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/sammcj/agentic-coding/llm-prompting-guidenpx skills add sammcj/agentic-coding --skill llm-prompting-guidegit clone --depth 1 https://github.com/sammcj/agentic-codingWrote 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/sammcj/agentic-coding/llm-prompting-guide)<a href="https://agentmods.dev/skills/sammcj/agentic-coding/llm-prompting-guide"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/llm-prompting-guide.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 | $0.00079 | $0.00330 |
| Opus 5 | $0.00039 | $0.00165 |
| Sonnet 5 | $0.00016 | $0.00066 |
| Haiku 4.5 | $0.00008 | $0.00033 |
Grade A, and why
llm-prompting-guide 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.
What it actually says
LLM Prompting Guide
The models below were trained on prompts written in a specific labelled structure, and guessing at that structure degrades output. Read the full guide for the model in play before writing any prompt text; the bullet here is a router, not enough to draft from.
- MiniMax H3 (video + native stereo audio; T2VA, I2VA, FL2VA, L2VA, R2V) -> references/minimax-h3.md
- MiniMax Music 3 (full songs from a structured caption + tagged lyrics) -> references/minimax-music-3.md
- Music 3 only responds to styles its training corpus contains. Check every genre term and production phrase before writing: references/minimax-music-3-corpus.md
Adding a model: one bullet above, plus the skills references/<model>.md opening with a table of contents and a mode-selection table.
By default, unless instructed otherwise save your generated outputs to a file in the current working directory named something related to the request.
What ships with it
6 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.
- 6d ago First seen · 17 lines · 79 tokens per session scan A 74fd7338156b
llm-prompting-guide is a skill published in the GitHub repository sammcj/agentic-coding (159 stars, last pushed yesterday), licensed Apache-2.0. It adds 79 tokens to every session and 330 once invoked, about $0.0004 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 2026-08-30.
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