Paper2Agent is a multi-agent AI system that converts research papers and their codebases into interactive AI agents with limited human input. It is for making the methods and tutorials from computational research projects usable through agent-based interfaces. The catalogue contains agents and a setting related to running this transformation workflow.
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 jmiao24/Paper2Agent --skill paper2mcpgit clone --depth 1 https://github.com/jmiao24/Paper2AgentWrote 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/jmiao24/paper2agent/paper2mcp)<a href="https://agentmods.dev/skills/jmiao24/paper2agent/paper2mcp"><img src="https://agentmods.dev/badge/skills/jmiao24/paper2agent/paper2mcp/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/jmiao24/paper2agent/paper2mcp"><img src="https://agentmods.dev/badge/skills/jmiao24/paper2agent/paper2mcp.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 | $0.00054 | $0.01157 |
| Opus 5.5 | $0.00022 | $0.00463 |
| Sonnet 5.5 | $0.00011 | $0.00231 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
paper2mcp 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 21d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper2MCP
Build a tested MCP server from a research repository. Select useful operations, bind each to an existing implementation, and expose it through a minimal wrapper. Scientific computation stays in the repository's code; the skill coordinates selection, execution, implementation, and verification.
Start here
- Obtain the repository URL or local checkout, output project directory, and any requested tasks, source/tutorial filter, or resource limits. Ask only for missing required inputs.
- Read orchestration for workspace setup, agent assignments, handoffs, and resume behavior.
- Read language and hardware routing. Choose Python, R, or CLI by the interface being converted. Use the Python stages below or the R / CLI route.
- Apply tool selection and minimal wrappers during scanning, implementation, and independent verification. Read runtime requirements when preparing environments and validating the server. Load only the current stage and relevant role instructions.
Workflow
| Stage | Coordinator | Python specialists | Completion marker |
|---|---|---|---|
| 1. Environment and tool selection | Setup and selection | Environment manager and scanner, concurrently | environment_and_selection_done |
| 2. Source execution and reference results | Execution | Parallel executors | reference_execution_done |
| 3. Implementation, then independent verification | Implementation and testing | Parallel implementers, then fresh verifiers | implementation_and_verification_done |
| 4. MCP integration | Integration | Coordinator | mcp_integration_done |
| 5. Runtime installation validation | Requirements | Coordinator | runtime_validation_done |
| 6. Documentation and ZIP delivery | Documentation and output delivery | Coordinator and independent delivery verifier | delivery_done |
What ships with it
44 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 262 B
- references/agents/cli/test-verifier-cli.md 679 B
- references/agents/cli/tutorial-executor-cli.md 685 B
- references/agents/cli/tutorial-scanner-cli.md 610 B
- references/agents/cli/tutorial-tool-extractor-cli.md 761 B
- references/agents/environment-python-manager.md 1.8 KB
- references/agents/r/environment-r-manager.md 1.2 KB
- references/agents/r/test-verifier-r.md 694 B
- references/agents/r/tutorial-executor-r.md 677 B
- references/agents/r/tutorial-scanner-r.md 625 B
- references/agents/r/tutorial-tool-extractor-r.md 703 B
- references/agents/test-verifier-improver.md 4.0 KB
- references/agents/tutorial-executor.md 3.1 KB
- references/agents/tutorial-scanner.md 2.9 KB
- references/agents/tutorial-tool-extractor-implementor.md 5.5 KB
- references/extensions.md 4.5 KB
- references/language-routing.md 2.6 KB
- references/orchestration.md 7.3 KB
- references/output-delivery.md 5.5 KB
- references/prompts/cli/mcp-integration.md 614 B
- references/prompts/cli/setup-and-discovery.md 624 B
- references/prompts/cli/tool-extraction-and-testing.md 645 B
- references/prompts/cli/tutorial-execution.md 623 B
- references/prompts/language-and-gpu-detection.md 462 B
- references/prompts/mcp-integration.md 1.8 KB
- references/prompts/r/mcp-integration.md 566 B
- references/prompts/r/setup-and-discovery.md 576 B
- references/prompts/r/tool-extraction-and-testing.md 597 B
- references/prompts/r/tutorial-execution.md 575 B
- references/prompts/runtime-requirements.md 2.1 KB
- references/prompts/setup-and-discovery.md 2.0 KB
- references/prompts/tool-extraction-and-testing.md 2.4 KB
- references/prompts/tutorial-execution.md 1.9 KB
- references/prompts/usage-documentation.md 2.5 KB
- references/routes/cli.md 7.7 KB
- references/routes/r.md 9.1 KB
- references/runtime-verification.md 3.9 KB
- references/runtime.md 14 KB
- references/tool-selection-and-wrapping.md 10 KB
- references/workflow-state.md 15 KB
- scripts/extract_notebook_images.py 7.5 KB runs code
- scripts/preprocess_notebook.py 2.6 KB runs code
- scripts/verify_mcp_server.py 11 KB runs code
- scripts/verify_workflow.py 25 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.
- 21d ago First seen · 50 lines · 54 tokens per session scan A 58ef21d750f3
paper2mcp is a skill published in the GitHub repository jmiao24/Paper2Agent (3,715 stars, last pushed 20d ago), licensed MIT. It adds 54 tokens to every session and 1,157 once invoked, about $0.0002 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 2026-09-17.
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