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 verify-claimsgit 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/verify-claims)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-claims"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-claims/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/verify-claims"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-claims.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 | $0.00060 | $0.02874 |
| Opus 5 | $0.00030 | $0.01437 |
| Sonnet 5 | $0.00012 | $0.00575 |
| Haiku 4.5 | $0.00006 | $0.00287 |
Grade A, and why
verify-claims 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Claims
The claim-to-evidence gate. Reviewers attack the gap between what a paper asserts and what it shows — an unbacked "we are the first", a "state-of-the-art" with no comparison, a "significant improvement" with no test, a speedup stated in the text that the table does not support. This skill finds those candidate claims, makes the author map each to its evidence, and produces a claims matrix (claim · location · evidence · status) so the gap is visible and closeable before a reviewer finds it.
It is the content counterpart to verify-citations:
that skill proves each reference in the .bib resolves to a real paper; this
skill proves each load-bearing sentence in the prose is backed by something in
this paper (a number, a figure, a table, an experiment) or a real citation.
Run both before any draft, rebuttal, or camera-ready leaves the machine.
When to use
- The user asks to verify / audit / check claims (not citations): "are my claims supported?", "am I overclaiming?", "do my results match my tables?"
- Before a rebuttal — reviewers' top complaint is unsupported or overclaimed contributions; close the gaps first, or arm the rebuttal with the evidence.
- Before camera-ready or arXiv — last chance to soften an indefensible "first" or fix a number that drifted out of sync with a revised table.
- After
polish-tables-figuresregenerated a table, to confirm the prose still matches the new numbers.
Inputs
- The paper's
.tex(main file; the script follows\input/\include). Find it next to the.bib, or via the file with\documentclass. - The author, in the loop: the skill cannot decide whether a claim is true — it surfaces candidates and the author supplies (or admits the absence of) the evidence. Copilot, not pilot.
- Optional:
.paper-memory/profile.yml—risk_appetiteandcontribution_typeset how hard the author wants to push novelty vs. hedge (see Memory).
Process
What ships with it
3 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 · 206 lines · 60 tokens per session scan A 088fb9217eb0
verify-claims 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 60 tokens to every session and 2,874 once invoked, about $0.0003 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
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aaai-submission
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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…