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 ShaishavMaisuria/research-paper-lifecycle-skills --skill prepare-artifactsgit clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-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/shaishavmaisuria/research-paper-lifecycle-skills/prepare-artifacts)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-artifacts"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-artifacts/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/prepare-artifacts"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-artifacts.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 | $0.00225 | $0.02521 |
| Opus 5.5 | $0.00090 | $0.01008 |
| Sonnet 5.5 | $0.00045 | $0.00504 |
| Haiku 4.5 | $0.00022 | $0.00252 |
Grade A, and why
prepare-artifacts 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prepare Artifacts
Turn a research codebase into a submittable, badge-ready reproducibility artifact. Artifact evaluation is a separate, post-acceptance track at most systems/PL/ML venues with its own deadline, its own appendix, and badges that change per venue per year — this skill builds the package (README, appendix, run instructions, anonymized repo, archival deposit guidance), produces an artifact-readiness checklist and a packaging plan, and lints the artifact directory for the bars reviewers actually check.
It does not run the author's experiments or claim a result reproduces — it prepares and checks the package, and tells the author exactly what reviewers will verify by hand.
When to use
- "My paper was accepted — how do I do the artifact evaluation / get a badge?"
- "Package / clean up my code for submission." / "anonymize my repo for review."
- "What's an artifact appendix / Artifacts Available / Functional / Reusable?"
- "Do I need a Zenodo DOI? concept vs version?" / "Software Heritage?"
- "Fill out the NeurIPS code/reproducibility or ACL repro checklist."
- "What does Reproduced vs Replicated mean for this badge?"
- Alongside
prepare-camera-ready(de-anonymization + final deposit overlap).
Inputs
- The artifact directory — the code/data repo to be packaged (path).
- The target venue + track, and ideally
venues/conferences/<v>-<year>.yml(supplies the review blind level; create withparse-cfpif missing). The venue profile does NOT encode the artifact track's badge offering or its separate deadline — those are fetched live (step 1). - The paper's major claims (for a per-claim reproduction plan) and whether the artifact is for review-phase (often double-blind) or the final deposit. These change everything (anonymized ZIP vs version DOI).
Process
- Fetch the venue's CURRENT Call for Artifacts — mandatory, live. Badge
offerings vary per venue per year (OSDI '26 evaluates ONLY "Artifacts
Available"; SOSP '26 offers all three). Memory and last year are stale by
construction; verify live. From the live CFA confirm: which
badges are offered this cycle, the separate artifact deadline, the
archival-hosting requirement, the appendix template/length, and the blind
model. Snapshots to start from (re-verify, don't trust):
references/venue-artifact-rails.md.
Record the chosen badge target + artifact deadline in
.paper-memory/decisions.md.
What ships with it
6 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 · 169 lines · 225 tokens per session scan A 15d9c4ba1e5b
prepare-artifacts 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 225 tokens to every session and 2,521 once invoked, about $0.0009 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
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.
quantitative-analysis
You must use this when selecting statistical tests, interpreting effect sizes, or conducting power analysis.
research-synthesis
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
literature-review
You must use this when synthesizing existing knowledge, identifying research gaps, or tracing the evolution of scientific ideas.