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 hajibabaie/combinatorial-optimization-skills --skill matplotlib-optimization-visualizationgit clone --depth 1 https://github.com/hajibabaie/combinatorial-optimization-skillsWrote 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/hajibabaie/combinatorial-optimization-skills/matplotlib-optimization-visualization)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/matplotlib-optimization-visualization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/matplotlib-optimization-visualization/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/hajibabaie/combinatorial-optimization-skills/matplotlib-optimization-visualization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/matplotlib-optimization-visualization.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 | $0.00000 | $0.09890 |
| Opus 5.5 | $0.00000 | $0.03956 |
| Sonnet 5.5 | $0.00000 | $0.01978 |
| Haiku 4.5 | $0.00000 | $0.00989 |
Grade A, and why
matplotlib-optimization-visualization 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.
How it starts
The opening of the file, as written. The whole thing — 626 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Matplotlib Visualization for Optimization
You are an expert in scientific visualization for operations research. This skill covers the standard figure types of computational optimization papers — convergence curves, Gantt charts, route plots, Pareto fronts, and performance profiles — plus the publication-quality mechanics (vector output, font matching, column-width sizing) that journals and conferences require. Use the pattern catalog below: each pattern gives the motivation, a complete implementation, and the pitfall that most often ruins the figure.
Initial Assessment
Before producing any figure, establish:
- Venue and column geometry. Single-column (~3.3–3.5 in) or double-column (~7 in) figure? IEEE, INFORMS, Springer, and Elsevier column widths differ; the figure must be designed at its final printed width.
- Target format. PDF or EPS vector for the paper; PNG only for previews, slides, or raster-heavy panels. Some journals still require EPS or TIFF — confirm before styling.
- Data shape. Is the experiment data already in tidy form (one row per run, columns for instance, algorithm, seed, time, objective)? If not, fix the table first — plotting code should never reshape ad hoc.
- What the figure must argue. Anytime behavior (convergence plot), final quality distribution (box/strip plot), robustness across instances (performance profile), structural correctness (route/Gantt plot), or trade-offs (Pareto plot). One claim per figure.
- Minimization or maximization. Determines
np.minimum.accumulatevsnp.maximum.accumulate, axis direction, and which corner of a Pareto plot is "good." - Number of seeds and instances. Fewer than ~5 seeds: plot individual runs, not a band. Many instances: aggregate with performance profiles, not 50 separate convergence plots.
- Time axis semantics. Wall-clock seconds, CPU seconds, or evaluation count? Mixed hardware makes wall-clock comparisons unfair; evaluation counts hide per-iteration cost differences. State the choice on the axis label.
- Scale needs. Objectives spanning orders of magnitude, or late-stage differences of <1%, need log axes or gap-to-best transforms decided up front.
- Color constraints. Will reviewers print in grayscale? Is colorblind safety required (it should be)? Fix a palette before the first figure so the whole paper is consistent.
- Reproducibility requirement. Every paper figure should be regenerated by one script from one results file. Plan
scripts/make_figures.py+results/*.csv+figures/from the start.
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 · 626 lines · 0 tokens per session scan A 86c0cb381349
matplotlib-optimization-visualization is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (14 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 9,890 tokens. 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
crossing-the-chasm
Navigate the technology adoption lifecycle from early adopters to mainstream market. Use when the user mentions "crossing the chasm", "beachhead segment", "whole product", "early adopters vs mainstream", "tech go-to-market", "bowling pin strategy", "technology adoption lifecycle", "pragmatist buyers", "growth stalled…
design-everyday-things
Apply foundational design principles: affordances, signifiers, constraints, feedback, and conceptual models. Use when the user mentions "why is this confusing", "affordance", "error prevention", "discoverability", "human-centered design", "mental model", "mapping", "seven stages of action", "users keep making…
traction-eos
Implement the Entrepreneurial Operating System (EOS) to align vision and execution across a company. Use when the user mentions "EOS", "Entrepreneurial Operating System", "V/TO", "quarterly rocks", "Level 10 meetings", "accountability chart", "IDS process", "my company feels chaotic", "we keep having the same…
analyze
Analyze a legacy app in any language before rewriting it in Elixir: inventory, features as goals, why each mechanism exists, runtime topology, external contracts. Use to start or refresh a rewrite.
release
CONTRIBUTOR TOOL - Cut a plugin release: bump plugin.json version, finalize CHANGELOG, update README if needed, gate on make ci, commit, tag vX.Y.Z, and create the GitHub release. Use when shipping a new plugin version. NOT distributed.
watch-pr
Watch an Elixir/Phoenix PR for new review comments (bot + human) and CI results via a background watcher that wakes Claude only on real events. Use after opening a PR or pushing, while waiting on CI or reviewers.