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 raja21068/AutoResearch --skill resubmit-pipelinegit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/resubmit-pipeline)<a href="https://agentmods.dev/skills/raja21068/autoresearch/resubmit-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/resubmit-pipeline/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/raja21068/autoresearch/resubmit-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/resubmit-pipeline.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 | $0.00176 | $0.08226 |
| Opus 5 | $0.00088 | $0.04113 |
| Sonnet 5 | $0.00035 | $0.01645 |
| Haiku 4.5 | $0.00018 | $0.00823 |
Grade A, and why
resubmit-pipeline 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 8d 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.
This is a copy
94% identical to resubmit-pipeline — 34 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resubmit Pipeline: Text-Only Microedit Mode
Compose a polished paper into a new venue under text-only constraints: $ARGUMENTS
Why This Exists
Most ARIS writing workflows assume the input is either a narrative report (Workflow 3) or an in-progress paper that may still need experiments / bib changes / structural edits. Resubmit is a fundamentally different scope:
- The paper is already polished — proofs are done, experiments are done, bibliography is curated.
- The user wants to absorb prior reviewer concerns from a previous venue and re-submit, without introducing new experiments, new citations, or framework changes (LLM hallucination paranoia + tight resubmit timing + closed compute budget).
- The base submission directory is read-only — the new submission must compose into a sibling directory, never mutate prior state.
- Page limit may shrink between source and target venue (e.g., workshop camera-ready → 9-page main).
Existing skills cover adjacent territory but none of this exact composition: /rebuttal builds the OpenReview-style response document, not in-paper microedits; /auto-paper-improvement-loop is the per-round engine but presupposes someone has already chosen the base manuscript, migrated venue format, set the edit whitelist, queued the reviewer feedback, and decided what NOT to change. /resubmit-pipeline fills that orchestration gap.
When to Use
- A theory or system paper was rejected at venue A and you want to resubmit to venue B with tight time budget (≤ 1-2 weeks).
- You have 3 inputs ready: the polished paper directory at venue A's format, the target venue B's format/template/style files, and the prior reviewer reports.
- You explicitly do not want to re-derive theorems, run new experiments, or change the bibliography.
When NOT to Use
- The paper still needs experiments — use
/experiment-bridge→/auto-review-loopfirst. - The paper still needs structural rewrites or new sections — use
/paper-writing(Workflow 3). - You want to write the rebuttal response itself — use
/rebuttal(Workflow 4). - The reviewer feedback demands new theorems or new framework — escalate to user before starting; this skill emits
BLOCKEDwithreason_code: out_of_scope_microeditif it detects this case.
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.
- 8d ago First seen · 428 lines · 176 tokens per session scan A 3573206ecd6b
resubmit-pipeline is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 176 tokens to every session and 8,226 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to resubmit-pipeline, differing in 34 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…