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 draft-surveygit 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/draft-survey)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/draft-survey"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/draft-survey/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/draft-survey"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/draft-survey.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 | $0.01205 |
| Opus 5.5 | $0.00085 | $0.00482 |
| Sonnet 5.5 | $0.00043 | $0.00241 |
| Haiku 4.5 | $0.00021 | $0.00120 |
Grade A, and why
draft-survey 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft Survey
Two deliverables from one topic: a ranked reading list (what to read, best first, with a one-line why) and a two-column, arXiv-ready survey draft that synthesizes the area in original prose with verified citations. Composes find-papers, verify-citations, study-exemplars, and draft-related-work.
When to use vs. literature-review
literature-reviewbuilds the related-work for your own paper — scoped to your contribution.draft-surveyproduces a standalone survey/review document (a reading list + a publishable 2-column draft) of a whole area. Use it to learn a field fast or to draft a survey paper for arXiv.
Inputs
- The topic (e.g. "geospatial data conflation"), optionally a sub-scope and a target length (default 5–6 two-column pages).
- Optional: a venue/format (default: generic two-column
article, which compiles on arXiv).
Process
- Gather candidates broadly. Run
find-papersacross DBLP + Crossref + Semantic Scholar + arXiv for the topic and its synonyms; then run its citation-graph expansion so seminal anchors and direct lineages are not missed (a survey that omits the foundational papers is a weak survey). - Rank them. Run
python3 scripts/rank_papers.py candidates.json— a composite of normalized citation count, venue tier, recency, and citation-graph centrality (weights documented in references/ranking-criteria.md). Output the ranked reading list: rank, title, authors, year, venue, a citation/impact signal, and a one-line why read this (seminal / survey / SOTA / dataset / contrarian). Keep seminal and recent both represented. - Verify every entry. Route the list through
verify-citationsso each has a real DOI/arXiv id; drop or flag anything unresolved. A survey with a fabricated reference is disqualifying. - Build a taxonomy. Cluster the verified papers into 3–6 themes/sub-problems (the survey's section structure), each with its lineage (foundational → recent).
- Draft the survey, in original prose. Write a two-column
.tex: abstract, introduction (scope + why a survey now), one section per theme (synthesize and contrast methods — never copy source sentences), a cross-cutting comparison (a table helps), open problems / future directions, conclusion, and\bibliography. Every claim cites a verified paper. Target the requested length. - Make it arXiv-ready. Ensure it compiles (
latexmk), uses a portable two-column class, and the.bibis clean. arXiv has no peer-review desk-reject, but it expects a compilable source and a real abstract;preflight-checkcan sanity-check length/structure.
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.
- 1mo ago First seen · 49 lines · 213 tokens per session scan A 64e9db25b1cc
draft-survey 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 213 tokens to every session and 1,205 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.