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 vikasudasi/skill-vault --skill llm-promptinggit clone --depth 1 https://github.com/vikasudasi/skill-vaultWrote 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/vikasudasi/skill-vault/llm-prompting)<a href="https://agentmods.dev/skills/vikasudasi/skill-vault/llm-prompting"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/llm-prompting/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/vikasudasi/skill-vault/llm-prompting"><img src="https://agentmods.dev/badge/skills/vikasudasi/skill-vault/llm-prompting.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 | $0.00032 | $0.01495 |
| Opus 5.5 | $0.00013 | $0.00598 |
| Sonnet 5.5 | $0.00006 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
llm-prompting 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 1mo 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effective LLM Prompting
Use when getting an LLM to produce reliable output — structured or free-form — rather than hoping a vague instruction happens to work. Prompts are code: they drift, they regress, and they need tests.
Pair this with the llm-json-output skill when the target must be machine-parseable JSON. Rule of thumb established there: if the provider supports a native structured-output / JSON-schema mode, use that first and treat free-form prompting as the fallback. This skill is about making the fallback (and any free-form generation) as reliable as possible.
The five-part skeleton
- Role — one line of expertise/framing: "You are a senior Python reviewer."
- Task — one clear imperative sentence stating the deliverable.
- Input — the data the model operates on (small, self-contained, as ground truth).
- Output format — exact schema, delimiters, or shape; state "no other text".
- Few-shot examples — 2-3 ideal in/out pairs only when ambiguity exists.
Worked example, applied end-to-end
Goal: extract {port, enabled, name} from a config blob, drift-free.
[SYSTEM]
You are a config parser. Extract exactly three fields from the input into a
JSON object. Follow the schema, never invent values, never add prose.
[USER]
Input:
service: metrics, listen: 0.0.0.0:9100, tls: off
Return exactly: {"name": string, "port": int, "enabled": bool}
Example: "grafana" -> {"name": "grafana", "port": 3000, "enabled": true}
Then validate: json.loads, type-check the three fields, and if anything is
missing or malformed re-prompt with the exact error (see llm-json-output's
retry loop). The negative example below ("tls: off" → enabled: false)
sharpens behavior more than a second positive does — show one counter-case
when the mapping is non-obvious.
Decision table: which prompting mode when
| Mode | When | Why |
|---|---|---|
| Native structured output / JSON-schema | Provider supports it and output is machine-read | Constrains generation; no parsing hacks. Always first choice for llm-json-output work. |
| Zero-shot | One obvious task, correct output is unambiguous | Cheapest; no example tokens; fewer chances to overfit |
| Few-shot (2-3) | Ambiguous mapping, custom format, or rare edge, free-form output | Shows the exact shape; cost is example tokens and overfitting risk |
| Choose-then-run (agentic) | Task needs search/tools first | Same skeleton, but input comes from tool results, not the prompt |
What ships with it
2 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.
- 1mo ago First seen · 132 lines · 32 tokens per session scan A 195a2073c48e
llm-prompting is a skill published in the GitHub repository vikasudasi/skill-vault (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,495 once invoked, about $0.0001 per session on Opus 5.5. 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
role-ai-ml-engineer
Use when orchestrating AI/ML systems, RAG pipeline architecture, multi-provider model routing, prompt engineering, agent self-improvement, model fine-tuning, and LLM serving deployments.
Prompt Refiner
Improves AI prompts to be clearer, more specific, and produce more consistent outputs.
prompt-engineering
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, designing production prompt templates, or building AI-powered features.
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
natural
Render the provided prompt template with Jinja context and send it to the active NPC's LLM. The rendered text becomes the user prompt; the LLM response is placed in context['output'].