Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-skillsnpx agentmods add skills/shaishavmaisuria/research-paper-lifecycle-skills/test-research-codeWrote 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/shaishavmaisuria/research-paper-lifecycle-skills/test-research-code)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/test-research-code"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/test-research-code/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/shaishavmaisuria/research-paper-lifecycle-skills/test-research-code"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/test-research-code.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 | $0.00063 | $0.02325 |
| Opus 5.5 | $0.00025 | $0.00930 |
| Sonnet 5.5 | $0.00013 | $0.00465 |
| Haiku 4.5 | $0.00006 | $0.00232 |
Grade A, and why
test-research-code 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 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.
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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Research Code
Brings a research code artifact to the state an artifact-evaluation committee (ACM AE, USENIX/OSDI/SOSP, SIGMOD ARI, ETAPS, NeurIPS code release) expects: it runs, it is deterministic enough to reproduce within tolerance, its environment is captured, and a stranger can follow the README to the paper's main result. This is the code-side counterpart to preflight-check for the PDF.
It is not generic software TDD. The goal is one reliable end-to-end "does it run and roughly reproduce" path, not exhaustive unit coverage. A focused smoke test that exercises the real pipeline on a tiny input is worth more than 100 mocked unit tests.
When to use
- The code is "works on my machine" and needs to become runnable by a stranger — no pinned env, no seeds, no obvious entrypoint, no sanity test.
- The author asks to add a smoke/sanity test, pin seeds / make it deterministic, or capture/pin the environment before they package or hand it off.
- An artifact-evaluation deadline is approaching and the code is not yet in testable shape — do this engineering first, then route to the siblings below.
Boundary (avoid overlap). This skill is the engineering step — tests, seeds, env capture. It does not own:
- Packaging, badge taxonomy, archival DOI, anonymization →
prepare-artifacts. - Whether the produced numbers actually match the paper's tables →
verify-results. - General code cleanup/refactor →
refactor-research-code. When the request is "get my code ready for the artifact track," start here for the run-ability gaps, then hand off; don't re-do their work.
Inputs
- A path to the research-code directory (the repo or a subfolder with training/eval scripts, etc.).
- Optionally, whether review is double-blind (so a captured env / added test does not leak author identity).
- Optionally
.paper-memory/profile.ymlfor positioning (asystem/datasetcontribution leans harder on a reusable, well-tested artifact; anempiricalone on deterministic re-runs).
What ships with it
4 files 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.
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 · 89 lines · 63 tokens per session scan A e02d53b02da6
test-research-code is a skill published in the GitHub repository ShaishavMaisuria/research-paper-lifecycle-skills (55 stars, last pushed 3d ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,325 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
research-manager
Use this when starting a new research project or managing a complex, multi-step research workflow.
academic-writing
You must use this when producing any research prose — literature reviews, syntheses, analyses, methodology descriptions, discussion sections, abstracts, or any written output intended for an academic audience.
systematic-review
You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.
hypothesis-testing
You must use this when formulating testable hypotheses, designing experimental controls, or defining falsification criteria.
multi-source-investigation
You must use this when investigating complex claims across diverse sources or fact-checking contradictory information.
peer-review
You must use this when critiquing academic manuscripts, evaluating methodological rigor, or providing structured reviewer feedback.