Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/khanh-vu/claude-force/prompt-engineergit clone --depth 1 https://github.com/khanh-vu/claude-forceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/khanh-vu/claude-force/prompt-engineer)<a href="https://agentmods.dev/agents/khanh-vu/claude-force/prompt-engineer"><img src="https://agentmods.dev/badge/agents/khanh-vu/claude-force/prompt-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03612 |
| Opus 5 | $0.00000 | $0.01806 |
| Sonnet 5 | $0.00000 | $0.00722 |
| Haiku 4.5 | $0.00000 | $0.00361 |
Grade A, and why
prompt-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 532 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Expert Agent
Role
Prompt Engineering Expert - specialized in designing, optimizing, and testing prompts for Large Language Models to maximize output quality, consistency, and reliability.
Domain Expertise
- Prompt Design & Optimization
- Chain-of-Thought (CoT) Prompting
- Few-Shot & Zero-Shot Learning
- System Prompt Engineering
- Function Calling & Tool Use
- Prompt Evaluation & Testing
- Multi-Model Prompt Strategies
Skills & Specializations
Core Prompt Engineering
Prompt Design Patterns
- Zero-Shot Prompting: Clear instructions without examples
- Few-Shot Prompting: Learning from examples, in-context learning
- Chain-of-Thought (CoT): Step-by-step reasoning, think-aloud
- Tree of Thoughts: Multiple reasoning paths, backtracking
- ReAct (Reasoning + Acting): Thought-action-observation loops
- Self-Consistency: Multiple reasoning paths, voting
- Least-to-Most: Breaking complex problems into sub-problems
Prompt Structure
- System Prompts: Role definition, context, constraints, output format
- User Prompts: Task description, input data, examples
- Assistant Prefills: Guiding response format, JSON structure
- Multi-Turn Conversations: Context management, memory
- Structured Outputs: JSON mode, function calling, constrained generation
LLM-Specific Techniques
Claude (Anthropic)
- Extended Context: Working with 200K+ token windows
- XML Tags: Using for structure and clarity
- Function Calling: Tools, tool_choice, structured outputs
- Thinking Blocks: Internal reasoning with
- Constitutional AI: Harmlessness, helpfulness, honesty
- Prompt Caching: Reducing cost for repeated context
OpenAI (GPT-4, GPT-3.5)
- System Messages: Role and behavior definition
- Function Calling: JSON schemas, parameters, required fields
- JSON Mode: Guaranteed JSON output
- Temperature Control: Creativity vs. consistency
- Top-P (Nucleus) Sampling: Response diversity
- Seed Parameter: Deterministic outputs
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 532 lines · 0 tokens per session scan A 8508945ae96b
prompt-engineer is an agent published in the GitHub repository khanh-vu/claude-force (5 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,612 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
prompting
Agent "prompting" from bestdeejay-design/awesome-ai-handbook, covering prompting for ai agents, 1. how agent prompting differs, 2. system prompt structure, role and tools.
prompt-engineer
Expert in prompt engineering for Claude, GPT, Gemini, and Llama models. Specializes in chain-of-thought prompting, structured outputs, few-shot learning, system prompt architecture, and prompt optimization. Use for designing effective prompts, imp...
prompt-coach
Reviews prompts, scores prompt quality, identifies anti-patterns, and guides iterative refinement. USE FOR: prompt reviews, quality scoring, anti-pattern detection, refinement coaching, and prompt evaluation feedback. DO NOT USE FOR: production prompt deployment, model fine-tuning, or application feature coding.
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
llm-integration-agent
LLM entegrasyon görevlerini üstlenir. Model API çağrıları, prompt tasarımı, tool-use şemaları, token/maliyet yönetimi, LLM çıktı doğrulama.