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 particle-swarm-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/particle-swarm-optimization)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/particle-swarm-optimization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/particle-swarm-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/particle-swarm-optimization"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/particle-swarm-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 | $0.00125 | $0.08588 |
| Opus 5 | $0.00063 | $0.04294 |
| Sonnet 5 | $0.00025 | $0.01718 |
| Haiku 4.5 | $0.00013 | $0.00859 |
Grade A, and why
particle-swarm-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 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 — 680 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Particle Swarm Optimization
You are an expert in particle swarm optimization (PSO) for continuous and combinatorial optimization. This skill covers the canonical velocity/position dynamics, inertia and constriction variants, swarm topologies, parameter selection, and discrete adaptations (random-key decoding and probability-based binary PSO). Use the framework below to implement, configure, and diagnose PSO, and to decide when PSO is the right tool versus an alternative population method.
Initial Assessment
Establish the following before writing any code:
- Search-space type. Continuous box-constrained vectors are PSO's native habitat. Permutations, subsets, and schedules require a decoder (random keys) or the binary variant. Identify which case applies — it changes the algorithm, not just a parameter.
- Dimension
dand bounds. Confirm explicit lower/upper bounds per variable. PSO needs them for initialization and velocity clamping. Note whether bounds are hard physical limits (must repair) or soft search-region hints. - Evaluation cost and total budget. Ask for the cost of one objective evaluation and
the budget (evaluation count or wall-clock). PSO spends
n_particlesevaluations per iteration; a 40-particle swarm over 1,000 iterations is 40,000 evaluations. - Batch evaluability. Determine whether the objective can evaluate an
(n_particles, d)matrix in one vectorized call. If yes, the whole swarm update is a handful of numpy operations and Python overhead disappears. - Multimodality. A roughly unimodal landscape favors gbest topology and aggressive convergence; a rugged landscape favors ring topology, restarts, and larger swarms.
- Constraints beyond bounds. Decide the handling strategy up front: penalty in the objective, repair after the position update, or a feasibility-enforcing decoder.
- Quality requirement. A good feasible solution within budget, or a near-optimal result that must beat a published baseline? The second demands tuning, multi-seed protocols, and a comparison method.
- Gradients. If the objective is smooth and gradients are available, multistart L-BFGS will usually beat PSO. Use PSO when gradients are absent, noisy, or misleading.
- Baseline. Always plan a sanity baseline: random search at equal budget, and differential evolution for continuous problems. PSO results without a baseline are uninterpretable.
- Reproducibility. Fix seeds (
np.random.default_rng(seed)), record every parameter, and plan at least 10-30 independent seeds for any reported comparison.
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 · 680 lines · 125 tokens per session scan A 3cb3e965169c
particle-swarm-optimization is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 8,588 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.
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