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 cutting-stockgit 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/cutting-stock)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/cutting-stock"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/cutting-stock/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/cutting-stock"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/cutting-stock.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 | $0.00134 | $0.10440 |
| Opus 5 | $0.00067 | $0.05220 |
| Sonnet 5 | $0.00027 | $0.02088 |
| Haiku 4.5 | $0.00013 | $0.01044 |
Grade A, and why
cutting-stock 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 10d 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 — 624 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cutting Stock
You are an expert in the one-dimensional cutting stock problem (1D-CSP): the canonical pattern-based optimization problem and the original application of column generation (Gilmore & Gomory, 1961, "A linear programming approach to the cutting-stock problem"). This skill covers compact and pattern-based formulations, column generation with bounded-knapsack pricing, integer rounding strategies, trim-loss objectives, and supporting tooling (instance generation, independent validation, a metaheuristic baseline). Use the framework below to pick the right model for the instance size, get a provably good solution, and verify it independently.
Initial Assessment
Establish these facts before proposing a model or writing code:
- Instance dimensions. Number of distinct item widths
m, stock lengthL, and demand magnitudesd_i.m ≤ 15with smallLmay allow full pattern enumeration;min the hundreds with demands in the thousands is standard column-generation territory. - Width data type. Integer widths enable pseudo-polynomial knapsack pricing and arc-flow models. Fractional widths must be scaled to integers — ask for the measurement precision (mm, 0.1 mm) and check the scaled
Lstays manageable. - Stock assortment. One stock length or several? Multiple lengths change the master objective (cost per stock type) and require one pricing problem per length.
- Objective. Minimize number of rolls, total trim loss, or material cost? With identical rolls these align, but only under a stated overproduction policy — confirm whether cutting more pieces than demanded is waste, usable inventory, or forbidden.
- Demand semantics. Cover-at-least (
≥ d_i, the default, gives nonnegative duals and a clean pricing problem) or meet-exactly (= d_i, harder: duals can be negative, master can be infeasible with few columns)? - Side constraints. Maximum number of distinct patterns (setup costs), maximum pieces per pattern (knife count), pattern run-length limits, due dates per order. These decide between vanilla Gilmore-Gomory and an extension.
- Optimality requirement. Is
ceil(LP bound)or+1roll acceptable (almost always reached by rounding heuristics), or is a proven optimum required (branch-and-price or arc-flow)? - Solver availability. Gurobi licensed? If not, the same pattern applies with HiGHS/CBC for the master and a hand-written DP for pricing — only the master LP/IP calls change.
- Time budget. Column generation on
m ≤ 200converges in seconds; a compact MIP on the same instance may not finish in hours because of symmetry. - Validation path. Agree up front on an independent feasibility checker (pattern widths, demand coverage) so the model and the check do not share code.
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.
- 10d ago First seen · 624 lines · 134 tokens per session scan A 42b389cf9444
cutting-stock is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 10,440 once invoked, about $0.0007 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.
Other skills, from other repositories
learn-from-fix
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phx-deps-audit
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promote
Generate X/Twitter release promotion posts with ASCII tables and CodeSnap rendering. Use when writing release posts, promotion tweets, plugin announcements, or preparing social media content for new versions.
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.
session-deep-dive
Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.
catchup
Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.