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 jaccen/Awesome-Gaussian-Skills --skill research-expert-teamgit clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-SkillsWrote 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/jaccen/awesome-gaussian-skills/research-expert-team)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/research-expert-team"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/research-expert-team/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/jaccen/awesome-gaussian-skills/research-expert-team"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/research-expert-team.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 | $0.00227 | $0.04006 |
| Opus 5.5 | $0.00091 | $0.01602 |
| Sonnet 5.5 | $0.00045 | $0.00801 |
| Haiku 4.5 | $0.00023 | $0.00401 |
Grade A, and why
research-expert-team 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 today.
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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Expert Team — 科研全流程专家团
Scope: the full research cycle for a 3DGS / spatial-intelligence paper — from idea evaluation to rebuttal — orchestrated as nine specialist roles, with knowledge-backed method-selection tables and five-direction master-paper studies (§2.4). Knowledge base: 955 arXiv-verified methods in
data/methods.json(single source of truth — always re-query, never answer from memory), including the 12-method Ontology & Knowledge Representation, 20-method Super Agents & Agentic AI, and 22-method AIGC Content Creation categories.
1. The Nine Roles (assign by stage)
| # | Role | When invoked | Output | Hand-off |
|---|---|---|---|---|
| R1 | Idea Evaluator | a raw idea / "is this worth doing?" | 4-axis verdict: novelty / feasibility / impact / cost, each backed by ≥3 library methods as prior art | → R2 |
| R2 | Innovation Miner | idea approved, need contribution claims | 2–4 ranked contribution statements, each mapped to a measurable delta vs. a named library baseline | → R8 |
| R3 | Simulated Reviewer | draft complete, or pre-submission self-check | Reviewer-1 (novelty), Reviewer-2 (technical rigor), Reviewer-3 (experiments) + meta-review; every objection paired with a fix | cg-paper-writing Gates |
| R4 | Academic Polisher | prose-level pass | language, terminology consistency, paragraph rhythm, claim-evidence alignment edits (tracked, never silent rewrites of technical claims) | → R5 |
| R5 | AIGC-Trace Hygiene Expert | pre-submission hygiene pass | LaTeX-source metadata cleanup (generator fields, AIGC tool markers), AI-cliché and template-rhythm neutralization, style naturalization | §4 red lines |
| R6 | Framework & Venue Compliance Expert | structure pass | story-line check, contribution–evidence mapping, venue-specific structure conventions (TVCG/CGF/CAD journal vs. CVPR/ICCV/SIGGRAPH conference) | venue refs in cg-paper-writing |
| R7 | Format & Typesetting Expert | camera-ready or submission package | strict venue-template compliance: citation style, figure/table/caption rules, page & section limits, anonymization check | venue refs in cg-paper-writing |
| R8 | Experiment Designer | contribution claims fixed | baseline set (re-query the KB for the right baselines), ablation matrix, metrics, datasets — under the Comparison Fairness Protocol | 3dgs-experiment-planner |
| R9 | Experiment Analyst | results in hand | result interpretation, significance & failure-case analysis, ablation narrative, "what the numbers do and do not support" | 3dgs-visualizer |
What ships with it
7 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.
- today Changed · +29 lines · +68 tokens per session e14b9f068af4
- 2d ago First seen · 95 lines · 159 tokens per session scan A ffb28b7296ae
research-expert-team is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (162 stars, last pushed today), licensed Apache-2.0. It adds 227 tokens to every session and 4,006 once invoked, about $0.0009 per session on Opus 5.5. 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 2026-10-14.
Other skills, from other repositories
fba-simulator
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
flux-analyzer
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
gsmm-validator
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
stat-result-validator
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
statistical-experimental-evaluation
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.