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-camera-readygit 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-camera-ready)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-camera-ready"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-camera-ready/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-camera-ready"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/prepare-camera-ready.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 | $0.00223 | $0.02582 |
| Opus 5 | $0.00112 | $0.01291 |
| Sonnet 5 | $0.00045 | $0.00516 |
| Haiku 4.5 | $0.00022 | $0.00258 |
Grade A, and why
prepare-camera-ready 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 12d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prepare Camera-Ready
Walk an accepted paper through the camera-ready pipeline. The rules are tribal knowledge split across two big rails (ACM: eRights → ORCID → rights/DOI block → TAPS; IEEE: PDF eXpress → file naming → eCF → registration) plus the OpenReview-direct rail used by ML venues, and they change every cycle. This skill turns the venue profile into an ordered checklist, walks each form and upload with the user, and lints the final source for the mistakes that delay or block publication — but the user does every irreversible click.
When to use
- "My paper was accepted at SIGSPATIAL / ICDE / NeurIPS — what now?"
- "Help me with the camera-ready / final version."
- "What is this eRights email / TAPS link / PDF eXpress conference ID / eCF?"
- "Do all my coauthors really need ORCIDs?" / "Where does the DOI block go?"
- "How many pages do I get for the camera-ready?" / "How do I de-anonymize?"
- After
write-rebuttalsucceeds; beforemake-slides/ talk prep.
Inputs
- The acceptance email (ask the user to paste the camera-ready instructions from it — they override everything else).
- The venue profile:
venues/conferences/<venue>-<year>.yml(schema invenues/schema.yml; family files supply rail defaults). If missing, create one withparse-cfpfirst. - The final
.texsource (the file with\documentclass) once editing starts; a compiled.log/.pdfnext to it enables the page-count check.
Process
-
Resolve the rail and generate the checklist. Find the profile, ask which track the paper was accepted to (camera-ready deadlines and page limits differ per track), then run:
python3 scripts/camera_ready_checklist.py venues/conferences/<venue>-<year>.yml --track "<track>"It merges the family profile, resolves the rail (
acm-taps,ieee-pdfexpress,openreview-direct, or other), and prints the ordered steps, the venue-specific requirements, the deadline, and the extra-page rule.--jsonfor machine-readable output.
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.
- 12d ago First seen · 177 lines · 223 tokens per session scan A 8d01495c7d52
prepare-camera-ready 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 223 tokens to every session and 2,582 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.
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aaai-submission
Use when auditing an AAAI main technical track submission for OpenReview readiness, double-blind anonymity, page limits, reproducibility checklist, supplementary material, author limits, multiple-submission policy, and AAAI AI-use policy compliance.
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-author-response
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acl-camera-ready
Use when preparing an accepted ACL main-conference or Findings paper for camera-ready, covering the extra content page, de-anonymization and acknowledgements, AI-assistance disclosure, keeping the Limitations section, ACL Anthology metadata and CC BY 4.0 publication, meta-review-driven edits, and presentation-mode…
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…