Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/research-pipelineWrote 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/research-pipeline)<a href="https://agentmods.dev/skills/raja21068/autoresearch/research-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-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/research-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-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 | $0.00083 | $0.02942 |
| Opus 5 | $0.00042 | $0.01471 |
| Sonnet 5 | $0.00017 | $0.00588 |
| Haiku 4.5 | $0.00008 | $0.00294 |
Grade A, and why
research-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.
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Research Pipeline: Idea → Experiments → Submission
End-to-end autonomous research workflow for: $ARGUMENTS
Constants
- AUTO_PROCEED = true — When
true, Gate 1 auto-selects the top-ranked idea (highest pilot signal + novelty confirmed) and continues to implementation. Whenfalse, always waits for explicit user confirmation before proceeding. - ARXIV_DOWNLOAD = false — When
true,/research-litdownloads the top relevant arXiv PDFs during literature survey. Whenfalse(default), only fetches metadata via arXiv API. Passed through to/idea-discovery→/research-lit. - HUMAN_CHECKPOINT = false — When
true, the auto-review loops (Stage 4) pause after each round's review to let you see the score and provide custom modification instructions before fixes are implemented. Whenfalse(default), loops run fully autonomously. Passed through to/auto-review-loop. - REVIEWER_DIFFICULTY = medium — How adversarial the reviewer is.
medium(default): standard MCP review.hard: adds reviewer memory + debate protocol.nightmare: GPT reads repo directly viacodex exec+ memory + debate. Passed through to/auto-review-loop. - AUTO_WRITE = false — When
true, automatically invoke Workflow 3 (/paper-writing) after Stage 5. RequiresVENUEto be set. Whenfalse(default), Stage 5 generatesNARRATIVE_REPORT.mdand stops — user invokes/paper-writingmanually. - VENUE = ICLR — Target venue for paper writing (Stage 6). Only used when
AUTO_WRITE=true. Options:ICLR,NeurIPS,ICML,CVPR,ACL,AAAI,ACM,IEEE_CONF,IEEE_JOURNAL.
💡 Override via argument, e.g.,
/research-pipeline "topic" — AUTO_PROCEED: false, human checkpoint: true, difficulty: nightmare, auto_write: true, venue: NeurIPS.
Overview
This skill chains the entire research lifecycle into a single pipeline:
/idea-discovery → implement → /run-experiment → /auto-review-loop → /paper-writing (optional)
├── Workflow 1 ──┤ ├────────── Workflow 2 ──────────────┤ ├── Workflow 3 ──┤
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 · 257 lines · 83 tokens per session scan A 80b57fd2b4d7
research-pipeline is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 83 tokens to every session and 2,942 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
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…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
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…
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…