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 agentmods add skills/simota/agent-skills/fieldnpx skills add simota/agent-skills --skill fieldgit clone --depth 1 https://github.com/simota/agent-skillsWrote 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/simota/agent-skills/field)<a href="https://agentmods.dev/skills/simota/agent-skills/field"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/field.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.05251 |
| Opus 5 | $0.00017 | $0.02625 |
| Sonnet 5 | $0.00007 | $0.01050 |
| Haiku 4.5 | $0.00003 | $0.00525 |
Grade A, and why
field 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 4d 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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Field
"Good research asks the right questions. Great research changes what you thought was the question."
User research specialist — designs studies, conducts analysis, synthesizes insights, and delivers evidence-based recommendations. Field investigates and synthesizes; it does not implement product changes.
What ships with it
17 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.
- _common 10 B
- reference/_common 13 B
- reference/ai-assisted-research.md 2.3 KB
- reference/analysis-and-synthesis.md 2.7 KB
- reference/autorun-schema.md 1.9 KB
- reference/bias-checklist.md 2.3 KB
- reference/cards-ia-validation.md 7.0 KB
- reference/continuous-discovery-mixed-methods.md 2.3 KB
- reference/diary-longitudinal-study.md 7.2 KB
- reference/field 11 B
- reference/participant-screening.md 2.0 KB
- reference/research-anti-patterns-impact.md 2.6 KB
- reference/research-calibration.md 5.0 KB
- reference/research-ops-democratization.md 2.2 KB
- reference/spark 11 B
- reference/survey-quantitative-design.md 7.1 KB
- reference/tri-engine-research.md 19 KB
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.
- 4d ago First seen · 293 lines · 35 tokens per session scan A f88995459a5d
field is a skill published in the GitHub repository simota/agent-skills (75 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 5,251 once invoked, about $0.0002 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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