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 skills/graycodeai/starling/mdc-llm-prompt-engineeringnpx skills add GrayCodeAI/starling --skill mdc-llm-prompt-engineeringgit clone --depth 1 https://github.com/GrayCodeAI/starlingWrote 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/graycodeai/starling/mdc-llm-prompt-engineering)<a href="https://agentmods.dev/skills/graycodeai/starling/mdc-llm-prompt-engineering"><img src="https://agentmods.dev/badge/skills/graycodeai/starling/mdc-llm-prompt-engineering.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 | $0.00029 | $0.00068 |
| Opus 5 | $0.00015 | $0.00034 |
| Sonnet 5 | $0.00006 | $0.00014 |
| Haiku 4.5 | $0.00003 | $0.00007 |
Grade A, and why
mdc-llm-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 4d 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
- LLM Prompt Engineering: Dedicate a module or files for managing Prompt templates with version control.
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.
- 4d ago First seen · 8 lines · 29 tokens per session scan A fd16ca171f5b
mdc-llm-prompt-engineering is a skill published in the GitHub repository GrayCodeAI/starling (2 stars, last pushed 4d ago), licensed MIT. It adds 29 tokens to every session and 68 once invoked, about $0.0001 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
v4-best-practices
Use when working with deepseek-v4-pro or deepseek-v4-flash in thinking mode on multi-step or plan-driven tasks. Provides rules to prevent stale references, unverified plan assumptions, and vague plan output.
pi-prompting
Internal guidance for composing prompts that Pi runs (DeepSeek by default) handle reliably for coding, review, diagnosis, and research tasks.
prompt-engineering
Write prompts that are reliable, testable, versioned, and safe.
create-pi-prompt
Como criar prompt templates para pi. Use quando o usuário quiser criar atalhos /comando que expandem em prompts completos.
orchestrate-prompt-engineering
Write prompts for a HackerRank Orchestrate agent with the same engineering rigor as code — explicit allowed-output specifications, required-evidence framing, and format requirements, treating the prompt as a reviewable artifact rather than throwaway text. Use whenever writing or revising a system/task prompt for the…
prompt-polisher
Use when receiving messy, unstructured input like voice transcriptions, stream-of-consciousness notes, or rough document content that needs to be transformed into a polished, optimized prompt. Cleans up filler words, extracts intent, asks clarifying questions, applies Claude 4.x/Opus 4.5/Sonnet 4.5 best practices, and…