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-refine-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-refine-pipeline)<a href="https://agentmods.dev/skills/raja21068/autoresearch/research-refine-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-refine-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-refine-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-refine-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 | $0.00073 | $0.01483 |
| Opus 5 | $0.00036 | $0.00741 |
| Sonnet 5 | $0.00015 | $0.00297 |
| Haiku 4.5 | $0.00007 | $0.00148 |
Grade A, and why
research-refine-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
97% identical to research-refine-pipeline — 2 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Refine Pipeline: End-to-End Method and Experiment Planning
Refine and concretize: $ARGUMENTS
Overview
Use this skill when the user does not want to stop at a refined method. The goal is to produce a coherent package that includes:
- a problem-anchored, elegant final proposal
- the review history explaining why the method is focused
- a detailed experiment roadmap tied to the paper's claims
- a compact pipeline summary that says what to run next
This skill composes two existing workflows:
research-refinefor method refinementexperiment-planfor claim-driven validation planning
For stage-specific detail, read these sibling skills only when needed:
../research-refine/SKILL.md../experiment-plan/SKILL.md
Core Rule
Do not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.
Default Outputs
refine-logs/FINAL_PROPOSAL.mdrefine-logs/REVIEW_SUMMARY.mdrefine-logs/REFINEMENT_REPORT.mdrefine-logs/EXPERIMENT_PLAN.mdrefine-logs/EXPERIMENT_TRACKER.mdrefine-logs/PIPELINE_SUMMARY.md
Workflow
Phase 0: Triage the Starting Point
- Extract the problem, rough approach, constraints, resources, and target venue.
- Check whether
refine-logs/FINAL_PROPOSAL.mdalready exists and still matches the current request. - If the proposal is missing, stale, or materially different from the current request, run the full
research-refinestage. - If the proposal is already strong and aligned, reuse it and jump to experiment planning.
- If in doubt, prefer re-running
research-refinerather than planning experiments for the wrong method.
Phase 1: Method Refinement Stage
Run the research-refine workflow and keep its V3 philosophy intact:
- preserve the Problem Anchor
- prefer the smallest adequate mechanism
- keep one dominant contribution
- modernize only when it improves the paper
Exit this stage only when these are explicit:
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 · 187 lines · 73 tokens per session scan A b32794cd144c
research-refine-pipeline is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 1,483 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to research-refine-pipeline, differing in 2 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…
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…
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…
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…