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 check-originalitygit 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/check-originality)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/check-originality"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/check-originality/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/check-originality"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/check-originality.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 | $0.00213 | $0.01261 |
| Opus 5 | $0.00106 | $0.00630 |
| Sonnet 5 | $0.00043 | $0.00252 |
| Haiku 4.5 | $0.00021 | $0.00126 |
Grade A, and why
check-originality 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Check Originality
Multi-method originality check that catches the overlap which actually gets papers desk-rejected or retracted — before an editor's integrity scan does.
Read this honestly and tell the user: this is not iThenticate or Turnitin. It has no access to any private publisher corpus, so it cannot guarantee a passage is original against all published literature. What it does reliably is detect overlap against sources you can name or fetch, internal text recycling, and distinctive phrases that are publicly searchable. For a journal's official originality report you still need the publisher's tool — this gets your draft clean first.
When to use
- Before submission, to catch accidental copy-paste and close paraphrase.
- When reusing your own prior work (text recycling / self-plagiarism is a real violation at most venues even though it's "your" text).
- When a co-authored draft may contain a collaborator's verbatim text from elsewhere.
- After heavy AI drafting, to confirm nothing was reproduced verbatim from a source.
The detection methods (run several — that's what "catch it properly" means)
See references/detection-methods.md for the full method and thresholds.
- Source overlap — shingle (k-gram) comparison against sources the user provides or that
fetch-paperpulls (open-access only). Reports matched passages + overlap %. - Self-plagiarism / recycling — near-duplicate passages against the author's own prior papers, and internal duplication within the draft.
- Quote & paraphrase integrity — verbatim spans that lack quotation marks or a citation; paraphrases that stay too close to the source's wording.
- Distinctive-phrase external search — pull the draft's most distinctive long phrases and search the web / Semantic Scholar for verbatim matches the local check can't see.
- Common-knowledge vs needs-citation — flag factual claims presented without a citation that likely need one (hand off specifics to
verify-citations).
What ships with it
2 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 · 59 lines · 213 tokens per session scan A f1cf9b198924
check-originality 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 213 tokens to every session and 1,261 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…
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…