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 BigY0shi/super-skills --skill research-labgit clone --depth 1 https://github.com/BigY0shi/super-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/bigy0shi/super-skills/research-lab)<a href="https://agentmods.dev/skills/bigy0shi/super-skills/research-lab"><img src="https://agentmods.dev/badge/skills/bigy0shi/super-skills/research-lab/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/bigy0shi/super-skills/research-lab"><img src="https://agentmods.dev/badge/skills/bigy0shi/super-skills/research-lab.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 | $0.00193 | $0.01359 |
| Opus 5 | $0.00097 | $0.00679 |
| Sonnet 5 | $0.00039 | $0.00272 |
| Haiku 4.5 | $0.00019 | $0.00136 |
Grade A, and why
research-lab 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 11d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Lab
A complete, modular AI research and engineering suite — from rigorous data science and statistical modeling to cutting-edge AI agent architecture, multi-agent systems, LLM engineering, and voice AI.
How to Use This Skill
- Identify the task category from the routing table below
- Load the matching sub-skill from
references/skills-catalog.md - Execute following the sub-skill's workflow
Quick Routing Table
📊 Data Science & Research
| Task | Load |
|---|---|
| Data analysis, ML modeling, statistical inference, A/B testing, visualization | data-scientist |
| Rigorous academic research engineering — formal correctness, HPC, proofs | research-engineer |
| Scientific methodology, research design, literature synthesis | scientific-research |
| Brainstorming, ideation, structured creative thinking | brainstorming |
🤖 AI Agent Architecture
| Task | Load |
|---|---|
| Designing autonomous AI agents — tool use, memory, planning, guardrails | ai-agents-architect |
| Autonomous agent loops — ReAct, Plan-Execute, reflection, self-correction | autonomous-agents |
| Reusable agent design patterns — subagent coordination, handoffs | autonomous-agent-patterns |
| Computer use agents — GUI automation, screen interaction, browser agents | computer-use-agents |
🌐 Multi-Agent Systems
| Task | Load |
|---|---|
| Multi-agent orchestration — optimizing performance across agent networks | agent-orchestration |
| Dispatching and routing tasks to parallel agents | dispatching-parallel-agents |
| Subagent-driven development — building with specialized subagents | subagent-driven-dev |
| CrewAI — role-based crew design, task delegation, agent collaboration | crewai |
| LangGraph — stateful agent graphs, conditional edges, human-in-the-loop | langgraph |
🧠 Memory & State
| Task | Load |
|---|---|
| Agent memory systems — episodic, semantic, procedural, long-term persistence | agent-memory-systems |
| MCP-based memory integration for agents | agent-memory-mcp |
What ships with it
1 file 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.
- 11d ago First seen · 121 lines · 193 tokens per session scan A b5862e11fc88
research-lab is a skill published in the GitHub repository BigY0shi/super-skills (3 stars, last pushed 4mo ago), licensed MIT. It adds 193 tokens to every session and 1,359 once invoked, about $0.0010 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 2026-08-31.
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