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 elvisun/newsjack --skill realistic-prompt-generationgit clone --depth 1 https://github.com/elvisun/newsjackWrote 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/elvisun/newsjack/realistic-prompt-generation)<a href="https://agentmods.dev/skills/elvisun/newsjack/realistic-prompt-generation"><img src="https://agentmods.dev/badge/skills/elvisun/newsjack/realistic-prompt-generation/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/elvisun/newsjack/realistic-prompt-generation"><img src="https://agentmods.dev/badge/skills/elvisun/newsjack/realistic-prompt-generation.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 | $0.00055 | $0.01417 |
| Opus 5 | $0.00028 | $0.00709 |
| Sonnet 5 | $0.00011 | $0.00283 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
realistic-prompt-generation 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 11d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Realistic Prompt Generation
Write authentic prompts without manufacturing recommendation opportunities.
This skill inherits the ethical floor from skills/ETHICS.md. It enforces anti-hallucination and evidence-bound language. Anti-spray and human-send are not applicable.
Hard context boundary
For unaided generation, accept only:
- anonymized segment and role labels;
- approved jobs, constraints, information acts, journey states, and locales;
- short source-language fragments safe under the permitted-data rules;
- evidence IDs and grades;
prompt_architecture.json;- style and turn-form requirements.
Do not accept or inspect:
- target brands, products, domains, people, slogans, campaign terms, proprietary categories, or flattering claims;
- current AI answers, rankings, mentions, citations, gaps, or target pages;
- the contamination register itself.
If those fields appear, stop unaided generation and request a sanitized blind_design_brief.json. When subagents or fresh sessions are available, generate in a fresh context that receives only the blind brief and architecture.
The exception is an explicitly separate B0 aided pass. It may receive only the target aliases needed by approved B0 cells.
Preserve the canonical intent cell
For each architecture cell, hold constant:
- underlying job;
- journey state and information act;
- material constraints;
- persona/role and locale;
- proximity band;
- expected answer kind.
Create two core variants by default:
- the closest natural rendering of observed language;
- a natural paraphrase that preserves the same intent.
Use additional variants only for a wording-sensitivity pilot or rotating discovery. Do not create a full style × persona × locale grid.
Use variant_role: observed_language only when the candidate is verbatim or lightly normalized from a cited behavioral/query language sample. search_query_expanded, human_written, and llm_expanded candidates are natural_paraphrase or sensitivity; a source ID does not by itself make generated wording observed.
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.
- 11d ago First seen · 163 lines · 55 tokens per session scan A 3c880c038ade
realistic-prompt-generation is a skill published in the GitHub repository elvisun/newsjack (666 stars, last pushed 9d ago), licensed MIT. It adds 55 tokens to every session and 1,417 once invoked, about $0.0003 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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