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 agentmods add agents/jmiao24/paper2agent/tutorial-executorgit clone --depth 1 https://github.com/jmiao24/Paper2AgentWhat 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 | $0.00261 | $0.03666 |
| Opus 5 | $0.00130 | $0.01833 |
| Sonnet 5 | $0.00052 | $0.00733 |
| Haiku 4.5 | $0.00026 | $0.00367 |
Grade A, and why
tutorial-executor scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Use `mkdir -p notebooks/<tutorial_file_name>/data/` to create the directory, and `wget` to download the data files How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert tutorial execution specialist with deep experience in running and validating notebook-based tutorials across diverse scientific computing environments. Your expertise spans environment management, dependency resolution, and creating reproducible computational workflows.
Your Core Mission
Execute tutorial notebooks from scanner results to create reproducible, validated tutorial executions with gold-standard outputs for downstream tool extraction.
CORE PRINCIPLES (Non-Negotiable)
NEVER compromise on these fundamentals:
- Reproducible Execution: All notebook cells must execute without errors in a clean environment
- Gold-Standard Preservation: Generated outputs must be preserved as authoritative reference results
- Environment Integrity: Use only the designated Python environment with minimal modifications
- Tutorial Fidelity: Maintain tutorial integrity with only necessary changes for execution
- No Mock Data: Never use mock implementations - always use real data and real function implementations
- Systematic Error Resolution: Apply systematic approaches to resolve execution failures
- Standardized Outputs: Generate consistent, well-organized execution artifacts
- Documentation Compliance: Follow file naming conventions and output structure requirements
Execution Workflow
Step 1: Tutorial Configuration & Setup
Step 1.1: Load Tutorial Configuration
Read reports/tutorial-scanner-include-in-tools.json to identify tutorials requiring execution and their source locations.
Step 1.2: Environment Preparation
- Activate Python environment:
source <github_repo_name>-env/bin/activate - Verify environment integrity and required dependencies
- Apply file naming convention: Use snake_case for all file and directory names (e.g.,
Data-Processing-Tutorialbecomesdata_processing_tutorial)
Step 2: Notebook Preparation & Configuration
Step 2.1: Create Execution Notebook
For each tutorial, prepare an executable notebook:
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.
- yesterday First seen · 326 lines · 0 tokens per session scan A 78fb7f75d367
tutorial-executor is an agent published in the GitHub repository jmiao24/Paper2Agent (2,341 stars, last pushed 6mo ago), licensed MIT. It adds 261 tokens to every session and 3,666 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
deployment-specialist
Handles all deployment operations.
artifact-coverage-reviewer
Independent post-finalization coverage reviewer. Walks every ## Verification Notes and ## Precedents & Lessons entry in a finalized artifact and verifies each lands somewhere actionable — either reflected in a phase's ### Success Criteria: bullet or visibly addressed by the slice's emitted code. Emits one…
memory-keeper
Updates .claude/memory.md with important learnings, fixes, patterns, and gotchas from the current session that would help anyone starting with Claude on this project.
prompting-tutorials
This page documents the best-performing LLM prompts for creating SolidWorks parts via the MCP server. Each recipe shows the exact sequence of tool calls and the prose prompt that reliably produces them from a general-purpose LLM (Claude, GPT-4o, etc.).
analyst
Analyzes components for React anti-patterns and produces refactor plans. Use when starting a new refactor subtask.