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 agents/danilkotelnikov/vedix/plottergit clone --depth 1 https://github.com/danilkotelnikov/vedixWhat 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 | $0.00085 | $0.01446 |
| Opus 5 | $0.00043 | $0.00723 |
| Sonnet 5 | $0.00017 | $0.00289 |
| Haiku 4.5 | $0.00009 | $0.00145 |
Grade A, and why
vedix-plotter 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 yesterday.
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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plotter
Iterative publication-grade figure generation. Three cycles, each independently re-runnable.
Inputs
<input name="output_dir">— job output directory containingresults.csvand any*.npyfiles<input name="article_type">—experimental|review|benchmark<input name="journal_style">—nature|cell|ieee|springer|auto<input name="mode">—scripting(matplotlib) |latex_native(TikZ/pgfplots) |both<input name="cycle">—1|2|3|all<input name="figure_specs">— JSON list of{figure_id, kind, x, y, title, facets}
Cycle 1 — Inspect & Draft
For each spec:
- Sniff the schema of
results.csv— column dtypes, row count, missing %, ranges. - Justify the chosen plot kind in one sentence (e.g. "violin over bar because n=847 and Shapiro-Wilk rejects normality at p<0.001").
- Render a draft PNG at 2× target DPI (high enough to evaluate legibility, not final).
- Write
figures_draft1/<id>.pngandfigures_draft1/manifest.json.
Article-type defaults:
- review → bibliometric figures: timeline, taxonomy tree, co-citation cluster heatmap, journal/year heatmap. Booktabs summary tables in LaTeX. Prefer
latex_nativemode for font consistency with manuscript. - experimental → headline result with paired comparison + 95 % bootstrap CI; ablation table; Pareto front.
- benchmark → score-vs-method bars (sorted by median, not alphabetically), per-task heatmap, compute-vs-quality scatter, Bradley-Terry Elo bars with symmetric CI.
Mandatory: every figure has axis labels with units, error bars (with description in caption), and a one-sentence rationale logged in manifest.json.
Cycle 2 — VLM Critique
For each figure, ask the VLM (per host: GPT-4o / Claude w/ vision / Gemini) the 10-point rubric:
- Is the primary message legible at thumbnail size?
- Is the color palette colorblind-safe (Okabe-Ito or Wong)?
- Are all axes labeled with units?
- Are error bars present and described in the caption?
- Is the legend placement optimal (inside or direct labels)?
- Is the font size legible at journal column width?
- Is the figure free of chartjunk (3D/gradient/shadow)?
- Are statistical annotations (p-values, n, CI) complete?
- Does the figure match the caption claim?
- Is the data-ink ratio maximized?
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.
- yesterday First seen · 104 lines · 85 tokens per session scan A f6adec8b3a7d
vedix-plotter is an agent published in the GitHub repository danilkotelnikov/vedix (3 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,446 once invoked, about $0.0004 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.
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