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 constraint-handling-techniquesgit 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/constraint-handling-techniques)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/constraint-handling-techniques"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/constraint-handling-techniques/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/constraint-handling-techniques"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/constraint-handling-techniques.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 | $0.00121 | $0.11578 |
| Opus 5 | $0.00060 | $0.05789 |
| Sonnet 5 | $0.00024 | $0.02316 |
| Haiku 4.5 | $0.00012 | $0.01158 |
Grade A, and why
constraint-handling-techniques 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 — 768 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Constraint-Handling Techniques
You are an expert in constraint handling for metaheuristics and evolutionary computation. This skill catalogs the six main technique families — penalty functions (static, dynamic, adaptive), repair operators, feasibility-preserving operators, decoder-based feasibility, stochastic ranking, and Deb's feasibility rules — with numpy implementations, complexity notes, and per-constraint-type selection guidance. Use the framework below to pick a technique per constraint, implement it correctly, and verify the choice empirically with a head-to-head experiment.
Initial Assessment
Establish these facts before recommending any technique:
- Constraint inventory. List every constraint. For each: inequality or equality? Linear or black-box? How many?
- Hard vs soft. Hard constraints define feasibility; soft constraints are preferences. Soft constraints belong in the objective (weighted or lexicographic), never in a feasibility mechanism. Confirm the user agrees on the split.
- Feasible-region density. Sample random solutions: what fraction is feasible? Above ~10%, penalties and feasibility rules work out of the box. Below ~0.1%, you need repair, decoders, or feasibility-preserving operators — random search will never find the feasible region.
- Constraint structure. Is feasibility cheap to check (O(n) capacity sums) or expensive (a simulation)? Cheap checks enable repair and move filtering; expensive checks favor penalties on cached violation values.
- Representation already chosen? If the encoding is still open, the cheapest fix is to encode constraints away (permutation encoding for "visit each once", fixed-cardinality sets for "choose exactly k"). See solution-encodings before adding machinery here.
- Algorithm family. Population methods (GA, DE, EDA) can rank by violation across a population; single-solution methods (SA, tabu, ILS) need per-move decisions — repair, move filtering, or a penalized delta.
- Equality constraints present? Penalties handle equalities poorly (the feasible set has measure zero). Plan for reformulation, decoders, or projection-style repair.
- Where does the optimum live? For most resource-constrained problems the optimum sits on the feasibility boundary (Michalewicz & Schoenauer 1996). Techniques that cannot search near or across the boundary lose quality.
- Evaluation budget and time limit. Repair and decoding add per-individual cost; confirm the budget tolerates it.
- Validation hook. Confirm an independent feasibility checker exists (separate from the fitness code) so the chosen technique can be audited — see solution-validation-testing.
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 · 768 lines · 121 tokens per session scan A f3bc38957dce
constraint-handling-techniques is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 11,578 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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