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 NPC-Worldwide/npcsh --skill npcpy-promptinggit clone --depth 1 https://github.com/NPC-Worldwide/npcshWrote 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/npc-worldwide/npcsh/npcpy-prompting)<a href="https://agentmods.dev/skills/npc-worldwide/npcsh/npcpy-prompting"><img src="https://agentmods.dev/badge/skills/npc-worldwide/npcsh/npcpy-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/npc-worldwide/npcsh/npcpy-prompting"><img src="https://agentmods.dev/badge/skills/npc-worldwide/npcsh/npcpy-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00016 | $0.00634 |
| Opus 5 | $0.00008 | $0.00317 |
| Sonnet 5 | $0.00003 | $0.00127 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
npcpy-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 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
npcpy-prompting
npcpy LLM prompting and JSON formatting patterns.
Imports
Always import at module level:
from npcpy.llm_funcs import get_llm_responsefrom npcpy.npc_compiler import NPCfrom npcpy.gen.response import get_litellm_response(only for streaming)
Basic-Call
get_llm_response(prompt, model, provider, **kwargs) — first arg is POSITIONAL.
Do NOT write prompt=prompt. Do NOT use NPC.call(). The function is module-level.
Json-Mode
Pass format="json" for structured output.
npcpy parses internally. Access via response["response"].
Never call json.loads() manually.
Npc-Object
Create an NPC to hold model, provider, and primary_directive.
Pass it as npc=npc_instance so npcpy reads those values:
npc = NPC(name="...", primary_directive="...", model="...", provider="...")
response = get_llm_response(prompt, npc=npc, format="json", temperature=0.7)
data = response["response"]
Messages
Pass conversation history as messages=[{"role": "system", "content": msg}].
This is a kwarg like any other. It does not persist between calls.
Parameters
Sampling kwargs to get_llm_response:
temperature,top_p,top_k,max_tokensstream=Truereturns a generator inresponse["response"]
Streaming
For token-level streaming use get_litellm_response with stream=True.
For segment-level use get_llm_response(..., stream=True) and iterate response["response"].
Anti-Patterns
- Do NOT use
json.loads(response["response"]). - Do NOT call
response.get("response")and then parse it again. - Do NOT assume
responseis a string whenformat="json"is used.
Prompt-Formatting
When constructing prompt strings in Python:
- Use
f"""..."""for all multiline prompts. Do NOT use implicit string concatenation. - Do NOT put multiline strings directly in a
returnstatement. Assign to a variable first, then return it. - The closing
"""must be at the same indentation as the variable assignment. - Do NOT escape braces as
{{inside f-strings. If you need literal curly braces in the prompt output, use explicit string concatenation:prompt = f"""Write a JSON response like this:""" + """\n{'key': 'value'}\n""" - Never use
f"..." f"..."on adjacent lines or in parentheses expecting the parser to concatenate them.
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 · 81 lines · 16 tokens per session scan A fb383368de3b
npcpy-prompting is a skill published in the GitHub repository NPC-Worldwide/npcsh (478 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 634 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 2026-08-30.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…