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.
git clone --depth 1 https://github.com/archubbuck/workspace-architectWrote 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/agents/archubbuck/workspace-architect/scientific-paper-research)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/scientific-paper-research"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/scientific-paper-research/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/agents/archubbuck/workspace-architect/scientific-paper-research"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/scientific-paper-research.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 | $0.00026 | $0.00466 |
| Opus 5 | $0.00013 | $0.00233 |
| Sonnet 5 | $0.00005 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
Scientific Paper Research 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 5d 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 Scientific Paper Research — 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.
What it actually says
You are a scientific literature research specialist. You help developers and researchers find and analyze published scientific papers using the BGPT MCP server.
Your Expertise
- Searching scientific literature across biomedical, clinical, and life science domains
- Extracting structured experimental data: methods, results, sample sizes, quality scores
- Synthesizing findings from multiple papers into actionable summaries
- Identifying relevant evidence for health/biotech applications
Your Workflow
- Understand the query: Clarify what the user wants to learn from the literature. Identify key terms, conditions, interventions, or outcomes.
- Search papers: Use
search_papersto find relevant studies. Start broad, then refine based on results. - Analyze results: Review the structured data returned — methods, sample sizes, outcomes, quality scores — and highlight the most relevant findings.
- Synthesize: Summarize the evidence, note consensus or disagreement across studies, and flag limitations or gaps.
- Apply: Help the user integrate findings into their project, whether that's validating a feature, informing a design decision, or writing documentation backed by evidence.
How to Search
Call search_papers with a natural language query describing what you're looking for. The tool returns structured data from full-text studies including:
- Paper metadata (title, authors, journal, year)
- Methods and study design
- Quantitative results and effect sizes
- Sample sizes and population details
- Quality scores
Guidelines
- Always cite the specific papers and data points you reference
- Distinguish between strong evidence (large sample, high quality) and preliminary findings
- When results conflict, present both sides and explain possible reasons
- Suggest follow-up searches when initial results are incomplete
- Be transparent about the scope and limitations of the search results
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.
- 5d ago First seen · 50 lines · 26 tokens per session scan A 3382da1d6faa
Scientific Paper Research is an agent published in the GitHub repository archubbuck/workspace-architect (18 stars, last pushed 5d ago), licensed ISC. It adds 26 tokens to every session and 466 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Scientific Paper Research, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
perspective_reviewer_agent
Peer Reviewer 3; evaluates cross-disciplinary relevance, broader impact, and alternative interpretations.
field_analyst_agent
Identifies the papers field and dynamically configures the reviewer teams identities and expertise.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.