research-code-parsimony

research-code-parsimony is a skill for Codex from uchicago-dsi/ai-sci-skills. It costs 59 tokens per session (980 once invoked), scanned A, original, MIT.

A coding guide for keeping research implementations focused and avoiding duplicate code. It asks you to identify the requested scientific behavior, its existing owner and callers, and available library support before adding anything.

In plain words
What is it for?
Use it when writing, extending, or reviewing research code. It helps decide where behavior belongs, whether to reuse existing modules or dependencies, and when to create a new owner.
Why use it?
It reduces unnecessary code and prevents parallel implementations that are harder to maintain. It also helps preserve the reproducibility and full scope of research work.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it when writing, extending, or reviewing research code. It helps decide where behavior belongs, whether to reuse existing modules or dependencies, and when to create a new owner.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uchicago-dsi/ai-sci-skills/research-code-parsimony
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.

Any agent
npx skills add uchicago-dsi/ai-sci-skills --skill research-code-parsimony
Clone the repo
git clone --depth 1 https://github.com/uchicago-dsi/ai-sci-skills

Made for: Codex.

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 research-code-parsimony

README.md
[![agentmods](https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony/github.svg)](https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony)
Your own site
<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony/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.

agentmods 80×15 button for research-code-parsimony

Your own site · 80×15
<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/research-code-parsimony.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 980 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00059 $0.00980
Opus 5 $0.00030 $0.00490
Sonnet 5 $0.00012 $0.00196
Haiku 4.5 $0.00006 $0.00098

Measured 4d ago against content hash b5c139a9a53c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

research-code-parsimony 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 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.

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.

skills/research-code-parsimony/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Research Code Parsimony

Establish The Contract And The Owner First

Before adding code, be able to state:

  • the behavior or scientific contract being requested, including what must stay reproducible;
  • the module, function, script, or config that already owns this behavior, and which callers actually exercise it;
  • whether a maintained dependency or a native language/library capability already provides it.

Search before creating an owner. If none fits a genuinely new capability, create one clear owner rather than forcing unrelated behavior into an existing module.

Reuse Before You Add

  • Extend an understood existing owner instead of introducing a parallel one.
  • Prefer a maintained dependency when it reduces ownership burden while meeting the scientific and operational contract. Check the standard library and present dependencies first.
  • Parsimony means less code to own — not fewest files, shortest diff, or clever one-liners. Readable, explicit code beats a compressed version.
  • Never satisfy the request by solving a smaller or easier scientific problem than the one asked for.

Express Cohesive Families As Directories

Prefer a meaningful package hierarchy over a flat directory of long, repeated-prefix filenames. When several modules belong to one scientific or contract family, let the directory carry that context and give the modules short role names: for example, training/concentration_field/diffusion/runtime.py rather than training/concentration_field_diffusion_runtime.py. A separate training/physics_field/direct_inverse/ can own its own data, objective, and QC modules; genuinely shared training infrastructure stays at the shared level. Group by cohesive ownership, not chronology, and add depth only when it makes navigation and responsibilities clearer. Do not create speculative package trees or duplicate a family merely to achieve symmetry.

Apply this preference when choosing a new owner's home. Existing flat families can move in a bounded, authorized pass coordinated with their current owners; this preference does not authorize reorganizing active work during another task. Leave queued/running execution checkouts and immutable run artifacts untouched. Move live imports, entrypoints, config references, and hashed execution declarations together, updating valid source pins according to local policy. Validate the affected execution paths and remove old module routes without compatibility aliases. Completed runs retain their producing layout through their pinned commits, not duplicate source at HEAD.

Read the full file on GitHub · 83 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 83 lines · 59 tokens per session scan A b5c139a9a53c

Subscribe to this mod's changes

research-code-parsimony is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (17 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 980 once invoked, about $0.0003 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 2026-09-05.

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