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/yuanruichen/metforge/metforge-figurenpx skills add yuanruichen/MetForge --skill metforge-figuregit clone --depth 1 https://github.com/yuanruichen/MetForgeWrote 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/yuanruichen/metforge/metforge-figure)<a href="https://agentmods.dev/skills/yuanruichen/metforge/metforge-figure"><img src="https://agentmods.dev/badge/skills/yuanruichen/metforge/metforge-figure.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 5.1 | $0.00121 | $0.01012 |
| Opus 5 | $0.00060 | $0.00506 |
| Sonnet 5 | $0.00024 | $0.00202 |
| Haiku 4.5 | $0.00012 | $0.00101 |
Grade A, and why
metforge-figure 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 5d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MetForge Figure
Treat each figure as a visual scientific argument. Define what it must show, render it with the user's existing stack when possible, and inspect the exported result before delivery.
Figure loop
- Inspect the data, existing plotting code, target medium, and relevant prior figures.
- Write a compact figure contract using references/figure-contract.md.
- Choose the smallest figure grammar that tests the claim. Read references/atmospheric-patterns.md for atmospheric defaults.
- Implement within the user's language and project conventions. For a new Python workflow, prefer xarray + matplotlib and use cartopy only when geographic projection is needed.
- Render at final or representative size. Open the actual output and audit it with references/qa-checklist.md.
- Fix high-impact scientific or readability failures, rerender, and reinspect. Stop polishing when the contract is satisfied.
Execution mode and processing record
Before rendering, inspect data size, memory needs, expected runtime, available CPUs, whether sbatch/Slurm is available, whether the session is already inside a Slurm job, and existing project conventions.
- Render directly for ordinary figures and small validation subsets that fit safely in the current session.
- Use Slurm or the site's scheduler when figure preparation requires large aggregations, many files, ensemble processing, high-resolution rasterization, or work unsuitable for a login node.
- Do not guess scheduler account, partition, walltime, modules, or paths. Reuse validated project configuration.
- For submitted work, capture the job script, job ID, resources, environment, stdout/stderr paths, and terminal status.
After every completed data-preparation call, render/export, or audit-revision cycle, append a record to the project's existing log/provenance location. If none exists, use output/logs/YYYYMMDD-HHMM-<task>.md. Record inputs, software environment, direct/Slurm decision, commands or script, figure contract, transformations, output files, visual checks, deviations, and status. Never record credentials or secrets. If files cannot be written, return the same record explicitly in the response.
What ships with it
4 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.
- 5d ago First seen · 67 lines · 121 tokens per session scan A a119799f6305
metforge-figure is a skill published in the GitHub repository yuanruichen/MetForge (4 stars, last pushed 23d ago), licensed MIT. It adds 121 tokens to every session and 1,012 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
figure-review
Audit a scientific figure for publication-readiness: colormaps, uncertainty, axis labels, caption completeness, and claim support. Panel-aware — judges one panel of a composite against what its shared caption and sibling panels supply, and raises a likely-deliberate choice as by-design? instead of a defect. Use this…
lit-review
Search and synthesize scientific literature from Zotero, arxiv, bioRxiv, Google Scholar, and Consensus. Use when framing a research question, designing methods, interpreting results, mapping the field landscape, or identifying future directions.
figure-style
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a…
literature-review
Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to evidence strength.
paper-narrative
Judge and reshape the story told by an entire paper figure deck. Use when writing or revising a paper to derive a grounded brief from the manuscript and captions, review the full deck as a handling editor, and hand an ordered figure arc to figure-composer.
remote-compute-ssh
Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.