Borrowing it
Nothing to install: this file belongs to Mapika/nice-figures. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Mapika/nice-figures/main/AGENTS.mdgit clone --depth 1 https://github.com/Mapika/nice-figuresWrote 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/instructions/mapika/nice-figures/agents-md)<a href="https://agentmods.dev/instructions/mapika/nice-figures/agents-md"><img src="https://agentmods.dev/badge/instructions/mapika/nice-figures/agents-md/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/instructions/mapika/nice-figures/agents-md"><img src="https://agentmods.dev/badge/instructions/mapika/nice-figures/agents-md.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 | $0.00837 | $0.00837 |
| Opus 5.5 | $0.00335 | $0.00335 |
| Sonnet 5.5 | $0.00167 | $0.00167 |
| Haiku 4.5 | $0.00084 | $0.00084 |
Grade A, and why
nice-figures AGENTS.md scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -O https://raw.githubusercontent.com/Mapika/nice-figures/main/plugins/nice-figures/skills/nice-figures/scripts/soft_style.py How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nice-figures — instructions for AI agents
This repo provides a matplotlib visual register for scientific and research-blog figures: bold display titles, soft pastel palettes, smoothed trends with shaded bands, signature rounded bars, minimal axes. White background by default — output is conference- and paper-ready (PDFs embed Type 42 fonts).
If you are an agent asked to produce a plot in this style — or just a
good-looking research figure — follow this file. It is the
agent-agnostic equivalent of the Claude Code skill in
plugins/nice-figures/skills/nice-figures/SKILL.md.
Get the style helper
Either of:
pip install git+https://github.com/Mapika/nice-figures # import soft_style
# or copy the single file (matplotlib + numpy only):
curl -O https://raw.githubusercontent.com/Mapika/nice-figures/main/plugins/nice-figures/skills/nice-figures/scripts/soft_style.py
Workflow
- Pick the closest archetype from
chart_recipes.md— 16 tested, copy-paste-runnable recipes (trend bands, rounded bars, heatmaps, ROC, distributions, scaling laws, forest plots, Pareto fronts, …). Copy it and adapt only what's necessary. - Call
configure_style()before any plotting (scale=0.75for single-column paper figures). - Export with
save_figure(fig, name)→ PDF + PNG at 300 dpi. - Render, then look at the PNG with your vision capability before
delivering. Check: legend not covering data (
top_legend()exists for this), every multi-series plot has a legend or colorbar, nothing clipped, tick labels not colliding. Fix and re-render until clean. This step catches what running the code cannot.
Hard rules of the register
- Bar charts use
rounded_bars()/rounded_hbars(), never plainax.bar()— and axis limits are set before calling them. - Titles via
figure_title()(bold), panel labels viapanel_subtitle(). - One palette per figure (
LINE_PALETTE,BAR_PALETTE,MULTILINE_PALETTE);CMAP_GRADIENT+ colorbar for 6+ ordered series. - No grid; bottom + left spines only; no pure-black text
(use
NEUTRAL["ink"]for error bars and emphasis lines). - White background unless the user explicitly wants the cream blog look.
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 · 73 lines · 837 tokens per session scan A 09401de341aa
nice-figures AGENTS.md is an instructions file published in the GitHub repository Mapika/nice-figures (12 stars, last pushed 4mo ago), licensed MIT. It adds 837 tokens to every session, about $0.0033 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.