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 memetic-algorithmsgit 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/memetic-algorithms)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/memetic-algorithms"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/memetic-algorithms.svg" alt="Measured on agentmods" 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 | $0.00131 | $0.11093 |
| Opus 5 | $0.00066 | $0.05547 |
| Sonnet 5 | $0.00026 | $0.02219 |
| Haiku 4.5 | $0.00013 | $0.01109 |
Grade A, and why
memetic-algorithms 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 — 715 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memetic Algorithms
You are an expert in memetic algorithms (MAs): population-based metaheuristics that hybridize a genetic algorithm with local search so that every individual in the population is a local optimum (or near one). This skill covers the canonical MA loop, Lamarckian vs Baldwinian learning, budgeting local-search frequency and depth, restart management, and keeping a population diverse when strong local search keeps collapsing it. Use the framework below to design, implement, and tune an MA, with complete worked implementations for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP).
Initial Assessment
Establish the following before designing, implementing, or debugging a memetic algorithm:
- Problem class and encoding. Permutation (TSP, QAP, flow shop), binary selection (knapsack, set covering), integer assignment, or decoder-based? The encoding fixes which crossover, mutation, and neighborhood moves are legal. For representation choice see solution-encodings; for operator catalogs see crossover-operators and mutation-and-perturbation-operators.
- Local-search ingredients. Does a neighborhood with delta (incremental) evaluation already exist? What is the cost of one full descent in evaluations and milliseconds? An MA without fast delta evaluation is usually a mistake — fix that first (see local-search-and-neighborhoods and fitness-evaluation-and-caching).
- Evaluation budget. Wall-clock limit, evaluation-count limit, or both? Expect local search to consume 90%+ of all evaluations; the budget split between evolution and learning is the central design decision.
- Lamarckian feasibility. Can an improved phenotype be written back into the genotype? Direct encodings: yes. Decoder-based encodings (random keys, priority rules): often no — the improved schedule may have no preimage, which forces Baldwinian or repair-style designs.
- Constraint handling. Are all neighborhood moves feasibility-preserving, or do you need repair after crossover/mutation? Decide where infeasibility is allowed to exist (never, only pre-repair, or penalized throughout).
- Quality requirement. Gap to best-known values on benchmarks, or "good solution in 5 minutes"? MAs are the state of the art for QAP and TSP benchmarks but are heavier machinery than iterated local search (ILS).
- Baselines. Has anyone run plain GA, multistart local search, or ILS on this problem with the same budget? An MA must beat its own components, or the hybrid is not earning its complexity.
- Instance scale and protocol. Sizes (n), number of instances, tuning/test split, number of seeds per configuration.
- Reproducibility. Single
np.random.default_rng(seed)per run; seed recorded with every result row. - Compute model. Pure numpy on one core, multiprocessing for parallel descents, or batch (vectorized) fitness evaluation?
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 · 715 lines · 131 tokens per session scan A b6e2e8d0a9d1
memetic-algorithms is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 131 tokens to every session and 11,093 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
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.