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 5.5 | $0.00006 | $0.00254 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 (502 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 634 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 2026-08-30.
npcpy-prompting compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| npcpy-promptingNPC-Worldwide/npcsh | Skill | 502 | 3d ago | MIT | A | 16 | Claude Code, Codex |
| blueprint-from-collection-actionsreceptron/mulmoterminal | Skill | 261 | yesterday | MIT | A | 34 | Claude Code, Codex |
| blueprint-from-collection-actionsreceptron/mulmoterminal | Skill | 261 | yesterday | MIT | A | 34 | Claude Code, Codex |
| blueprint-from-collection-actionsreceptron/mulmoterminal | Skill | 261 | yesterday | MIT | A | 31 | Claude Code, Codex |
| loom-prompt-engineeringcosmix/loom | Skill | 65 | 5d ago | MIT | A | 34 | Claude Code |
| prompt-engineeringdoodledood/manifest-dev | Skill | 78 | yesterday | MIT | A | 56 | Claude Code |
| review-promptdoodledood/manifest-dev | Skill | 78 | yesterday | MIT | A | 60 | Claude Code |