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/vasilyu1983/AI-Agents-publicnpx agentmods add skills/vasilyu1983/ai-agents-public/research-scoutWrote 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/vasilyu1983/ai-agents-public/research-scout)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/research-scout"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/research-scout/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/vasilyu1983/ai-agents-public/research-scout"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/research-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.07430 |
| Opus 5 | $0.00018 | $0.03715 |
| Sonnet 5 | $0.00007 | $0.01486 |
| Haiku 4.5 | $0.00004 | $0.00743 |
Grade A, and why
research-scout 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 9d 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dev Research Scout
Scans high-signal research sources for methods, frameworks, and ideas worth applying to your own work, and converts the top finds into idea cards with how-to-apply recipes, evidence quality grades, and reproducibility notes.
Supported sources: arXiv, Hugging Face Papers, Semantic Scholar, Papers with Code (archive only — shut down Jul 2025), conference proceedings (NeurIPS / ICML / ICLR / ACL / EMNLP / KDD), industry research blogs (Anthropic / OpenAI / DeepMind / Google Research / Meta AI / Microsoft Research / Apple ML), and curator newsletters (Lilian Weng, Sebastian Raschka, Eugene Yan, Latent Space, Simon Willison, The Batch, Import AI, Interconnects / Nathan Lambert, Davis Summarizes Papers / Davis Blalock).
Output is a generative toolkit, not a landscape report:
- pattern catalog (methods worth stealing, with how-to-apply)
- anti-pattern catalog (research traps — irreproducibility, benchmark gaming, hype)
- recipes (extraction, validation-before-adoption, kill criteria)
Key distinction from sibling scouts:
- This skill = research-grade idea mining (papers + research blogs + curated synthesis)
research-painpoint-scanner= community-pain mining (Reddit / HN / GitHub Issues / G2 / Stack Overflow)research-arxiv-scout= arXiv-only deep triage with category taxonomy and attribution; specialist downstreamresearch-git= public GitHub repo research for skills, practices, and code patterns (separate concern)
Use this skill when the question is "what methods or frameworks are worth stealing from recent research?" — escalate to research-arxiv-scout for arXiv-only work where category taxonomy and attribution matter most.
Quick Reference
| Need | Go to |
|---|---|
| Pick the source mix | ## Source Selection Guide |
| Run the end-to-end scan | ## Workflow |
| Reject hype / irreproducible / benchmark-gamed work | known-traps.md |
| Pattern-match a paper to a known method shape | idea-extraction-framework.md |
| How to actually apply a stolen idea | recipes.md |
| Source-specific query and credibility guidance | ## Navigation |
| Package the idea cards | ## Templates & Assets |
| Mine industry/eng blogs + HCI papers for killer-feature attribution (bundle handoff) | ## Killer-Feature Mode (Feature-Precedent Mining) |
What ships with it
28 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.
- agents/openai.yaml 280 B
- assets/idea-card.md 3.6 KB
- assets/research-findings.tsv 1007 B
- assets/research-scan-report.md 4.6 KB
- data/sources.json 31 KB
- learnings.consolidated.md 590 B
- learnings.md 7.8 KB
- references/arxiv-strategy.md 4.0 KB
- references/conference-proceedings-strategy.md 5.6 KB
- references/curator-newsletters-strategy.md 5.9 KB
- references/feature-precedent-mining.md 11 KB
- references/free-first-sourcing-recipe.md 6.3 KB
- references/hf-papers-strategy.md 2.4 KB
- references/idea-extraction-framework.md 8.5 KB
- references/known-traps.md 9.5 KB
- references/papers-with-code-strategy.md 3.1 KB
- references/recipes.md 10 KB
- references/research-blogs-strategy.md 4.2 KB
- references/semantic-scholar-strategy.md 5.4 KB
- references/source-currency.md 16 KB
- scripts/aggregate_research_ideas.py 8.6 KB runs code
- scripts/generate_arxiv_queries.py 4.0 KB runs code
- scripts/generate_blog_queries.py 4.7 KB runs code
- scripts/generate_conference_queries.py 3.8 KB runs code
- scripts/generate_hf_papers_queries.py 2.5 KB runs code
- scripts/generate_papers_with_code_queries.py 3.9 KB runs code
- scripts/generate_semantic_scholar_queries.py 3.9 KB runs code
- scripts/validate_findings_tsv.py 4.5 KB runs code
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.
- 9d ago First seen · 420 lines · 36 tokens per session scan A ab0925d0d511
research-scout is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 36 tokens to every session and 7,430 once invoked, about $0.0002 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
arithmetic-evaluator
Evaluate arithmetic expressions and return numeric results. Use when a user asks an arithmetic question, requests calculation, or provides a math expression involving numbers, parentheses, and arithmetic operators.
conducting-scientific-research
Conduct rigorous, reproducible multi-step scientific work with literature, databases, local files, Python, R, shell, artifacts, reviewers, and approved compute. Use for evidence synthesis, data or statistical analysis, machine learning, simulation, study design, scientific figures or manuscripts, reproduction audits…
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…