Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/agent-research-aggregator)<a href="https://agentmods.dev/skills/raja21068/autoresearch/agent-research-aggregator"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/agent-research-aggregator/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/agent-research-aggregator"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/agent-research-aggregator.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 | $0.00177 | $0.03321 |
| Opus 5 | $0.00088 | $0.01661 |
| Sonnet 5 | $0.00035 | $0.00664 |
| Haiku 4.5 | $0.00018 | $0.00332 |
Grade A, and why
agent-research-aggregator 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 7d 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
100% identical to agent-research-aggregator — 0 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-research-aggregator
Should I run? (decision gate)
Before starting Phase 1, check whether aggregation is actually needed:
| Situation | Action |
|---|---|
workspace/inputs/idea.md and workspace/inputs/experimental_log.md both exist and are non-empty |
Skip this skill entirely. Proceed directly to paper-orchestra. |
| Either file is missing or empty, and the user provided a directory path | Run this skill with that directory as --search-roots. |
| Either file is missing or empty, and no directory was provided | Scan cwd and ~ by default; show the discovery summary to the user before continuing. |
| The inputs exist but look thin (e.g. idea.md has < 5 lines, no numeric data in experimental_log.md) | Ask the user whether to supplement with aggregation or proceed as-is. |
The skill is intentionally a pre-pass — it is cheap to skip and should only run when the structured inputs don't already exist.
A pre-processing skill for PaperOrchestra (arXiv:2604.05018). Reads scattered
experimentation artifacts from AI coding-agent cache directories and synthesizes
them into the structured (I, E) input pair the PaperOrchestra pipeline expects.
[.claude/] [.cursor/] [.antigravity/] [.openclaw/]
│ │ │ │
└────────────┴──────────────┴───────────────┘
│
Phase 1: Discovery
(discover_logs.py)
│
discovered_logs.json
│
Phase 2: Extraction
(LLM call per log batch)
│
raw_experiments.json
│
Phase 3: Synthesis
(LLM call — consolidate)
│
synthesis.json
│
Phase 4: Formatting
(format_po_inputs.py)
│
┌────────────┴────────────┐
workspace/inputs/ workspace/ara/
idea.md aggregation_report.md
experimental_log.md discovered_logs.json
raw_experiments.json
synthesis.json
What ships with it
6 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.
- 7d ago First seen · 362 lines · 177 tokens per session scan A c543b867f847
agent-research-aggregator is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 177 tokens to every session and 3,321 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-research-aggregator, differing in 0 lines, and is treated as a copy.
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…