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 download4you/n2-fieldops --skill fieldops-prompt-refinergit clone --depth 1 https://github.com/download4you/n2-fieldopsWrote 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/download4you/n2-fieldops/fieldops-prompt-refiner)<a href="https://agentmods.dev/skills/download4you/n2-fieldops/fieldops-prompt-refiner"><img src="https://agentmods.dev/badge/skills/download4you/n2-fieldops/fieldops-prompt-refiner/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/download4you/n2-fieldops/fieldops-prompt-refiner"><img src="https://agentmods.dev/badge/skills/download4you/n2-fieldops/fieldops-prompt-refiner.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 | $0.00103 | $0.00250 |
| Opus 5.5 | $0.00041 | $0.00100 |
| Sonnet 5.5 | $0.00021 | $0.00050 |
| Haiku 4.5 | $0.00010 | $0.00025 |
Grade A, and why
fieldops-prompt-refiner 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.
What it actually says
Prompt Refiner
- Extract outcome, audience/agent, inputs, environment, and completion condition.
- Separate hard constraints from preferences and examples.
- Ask only about ambiguities that materially change safe execution.
- Remove duplication, conflict, unenforceable claims, and low-value prose.
- Organize with
references/prompt-architecture.md. - Add proportional evidence and verification requirements.
- Preserve language, terminology, intent, and authority boundary.
- Return a copy-ready prompt plus a short design note when useful.
For large reusable profiles, prefer modular skills and references over one always-loaded prompt. Never claim a prompt can override higher-priority runtime instructions.
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 · 18 lines · 103 tokens per session scan A 5cdf643a408b
fieldops-prompt-refiner is a skill published in the GitHub repository download4you/n2-fieldops (3 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 250 once invoked, about $0.0004 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.
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