Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
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/unicomai/wanwu/figure-stylenpx skills add UnicomAI/wanwu --skill figure-stylegit clone --depth 1 https://github.com/UnicomAI/wanwuWrote 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/unicomai/wanwu/figure-style)<a href="https://agentmods.dev/skills/unicomai/wanwu/figure-style"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/figure-style.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 | $0.00167 | $0.04034 |
| Opus 5 | $0.00084 | $0.02017 |
| Sonnet 5 | $0.00033 | $0.00807 |
| Haiku 4.5 | $0.00017 | $0.00403 |
Grade A, and why
figure-style 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 7d 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.
This is a copy
83% identical to figure-style — 147 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Publication-Grade Figure Rules
A checklist for correct, legible, internally-consistent scientific figures. This
skill does not impose a visual house style — frame, font, and palette are
parameters. Load it and call apply_figure_style() before any plot.
§0 Scope
§1–§3, §8, and §9 are correctness — they apply to every plot, in every
context, and have no aesthetic content. §4–§7 are guidance — defaults that
produce a clean result but that a deliberate alternative can override
(individual rules inside §4–§7 that state a factual/perceptual invariant — e.g.
§4.4 semantic-zero centring, §4.5 CVD, §6.9 leader anchoring — still bind). On
its own, this skill is the inner tier (make one plot good); figure-composer
and paper-narrative supply multi-panel and whole-paper context.
§1 Data fidelity & self-consistency
1.1 Excluded rows. A row marked excluded or flagged in the source data is either omitted entirely or drawn with a visually distinct open/hatched marker and named in the key. It never enters a summary statistic plotted alongside the included rows.
1.2 Comparable conditions only. Arms measured under non-comparable conditions (different N, epoch budget, initialisation, protocol) are not plotted as visual peers. Separate them with a facet break or a marker on the label, and state the difference once in the caption.
1.3 Self-consistency. Every key, threshold, and title inside the figure must be satisfied by every plotted row. Before saving, walk each categorical outcome label back to the rule that defines it; if a row's value contradicts its label or the title, the figure is wrong, not the data.
1.4 Claim-titles must be true. A sentence-title (§5.1) is tested against every category on the axis before rendering. If any contradicts it, qualify the title ("on 3 of 4 pairs") or downgrade it to a description.
1.5 State n and what was held fixed. Every panel that draws a summary mark
states n and the unit of replication, and every small-multiple that holds a
variable fixed states the fixed value — in the panel or, when §2 budget is
tight, in the caption.
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.
- 7d ago First seen · 332 lines · 167 tokens per session scan A 7bd8416f1abc
figure-style is a skill published in the GitHub repository UnicomAI/wanwu (2,458 stars, last pushed 2d ago), licensed Apache-2.0. It adds 167 tokens to every session and 4,034 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to figure-style, differing in 147 lines, and is treated as a copy.
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