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optskills

optskills is a skill for Claude Code, Codex from VeryMath/AI4Math-Optimization. It costs 51 tokens per session (689 once invoked), scanned A, original, MIT.

A library of problem-solving patterns for natural-language operations-research problems, such as choosing variables, objectives, constraints, and assumptions.

In plain words
What is it for?
Use it to select a suitable problem card for planning, scheduling, allocation, or other optimization tasks, or to check whether the library is up to date.
Why use it?
It helps match a structured decision problem to a released solution pattern instead of designing from scratch. Operations research uses mathematical models to choose the best option under constraints.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 11repo +1 changed 1mo ago A scan 51 tokens original MIT

Good fit Use it to select a suitable problem card for planning, scheduling, allocation, or other optimization tasks, or to check whether the library is up to date.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/verymath/ai4math-optimization/optskills
View source ↗ VeryMath/AI4Math-Optimization
Install

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.

Any agent
npx skills add VeryMath/AI4Math-Optimization --skill optskills
Clone the repo
git clone --depth 1 https://github.com/VeryMath/AI4Math-Optimization

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for optskills

README.md
[![agentmods](https://agentmods.dev/badge/skills/verymath/ai4math-optimization/optskills/github.svg)](https://agentmods.dev/skills/verymath/ai4math-optimization/optskills)
Your own site
<a href="https://agentmods.dev/skills/verymath/ai4math-optimization/optskills"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/optskills/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.

agentmods 80×15 button for optskills

Your own site · 80×15
<a href="https://agentmods.dev/skills/verymath/ai4math-optimization/optskills"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/optskills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 689 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.00689
Opus 5.5 $0.00020 $0.00276
Sonnet 5.5 $0.00010 $0.00138
Haiku 4.5 $0.00005 $0.00069

Measured 1mo ago against content hash d5da9e1326bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

optskills 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.

skills/optskills/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OptSkills

Use this package independently for problem modeling. It does not require the OptSkills training, agent, chat, or embedding system. Solver environment setup can use the separate or-solver skill as described in step 6.

Lightweight version check

At the start of every normal use, read only the Upstream snapshot commit line from SOURCES.md and run git ls-remote https://github.com/fujiwaranoM0kou/OptSkills.git HEAD. Compare only those two commits; do not fetch or inspect upstream cards. If they match, continue silently. If they differ or the lookup fails, give at most one short note and continue with the packaged cards without retrying. Read UPDATE.md and inspect upstream content only when the user asks to update.

Normal use

  1. Restate the user's sets, parameters, decision variables, objective, constraints, units, and assumptions.
  2. Read skill_library/index.json. Compare the problem structure with each entry's name and description, then select one to three candidates.
  3. Read only the selected files using their relative path fields. Never invent a path from a skill ID.
  4. Choose the closest card by variables, objective, and constraint structure. If none fits, say that no released card matched and continue with general mathematical modeling without claiming a card was used.
  5. Apply the card to the user's actual data. Card examples are guidance, not proof that the new model is correct.
  6. Choose the solver in this modeling task, honoring the user's choice, model requirements, and actual library and license availability. If the chosen solver needs installation or configuration, use or-solver when available (in this repository: ../or-solver/SKILL.md). Pass the specified solver, interface, any version requirements, target Python environment, and observed setup error. That skill only configures the named target; solver choice and solving remain here. Resume after setup, using only libraries and licenses actually available. If setup support is unavailable or fails, explain the missing conditions and continue with work the current environment supports. Ask before installing dependencies unless the user has already authorized it.
  7. Check solver status before reading values. Recompute the objective and the problem's key constraints whenever execution is possible.
  8. Report separately: selected card, completed model, solver execution, constraint checks, and unresolved limitations.

Read the full file on GitHub · 62 lines

Files

What ships with it

60 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.

Changes

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.

  1. 1mo ago Changed · +8 lines d5da9e1326bc
  2. 1mo ago First seen · 54 lines · 51 tokens per session scan A 1ae1469a706e

Subscribe to this mod's changes

optskills is a skill published in the GitHub repository VeryMath/AI4Math-Optimization (11 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 689 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-04.

OptimizationPlanningai4mathcdoptcoding-agentsmathematicsskills
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