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-orchestratorgit 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-orchestrator)<a href="https://agentmods.dev/skills/download4you/n2-fieldops/fieldops-orchestrator"><img src="https://agentmods.dev/badge/skills/download4you/n2-fieldops/fieldops-orchestrator/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-orchestrator"><img src="https://agentmods.dev/badge/skills/download4you/n2-fieldops/fieldops-orchestrator.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 | $0.00083 | $0.00268 |
| Opus 5.5 | $0.00033 | $0.00107 |
| Sonnet 5.5 | $0.00017 | $0.00054 |
| Haiku 4.5 | $0.00008 | $0.00027 |
Grade A, and why
fieldops-orchestrator 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
FieldOps Orchestrator
- Define the concrete outcome and authorized scope.
- Classify workstreams with
references/routing.md. - Select the smallest set of specialist skills.
- Order dependencies: controls, inspection, implementation, verification, report.
- For unfamiliar questions, route evidence gathering through
fieldops-research. - Maintain one ledger for assumptions, artifacts, decisions, and results.
- Stop at the requested mutation boundary.
- Deliver outcome, evidence, verification, and uncertainty.
Read and follow every selected skill completely. Do not duplicate its instructions or activate all skills by default. Preserve originals and unrelated work, prefer runtime evidence, use explicit UTF-8 boundaries for multilingual PowerShell, and require reproducible verification. When no specialist skill exists, decompose the task, use available tools directly, and keep the same evidence and verification contract.
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 · 83 tokens per session scan A d5588ac29fbf
fieldops-orchestrator is a skill published in the GitHub repository download4you/n2-fieldops (3 stars, last pushed 2mo ago), licensed MIT. It adds 83 tokens to every session and 268 once invoked, about $0.0003 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.
Other skills, from other repositories
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…
alterlab-paper-writer
Drafts and revises academic papers through a 12-agent pipeline with hardened LaTeX output (apa7 class, PDF compiled from LaTeX), supporting IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures, APA 7.0 (default), Chicago, MLA, IEEE, and Vancouver citation formats, bilingual…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-pyhealth
Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…
alterlab-citation-verifier
Verifies that every bibliography entry actually exists by cross-checking Crossref, OpenAlex, Semantic Scholar, and arXiv (no API key required) plus doi.org DOI registration, fuzzy-matching title and authors (difflib ratio >= 0.70), flagging retractions recorded by Crossref (including Retraction Watch data) or…
alterlab-timesfm
Forecasts time series zero-shot with Google's TimesFM foundation models — TimesFM 2.5 (200M, Apache-2.0 weights; ForecastConfig API, XReg covariates) and TimesFM 3.0 (330M, multivariate with native past/future covariates; non-commercial weights) — producing point forecasts and quantile prediction intervals from…