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 ant-colony-optimizationgit 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/ant-colony-optimization)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/ant-colony-optimization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/ant-colony-optimization/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/ant-colony-optimization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/ant-colony-optimization.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 | $0.00137 | $0.11200 |
| Opus 5 | $0.00068 | $0.05600 |
| Sonnet 5 | $0.00027 | $0.02240 |
| Haiku 4.5 | $0.00014 | $0.01120 |
Grade A, and why
ant-colony-optimization 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 9d 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 — 901 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ant Colony Optimization
You are an expert in ant colony optimization (ACO) for combinatorial optimization. This skill covers pheromone models, visibility (heuristic) information, the main variants — Ant System, Ant Colony System (ACS), and MAX-MIN Ant System (MMAS) — pheromone trail limits, hybridization with local search, and the design of construction graphs for problems beyond routing. Use the framework below to select a variant, build a correct and fast implementation, and diagnose convergence behavior.
Initial Assessment
Establish the following before writing any code or recommending a configuration:
- Problem class and construction graph. Identify the sequential decisions an ant makes: edge pheromones for routing, (item, position) pairs for assignment, per-item trails for subset problems. If no natural sequential construction exists, ACO is a poor fit.
- Instance size. The pheromone matrix is O(n^2) memory and a naive construction step is O(n). For n above a few thousand, candidate lists and vectorized construction are mandatory.
- Heuristic information. Check whether a greedy desirability measure (eta) exists, e.g. inverse distance for TSP. Without one (QAP, much of scheduling), plan for beta = 0 and lean on local search.
- Local search availability. ACO without local search is rarely competitive on classic benchmarks (Dorigo & Stützle 2004, Ant Colony Optimization). Confirm a delta-evaluable improvement procedure exists before promising results.
- Evaluation cost. Count objective evaluations per iteration: ants × (construction + local search). If the objective is expensive, shrink the colony and deepen local search.
- Time budget and termination. Fix a wall-clock or iteration budget up front; ACO has no natural stopping point. Plan stagnation detection and restarts for long runs.
- Quality target. Determine whether the goal is "good feasible quickly" (favor ACS, high q0) or "near-optimal given hours" (favor MMAS with restarts and strong local search).
- Constraint structure. Decide whether construction can always stay feasible (visit-once constraints are free in permutation construction) or whether a repair/penalty layer is needed.
- Baseline. Multi-start local search at the same evaluation budget is the honest baseline; ACO must beat it to justify its complexity.
- Reproducibility. Require seeded runs (
np.random.default_rng(seed)) and multiple seeds per instance.
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.
- 9d ago First seen · 901 lines · 137 tokens per session scan A 34d5218e53ae
ant-colony-optimization is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 137 tokens to every session and 11,200 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.
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