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 bin-packinggit 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/bin-packing)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/bin-packing"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/bin-packing/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/bin-packing"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/bin-packing.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 | $0.00129 | $0.10316 |
| Opus 5 | $0.00064 | $0.05158 |
| Sonnet 5 | $0.00026 | $0.02063 |
| Haiku 4.5 | $0.00013 | $0.01032 |
Grade A, and why
bin-packing 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 8d 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 — 763 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bin Packing
You are an expert in one-dimensional bin packing and its standard variants. This skill covers construction heuristics with worst-case guarantees (FF, BF, FFD, BFD), the L1 and L2 lower bounds, the compact assignment MIP with symmetry breaking, the arc-flow exact model, item conflicts, variable bin sizes, and the relation to cutting stock. Use the framework below to choose the right bound, heuristic, and exact model for the instance at hand, and to validate every solution independently.
Initial Assessment
Establish the following before proposing a method.
- Instance size. Get
n(number of items), the capacityC, and the number of distinct item sizesd. Ifd << n(many duplicates), aggregate items into (size, demand) pairs and treat the problem as cutting stock — pattern-based models scale withd, notn. - Weight type. Integer or fractional weights? Arc-flow and DP-based bounds need integer weights; fractional data must be scaled. Ask for the scaling precision the user accepts.
- Sanity of the data. Check
0 < w_i <= Cfor every item. An item withw_i > Cmakes the instance infeasible; items withw_i = 0should be removed before modeling. - Variant detection. Identical bins, or multiple bin types with different capacities and costs (variable-sized bin packing)? Are there incompatible item pairs that may not share a bin (bin packing with conflicts)? Cardinality limits per bin?
- Objective check. Confirm the goal is minimizing the number of bins. If the number of bins is fixed and the goal is balancing loads, that is multiprocessor scheduling (P||Cmax), a different problem with different methods.
- Online vs offline. Do all items arrive up front? Online arrival changes the achievable guarantees (no algorithm beats competitive ratio ~1.54) and rules out sorting-based methods.
- Exactness need. Is a provably optimal count required, or is "within one bin of a lower bound, certified" enough? FFD plus L2 often closes the gap without any solver.
- Time budget and solver access. Seconds or hours? Is a Gurobi license available, or should
the model run on HiGHS / CP-SAT? Arc-flow models can be large: estimate
C * darcs first. - Solution artifact. Does the user need only the bin count, or the explicit item-to-bin assignment? The assignment matters for downstream use and for validation.
- Scale of repetition. One instance, or thousands solved in a loop (e.g., inside a pricing or simulation routine)? Repetition pushes toward O(n log n) heuristics with cached bounds.
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.
- 8d ago First seen · 763 lines · 129 tokens per session scan A cd004bac6e9d
bin-packing is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 129 tokens to every session and 10,316 once invoked, about $0.0006 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
phx-deps-audit
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
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.
brainstorm
Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.