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 tailor-to-venuegit 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/tailor-to-venue)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/tailor-to-venue"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/tailor-to-venue/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/tailor-to-venue"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/tailor-to-venue.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 | $0.00185 | $0.02108 |
| Opus 5 | $0.00093 | $0.01054 |
| Sonnet 5 | $0.00037 | $0.00422 |
| Haiku 4.5 | $0.00018 | $0.00211 |
Grade A, and why
tailor-to-venue 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tailor to Venue
Diff a draft against a target venue's requirements and produce a concrete tailoring plan: what to reposition, what to cut, what to reformat, and what to anonymize — before any edit is made. This skill plans; the user (or a follow-up request) executes the edits.
When to use
- Retargeting a paper (new submission, rejection, or venue switch) to a different conference, journal, track, or page limit.
- Converting between template families (acmart / IEEEtran / NeurIPS-style / llncs) or between blind levels (single ↔ double ↔ triple).
- Deciding which track at one venue fits the work best.
Related skills: parse-cfp (build a missing venue profile), select-venue
(choose the venue first), preflight-check (final desk-reject lint before
submission), prepare-camera-ready (after acceptance).
Inputs
- The draft: main
.texfile (the scripts resolve\input/\include); optionally the compiled PDF and.bibfiles. - Target venue profile:
venues/conferences/<venue-id>.yml(schema:venues/schema.yml; family defaults merge automatically fromvenues/families/). - Target track name, if the user has chosen one.
Process
1. Resolve the venue profile
Find the profile in venues/conferences/. If none exists, do NOT invent
requirements — run the parse-cfp skill against the venue's CFP URL to
create one, or proceed with only facts quoted live from the CFP.
2. Re-verify against the live CFP (mandatory)
Profiles go stale and a wrong page limit causes a desk reject. Fetch the
profile's cfp_url and re-verify before relying on anything: page limits and
exclusions for the chosen track, deadlines and timezone, blind level,
template/documentclass invocation, and required sections. Note in the plan
what was verified and when; if the live CFP contradicts the profile, the CFP
wins — flag the profile for update. If the CFP cannot be fetched, mark every
profile-derived fact "UNVERIFIED — confirm on CFP" in the plan.
3. Pick the track with the user
What ships with it
9 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.
- references/anonymization-sweep.md 5.4 KB
- references/contribution-reframing.md 6.5 KB
- references/page-budget-cutting.md 6.1 KB
- references/template-switching.md 7.3 KB
- scripts/anon_sweep.py 12 KB runs code
- scripts/page_budget.py 11 KB runs code
- scripts/texscan.py 4.6 KB runs code
- scripts/venue_diff.py 13 KB runs code
- scripts/venueyaml.py 10 KB runs code
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 · 180 lines · 185 tokens per session scan A 00b71fbcee42
tailor-to-venue 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 185 tokens to every session and 2,108 once invoked, about $0.0009 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…
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
Use when drafting an ACL author response inside an ACL Rolling Review cycle on OpenReview, covering the response window before meta-review, reviewer discussion dynamics, score-change strategy, flagging review issues to the area chair, anonymity rules, and deciding between responding now versus revising for a later ARR…
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…