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 anonymize-papergit 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/anonymize-paper)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/anonymize-paper"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/anonymize-paper/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/anonymize-paper"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/anonymize-paper.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 | $0.00219 | $0.01980 |
| Opus 5.5 | $0.00088 | $0.00792 |
| Sonnet 5.5 | $0.00044 | $0.00396 |
| Haiku 4.5 | $0.00022 | $0.00198 |
Grade A, and why
anonymize-paper 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 1mo 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anonymize Paper
Make a LaTeX submission genuinely double-blind — then undo it cleanly after
acceptance. Anonymization leaks are documented desk-reject grounds (CHI and
NeurIPS state this explicitly, including leaks in supplementary material and
linked repos), and de-anonymization done by hand routinely leaves placeholder
authors or dead anonymous.4open.science links in the published PDF. This
skill runs the deep sweep in both directions and records every change so the
reversal is mechanical, not archaeological.
When to use
- "Anonymize my paper for NeurIPS / CHI / ICML / KDD ..." / "blind this"
- "Did I leak my identity anywhere?" / "double-blind check" before submission
- "Rewrite my self-citations in third person"
- "My code/data links identify me — what do I do?"
- After acceptance: "de-anonymize", "restore the authors", "prepare the camera-ready author block"
- Called from
tailor-to-venue(anonymization sweep step) or beforepreflight-check(final gate).
Inputs
- The main
.texfile (with\documentclass);\input/\includefiles are followed automatically. - The venue profile
venues/conferences/<venue>-<year>.yml(schema invenues/schema.yml) — supplies the blind level (single/double/triple) and thecfp_url. No profile? Ask the user for the blind level or create a profile withparse-cfp. - Optional but recommended: the compiled PDF (metadata check), the supplementary directory, and the author/institution names to grep for.
Process — anonymize (submission)
-
Resolve the blind level, then re-verify it live — mandatory. Read the venue profile; fetch the
cfp_urland confirm the blind level and the venue's anonymization policy wording (what counts as a violation, whether acknowledgments must be removed, whether anonymized artifact links are allowed). Single-blind venues (e.g. SIGSPATIAL) need no anonymization — tell the user and stop instead of mangling a fine paper. -
Run the deep scan:
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.
- 1mo ago First seen · 147 lines · 219 tokens per session scan A 195d8703da5f
anonymize-paper is a skill published in the GitHub repository ShaishavMaisuria/research-paper-lifecycle-skills (55 stars, last pushed 3d ago), licensed Apache-2.0. It adds 219 tokens to every session and 1,980 once invoked, about $0.0009 per session on Opus 5.5. 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
academic-writing
You must use this when producing any research prose — literature reviews, syntheses, analyses, methodology descriptions, discussion sections, abstracts, or any written output intended for an academic audience.
systematic-review
You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.
hypothesis-testing
You must use this when formulating testable hypotheses, designing experimental controls, or defining falsification criteria.
quantitative-analysis
You must use this when selecting statistical tests, interpreting effect sizes, or conducting power analysis.
research-synthesis
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
literature-review
You must use this when synthesizing existing knowledge, identifying research gaps, or tracing the evolution of scientific ideas.