git-for-research-code

git-for-research-code is a skill for Claude Code, Codex from hajibabaie/combinatorial-optimization-skills. It costs 117 tokens per session (10,105 once invoked), scanned A, original, MIT.

A version-control workflow for optimization research code. It connects code changes, experiment results, solver logs, and paper tables to specific Git commits, which are saved versions of the code.

In plain words
What is it for?
Use it to plan research commits, tag code versions behind paper results, ignore generated files, record commit hashes with results, and manage risky refactors.
Why use it?
It makes reported results traceable and reproducible. You can identify exactly which code produced a result and keep temporary solver files out of the project history.

Skill for Claude CodeCodex

Part of the combinatorial-optimization plugin — 76 skills shipped together

Install

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.

agentmods
npx agentmods add skills/hajibabaie/combinatorial-optimization-skills/git-for-research-code
Any agent
npx skills add hajibabaie/combinatorial-optimization-skills --skill git-for-research-code
Clone the repo
git clone --depth 1 https://github.com/hajibabaie/combinatorial-optimization-skills

Made for: Claude Code, Codex.

Or install combinatorial-optimization, the plugin that ships this one along with the rest of its 76 skills.

Wrote 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.

agentmods badge for git-for-research-code

README.md
[![agentmods](https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/git-for-research-code.svg)](https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/git-for-research-code)
Your own site
<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/git-for-research-code"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/git-for-research-code.svg" alt="Measured on agentmods" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,105 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00117 $0.10105
Opus 5 $0.00059 $0.05053
Sonnet 5 $0.00023 $0.02021
Haiku 4.5 $0.00012 $0.01010

Measured 4d ago against content hash 97f88f3541f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

git-for-research-code 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 4d 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.

out = subprocess.run(
skills/git-for-research-code/SKILL.md · 781 lines

How it starts

The opening of the file, as written. The whole thing — 781 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Git for Research Code

You are an expert in version control for computational optimization research. This skill covers the solo-researcher git workflow: commit granularity tied to experiments, annotated tags that freeze the code state behind every paper table, ignore rules for solver logs and result artifacts, provenance stamping that links each result row to a commit hash, and branch strategies that make risky refactors safe. Use the pattern catalog below to make every reported number reproducible from a single hash.

Initial Assessment

Establish the following before recommending a workflow or writing tooling:

  • Repository status. Does a repo exist already? If yes, run git status and git count-objects -vH mentally through the user: is the worktree clean, and has the repo already been polluted with large result files or solver logs?
  • Team size. Solo PhD-style work, a 2-3 person lab project, or a larger team? Solo work permits rebase-based history cleanup and direct commits to main; shared remotes require merge discipline and protected branches.
  • Artifact inventory. What does a run produce? Typical optimization artifacts: result CSV/parquet tables, solver logs (.log), model files (.lp, .mps), solution files (.sol), checkpoints, convergence traces, figures. Each class needs an explicit track/ignore decision.
  • Instance data. Are benchmark instances small text files (TSPLIB-style, fine to commit), large binaries (need LFS or an external store plus checksums), or licensed data that must never enter a public repo?
  • Experiment cadence. How often does the user change code and re-run? Daily parameter sweeps need a low-friction stamp-and-run loop; monthly campaign-style runs justify heavier manifests.
  • Paper pipeline. Which deliverables consume results - LaTeX tables, figures, a results section? Every camera-ready number should trace back to a tag.
  • Reproducibility horizon. Will results need re-running at revision time (6-12 months later)? If yes, environment capture (Python and package versions) must ride along with the git hash.
  • Solver licensing. Gurobi license files (gurobi.lic), WLS credentials, and cluster SSH keys must be ignored and scanned for before any push to a public remote.
  • Remote and backup. Is there a remote (GitHub/GitLab/institute server)? A repo with no remote is one disk failure away from losing the entire provenance chain.
  • Existing damage. If history already contains multi-GB results or secrets, plan a history rewrite (git filter-repo) before adopting the clean workflow, not after.
  • Tooling constraints. Python version, OS (path separators in hooks), whether the user can install the pre-commit framework or needs plain .git/hooks scripts.

Read the full file on GitHub · 781 lines

Changes

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.

  1. 4d ago First seen · 781 lines · 117 tokens per session scan A 97f88f3541f9

Subscribe to this mod's changes

git-for-research-code is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 117 tokens to every session and 10,105 once invoked, about $0.0006 per session on Opus 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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