflows.

Tools

ToolPurposeCommand
prompt_optimizer.pyAnalyze/optimize prompts: tokens, clarity, structure, few-shot extractionpython scripts/prompt_optimizer.py prompt.txt --analyze
rag_evaluator.pyEvaluate RAG context relevance, faithfulness, retrieval metricspython scripts/rag_evaluator.py --contexts ctx.json --questions q.json
agent_orchestrator.pyValidate, visualize, and cost-estimate agent configspython scripts/agent_orchestrator.py agent.yaml --validate

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

  • references/tools-and-workflows.md — full tool usage with sample outputs, the prompt-optimization / few-shot / structured-output workflows, common-patterns and command quick references, troubleshooting table, success criteria, and complete per-script parameter/output-format reference. Read when running any tool or executing a workflow.
  • references/prompt_engineering_patterns.md — 10 prompt patterns (zero/few-shot, CoT, role, structured output, self-consistency, ReAct, tree-of-thoughts, RAG) with example inputs and expected outputs. Read when choosing or applying a prompt technique.
  • references/llm_evaluation_frameworks.md — evaluation metrics, text-generation and RAG-specific scoring, human-eval frameworks, A/B testing, benchmark datasets, and pipeline design. Read when measuring quality or comparing prompts.
  • references/agentic_system_design.md — agent architectures (ReAct, Plan-and-Execute, Tool Use, multi-agent, memory/state) and design patterns with pseudocode. Read when building agents or tool-calling systems.
Show full SKILL.md (224 more words)Show less

Scope & Limitations

This skill covers:

  • Static prompt analysis: token counting, clarity scoring, structure detection, and optimization suggestions
  • RAG evaluation: context relevance, answer faithfulness, groundedness, and retrieval metrics (Precision@K, ROUGE-L, MRR, NDCG)
  • Agent workflow design: configuration validation, ASCII/Mermaid visualization, and token cost estimation
  • Few-shot example extraction and management from existing prompts

This skill does NOT cover:

  • Live LLM calls or runtime prompt testing --- all analysis is static/deterministic (see senior-ml-engineer for LLM integration)
  • Vector database setup or embedding generation --- RAG evaluator scores pre-retrieved contexts only (see senior-data-engineer for pipeline orchestration)
  • Fine-tuning, RLHF, or model training workflows (see senior-ml-engineer for model deployment)
  • Production monitoring, A/B test execution, or real-time drift detection (see senior-data-scientist for experiment design)

Integration Points

SkillIntegrationData Flow
senior-ml-engineerLLM integration and model deploymentOptimized prompts from this skill feed into the prompt templates of its LLM integration layer
senior-data-scientistA/B test design for prompt experimentsexperiment_designer.py defines test parameters; this skill provides the prompt variants to compare
senior-data-engineerRAG pipeline orchestrationpipeline_orchestrator.py builds the retrieval pipeline; this skill evaluates its output quality
senior-fullstackEnd-to-end application scaffoldingFullstack apps consume agent configs validated by agent_orchestrator.py
senior-securityPrompt injection and adversarial input reviewSecurity analysis covers the attack surface; this skill ensures prompts include defensive constraints
senior-qaQuality assurance for AI-powered featuresQA test suites validate that optimized prompts produce consistent outputs in production

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in engineering/senior-prompt-engineer of borghei/Claude-Skills.

  • SKILL.md
  • references/agentic_system_design.md
  • references/llm_evaluation_frameworks.md
  • references/prompt_engineering_patterns.md
  • references/tools-and-workflows.md
  • scripts/agent_orchestrator.py
  • scripts/prompt_optimizer.py
  • scripts/rag_evaluator.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

Senior Prompt Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Senior Prompt Engineer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Senior Prompt Engineer this skillborghei/Claude-Skills886—~1.5kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2603 repos~1.4kAutomated safety check: PassCustom licence
Prompt Engineering Patternswshobson/agents40k—~1.3kAutomated safety check: PassMIT
Kayba Stage 2 Domain Contextkayba-ai/agentic-context-engine2.6k—~1.9kAutomated safety check: PassApache-2.0
DSPy Language Model ProgrammingOrchestra-Research/AI-Research-SKILLs13k9 repos~3.8kAutomated safety check: PassMIT
Lintlanghermes-labs-ai/lintlang138—~719Automated safety check: PassApache-2.0

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    40k GitHub stars~1.3k tokensUpdated 4 days ago
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Works with

Categories

Questions about Senior Prompt Engineer

What does Senior Prompt Engineer do?

Prompt engineering and LLM evaluation. An agent skill from borghei/Claude-Skills. Senior Prompt Engineer is an agent skill from borghei/Claude-Skills. Prompt engineering and LLM evaluation.

When should I use Senior Prompt Engineer?

Senior Prompt Engineer fits situations like: optimizing prompts; designing prompt templates; evaluating LLM outputs; building agentic systems.

How do I install Senior Prompt Engineer in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill senior-prompt-engineer -a claude-code`. Or copy the skill folder (engineering/senior-prompt-engineer in borghei/Claude-Skills) into .claude/skills/senior-prompt-engineer in your project. Claude Code loads it when a task matches its description.

How do I install Senior Prompt Engineer in Codex?

Run `npx skills add borghei/Claude-Skills --skill senior-prompt-engineer -a codex`. Or copy the skill folder (engineering/senior-prompt-engineer in borghei/Claude-Skills) into .agents/skills/senior-prompt-engineer in your project. Codex loads it when a task matches its description.

Can I use Senior Prompt Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill senior-prompt-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-prompt-engineer, .gemini/skills/senior-prompt-engineer, .github/skills/senior-prompt-engineer and .opencode/skills/senior-prompt-engineer in your project.

What does Senior Prompt Engineer need to run?

Going by SKILL.md and its folder, Senior Prompt Engineer needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Senior Prompt Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Senior Prompt Engineer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Senior Prompt Engineer use?

Senior Prompt Engineer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Senior Prompt Engineer use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17k tokens, read only when the agent opens those files.

What are the alternatives to Senior Prompt Engineer?

Skills that share tags, products or a category with Senior Prompt Engineer: Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Prompt Engineering Patterns (wshobson/agents, 40k stars), Kayba Stage 2 Domain Context (kayba-ai/agentic-context-engine, 2.6k stars) and DSPy Language Model Programming (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Senior Prompt Engineer?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 886 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.