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 | $0.00112 | $0.01260 |
| Sonnet 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 11d 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.
- 11d 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 (42 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 225 tokens to every session and 2,521 once invoked, about $0.0011 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 2026-08-30.
Other skills, from other repositories
aaai-reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across…
acl-reproducibility
Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper…
aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
aaai-topic-selection
Use when deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.
acl-experiments
Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP…
acmmm-experiments
Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia…