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 skills add Jsebayhi/prompt-engineering-extension --skill prompt_engineeringgit clone --depth 1 https://github.com/Jsebayhi/prompt-engineering-extensionWrote 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/skills/jsebayhi/prompt-engineering-extension/prompt_engineering)<a href="https://agentmods.dev/skills/jsebayhi/prompt-engineering-extension/prompt_engineering"><img src="https://agentmods.dev/badge/skills/jsebayhi/prompt-engineering-extension/prompt_engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jsebayhi/prompt-engineering-extension/prompt_engineering"><img src="https://agentmods.dev/badge/skills/jsebayhi/prompt-engineering-extension/prompt_engineering.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00043 | $0.00483 |
| Opus 5 | $0.00022 | $0.00242 |
| Sonnet 5 | $0.00009 | $0.00097 |
| Haiku 4.5 | $0.00004 | $0.00048 |
Grade A, and why
prompt engineering 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 10d 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.
What it actually says
Prompt Engineering Skill
You possess the skills of a world-class Prompt and Context Engineering Master.
Capabilities
- Prompt Design: You can craft high-performance prompts using advanced methodologies (Chain of Thought, Tree of Thoughts, PCTR Framework).
- Adversarial Analysis: You proactively identify flaws, loopholes, and ambiguities in prompts (Red Teaming).
- Optimization: You can refine existing prompts to be more efficient, precise, and robust.
Mandates & Protocol
CRITICAL: When utilizing this skill, you MUST strictly adhere to the protocols defined in the reference documents. Do not rely solely on your general training; use the specific engineering workflows provided below.
-
Workflow Enforcement:
- For any request involving the creation or significant modification of a prompt, you MUST follow the Collaborative Prompt Building Workflow.
- Reference:
references/prompt_building_workflow.md
-
Best Practices Application:
- Consult the Prompt Engineering Guide to select the appropriate techniques (e.g., "Step-Back Prompting", "Role-Based Prompting") for the specific task.
- Reference:
references/prompt_engineering_guide.md
-
Pattern Utilization:
- Review the Golden Examples to identify proven patterns (e.g., "Pragmatic Ambiguity Handling", "Stateful Q&A Protocol") that can be adapted to the user's needs.
- Reference:
references/prompt_golden_examples.md
Guiding Principles
- Goal-First: Always deconstruct the user's intent, not just their literal instruction.
- Systematic & Adversarial: Build step-by-step, then mercilessly critique your own work before presenting it.
- Pragmatic: Tailor the complexity of the prompt to the complexity of the task.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 39 lines · 43 tokens per session scan A 88cdb686e713
prompt engineering is a skill published in the GitHub repository Jsebayhi/prompt-engineering-extension (7 stars, last pushed 8mo ago), licensed MIT. It adds 43 tokens to every session and 483 once invoked, about $0.0002 per session on Opus 5. 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 skills, from other repositories
appendix-prompt-engineering
This skill should be used when the user wants to learn "prompt engineering", "few-shot prompting", "zero-shot prompting", "chain of thought prompting", "structured output prompting", "role prompting", "system prompt design", "prompt best practices", "CoT prompting", "Pydantic structured output", "prompt iteration"…
appendix-reasoning-engines
This skill should be used when the user wants to understand "reasoning models", "thinking tokens", "extended thinking", "LLM internal reasoning", "o1 style reasoning", "Gemini thinking model", "chain of thought internally", "reasoning vs standard models", "when to use reasoning models", "inference-time compute…
prompt-chaining
This skill should be used when the user wants to "chain prompts", "build a pipeline", "break down complex tasks into steps", "sequential LLM calls", "multi-step reasoning", "pipeline pattern", "sequential agent pipeline", "multi-step prompt pipeline", "LLM chain", "step-by-step agent", "prompt pipeline", "decompose…
prompt-optimizer
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite…
gemini-api
Google Gemini API patterns for Python and TypeScript. Covers content generation, streaming, tool use (function calling), vision, system instructions, context caching, batch requests, and agent workflows. Use when building applications with the Gemini API or Google Generative AI SDKs.
image-prompt
A skill that turns a vague request into a detailed prompt for generating images, including options for covers, posters, promotional materials, typography, and other visual formats. A prompt is an instruction given to an image-generation system.