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 simulate-reviewersgit 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/simulate-reviewers)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/simulate-reviewers"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/simulate-reviewers/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/simulate-reviewers"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/simulate-reviewers.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 | $0.00239 | $0.02851 |
| Opus 5.5 | $0.00096 | $0.01140 |
| Sonnet 5.5 | $0.00048 | $0.00570 |
| Haiku 4.5 | $0.00024 | $0.00285 |
Grade A, and why
simulate-reviewers 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simulate Reviewers
Run a paper through a simulated, venue-calibrated review panel before submission. A NeurIPS main-track reviewer and a SIGSPATIAL demo-track judge reject for different reasons at different thresholds — this skill reproduces that difference: persona-driven weakness hunting, rubric scoring on the venue's own scale, and a deterministic decision-risk readout that tells the authors what to fix while there is still time.
When to use
- "What would reviewers say about this paper?" / "simulate a review"
- "Review this like a harsh NeurIPS reviewer" / "what will Reviewer 2 hate?"
- "Is this good enough for KDD, or should I aim for the short track?"
- "Find the weaknesses before the reviewers do" / "red-team my submission"
- After
preflight-checkpasses (format is clean) but before submitting — this skill judges content, preflight judges compliance.
Inputs
- The paper: a
.texsource tree, a PDF, or a draft in any readable form. Process it transiently — never copy paper text into the repo. - A venue profile:
venues/conferences/<venue>-<year>.yml(schema invenues/schema.yml). No profile? Create one withparse-cfpfirst, or run against the nearest family default and say so. - The target track (page limits and reviewer expectations differ — ask).
Process
-
Build the calibrated review packet. Run:
python3 scripts/review_form.py venues/conferences/<venue>-<year>.yml \ --track "<track>"This is deterministic and offline. It merges the family profile and emits the panel (personas + harshness), the venue score scale with its borderline threshold, the rubric, the per-reviewer form skeleton, and a
scores.jsontemplate. Add--jsonfor machine-readable output. Exit codes: 0 ok, 2 missing/unparsable profile or unknown track. -
Re-verify against the live CFP — mandatory. Profiles and the script's scale anchors are historical norms, never ground truth. Fetch the profile's
cfp_url(and reviewer-guidelines page if linked) and confirm: review scale and form, blind level, rebuttal format, track expectations. If anything differs, update the profile YAML, note the discrepancy in the report, and prefer the live facts. Label every venue fact you state with a confidence tag and a clickable source:verified-live/corroborated/inferred-from-family/needs-verification. A scale number quoted to the user with no source is a bug, not a convenience.
What ships with it
5 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 · 212 lines · 239 tokens per session scan A a3474a63d6ea
simulate-reviewers 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 239 tokens to every session and 2,851 once invoked, about $0.0010 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.