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 triage-reviewsgit 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/triage-reviews)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/triage-reviews"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/triage-reviews/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/triage-reviews"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/triage-reviews.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 | $0.00194 | $0.01890 |
| Opus 5 | $0.00097 | $0.00945 |
| Sonnet 5 | $0.00039 | $0.00378 |
| Haiku 4.5 | $0.00019 | $0.00189 |
Grade A, and why
triage-reviews 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triage Reviews
Convert raw reviewer text into a structured decision artifact: every concern
isolated, classified (misunderstanding / real flaw / requested experiment /
clarification / disagreement), scored severity x effort, and ordered into a
response plan that fits the venue's rebuttal budget. This is the planning
step that runs between "reviews arrived" and write-rebuttal — rebuttals
written straight from raw reviews bury the score-moving points under typo
acknowledgments.
When to use
- "My NeurIPS/ICML/SIGSPATIAL/... reviews are in — help me respond"
- "Triage these reviews" / "what do I address first in my rebuttal?"
- "Reviewer 2 says X but the paper already covers it — how do I handle this?"
- Reviews pasted from OpenReview, EasyChair, CMT, HotCRP, PCS, or a notification email
- Always before
write-rebuttal; also useful for journal revise-and-resubmit responses
Inputs
- Raw review text in a file (e.g.
reviews.txt): the user pastes or exports it from the submission system. Per-platform copy-out instructions and gotchas: references/platform-formats.md. - Venue profile (optional but recommended):
venues/conferences/<venue>-<year>.yml(schema invenues/schema.yml) — suppliesreview.rebuttal_format,review.rebuttal_limit, anddeadlines.rebuttal_end. If missing, runparse-cfpfirst or proceed platform-generic. - The submitted paper (
.tex/PDF, optional): needed to verify misunderstanding claims and fill evidence anchors.
Process
-
Stage the raw text — confidentially. Have the user save the reviews to a local file outside any git repository (or add it to
.gitignore). Review text is confidential at most venues: process it transiently and never commit it. -
Parse deterministically. Run:
python3 scripts/parse_reviews.py reviews.txt -o triage.jsonAuto-detects the platform; force with
--format openreview|easychair|cmt|hotcrpand catch terse one-liners with--min-words 3if needed. Output is a JSON skeleton: reviewers, scores, canonical sections, and per-concern entries (R1.1,R1.2, ...) withclassification/severity/effortleft null. Exit codes: 0 ok, 1 nothing detected, 2 bad input.
What ships with it
4 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 · 148 lines · 194 tokens per session scan A 921caf4ee438
triage-reviews 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 194 tokens to every session and 1,890 once invoked, about $0.0010 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.
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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…