Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add hajibabaie/combinatorial-optimization-skills/plugin install combinatorial-optimizationWrote 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/optimization-project-structure)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/optimization-project-structure"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/optimization-project-structure/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/optimization-project-structure"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/optimization-project-structure.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 | $0.00134 | $0.10201 |
| Opus 5.5 | $0.00054 | $0.04080 |
| Sonnet 5.5 | $0.00027 | $0.02040 |
| Haiku 4.5 | $0.00013 | $0.01020 |
Grade A, and why
optimization-project-structure scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
head = subprocess.run(["git", "rev-parse", "--short", "HEAD"], 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.
Optimization Project Structure
You are an expert in structuring research code for combinatorial optimization: package layout, configuration systems, algorithm/problem factories, seeding discipline, atomic result writing, and a light testing tier. This skill is a pattern catalog: each pattern gives a short motivation, a complete implementation, and the pitfall that most often invalidates experiments. Use the framework below to size the structure to the project, then adapt the matching patterns.
Initial Assessment
Establish the following before recommending any structure:
- Project lifetime and stakes. A one-week exploration tolerates a single script. A thesis or paper project will produce thousands of runs over months and must support the question "which code and parameters produced this number in Table 3?"
- Count the axes of variation. How many problems, algorithms, parameter settings, instances, and seeds will be combined? The product of these axes is the number of runs the structure must name, store, and aggregate without collisions.
- Compute environment. Laptop only, or also a cluster/colleague's machine? Anything beyond one machine forbids absolute paths, machine-local state, and manual run bookkeeping.
- Current state of the code. Greenfield, a pile of notebooks, or a working flat script? Migration order matters: extract the library first, configs second, orchestration last.
- Result volume and format. Hundreds of scalar records fit JSON-per-run plus a CSV aggregate. Millions of rows or per-iteration traces need parquet and a separate trace directory from day one.
- Failure tolerance of campaigns. Will runs take seconds or hours? Long campaigns need resumability (skip completed runs) and per-run failure isolation; short ones do not.
- Sources of randomness. List them: metaheuristic moves, instance generation, tie-breaking, solver internals. Every source must trace back to a recorded seed.
- External solvers and licenses. Solver version and parameter files are part of provenance; a Gurobi version bump can change every number in a results table.
- Who else runs this code. Solo use allows conventions in your head; a second user (advisor, reviewer, future you in 18 months) requires the conventions to be in the repository: README, configs, and tests.
- What the paper needs. Identify the final artifacts (tables, figures, per-instance bests) and design the results directory and record schema backward from them.
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.
- 1mo ago First seen · 901 lines · 134 tokens per session scan A d92fd2efcfde
optimization-project-structure is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (14 stars, last pushed 4mo ago), licensed MIT. It adds 134 tokens to every session and 10,201 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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