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Added cicd and bug report templates
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‎.github/ISSUE_TEMPLATE/bug_report.yml‎

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name: Bug Report
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description: Something isn't working as expected
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title: "[Bug]: "
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labels: ["bug"]
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body:
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- type: textarea
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id: description
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attributes:
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label: What happened?
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description: A clear description of the bug.
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validations:
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required: true
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- type: textarea
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id: steps
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attributes:
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label: Steps to reproduce
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description: How can we reproduce this?
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value: |
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1.
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2.
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3.
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validations:
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required: true
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- type: textarea
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id: input
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attributes:
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label: Input used
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description: Paste your methodology text, caption, and the CLI command or Python code you ran.
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render: bash
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validations:
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required: true
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- type: textarea
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id: expected
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attributes:
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label: Expected behavior
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description: What you expected to happen.
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validations:
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required: false
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- type: textarea
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id: output
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attributes:
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label: Actual output or error
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description: Paste any error messages, tracebacks, or attach the generated image.
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render: text
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validations:
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required: true
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- type: input
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id: os
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attributes:
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label: Operating system
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placeholder: "e.g. Ubuntu 22.04, macOS 14.2, Windows 11"
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validations:
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required: true
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- type: input
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id: python
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attributes:
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label: Python version
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placeholder: "e.g. 3.11.5"
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validations:
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required: true
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- type: input
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id: version
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attributes:
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label: PaperBanana version or commit hash
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placeholder: "e.g. v0.1.0 or abc1234"
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validations:
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required: false

‎.github/ISSUE_TEMPLATE/config.yml‎

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blank_issues_enabled: false
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contact_links:
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- name: Questions & Ideas
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url: https://github.com/llmsresearch/paperbanana/discussions
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about: Use Discussions for questions, ideas, and general conversation. Issues are for bugs and concrete feature requests.
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- name: Submit a Reference Example (low effort)
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url: https://github.com/llmsresearch/paperbanana/discussions
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about: Drop an arXiv link and figure number in Discussions and we'll handle extraction.

‎.github/ISSUE_TEMPLATE/feature_request.yml‎

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name: Feature Request
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description: Suggest a new feature or improvement
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title: "[Feature]: "
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labels: ["enhancement"]
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body:
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- type: textarea
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id: problem
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attributes:
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label: Problem or motivation
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description: What problem does this solve? What are you trying to do that isn't currently possible or is harder than it should be?
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validations:
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required: true
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- type: textarea
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id: solution
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attributes:
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label: Proposed solution
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description: How would you like this to work? Include CLI examples, API usage, or workflow descriptions if applicable.
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validations:
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required: true
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- type: dropdown
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id: area
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attributes:
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label: Area
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options:
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- Pipeline / agents
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- Reference dataset
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- MCP server
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- CLI
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- Python API
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- Evaluation
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- New provider support (OpenAI, Anthropic, local models, etc.)
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- Documentation
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- Other
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validations:
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required: true
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- type: textarea
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id: alternatives
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attributes:
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label: Alternatives considered
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description: Any alternative approaches you've thought about.
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validations:
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required: false
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- type: checkboxes
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id: contribution
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attributes:
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label: Willingness to contribute
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options:
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- label: I'd be willing to submit a PR for this feature

‎.github/ISSUE_TEMPLATE/reference_example.yml‎

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name: Reference Example Submission
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description: Submit a paper with a good methodology diagram for the reference dataset
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title: "[Reference]: "
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labels: ["reference-dataset"]
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body:
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- type: markdown
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attributes:
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value: |
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Thanks for helping improve the reference dataset. Better reference examples directly improve output quality.
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- type: input
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id: paper_url
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attributes:
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label: Paper URL
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description: arXiv link or other publicly accessible URL.
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placeholder: "https://arxiv.org/abs/..."
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validations:
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required: true
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- type: input
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id: figure
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attributes:
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label: Figure number
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description: Which figure contains the methodology diagram?
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placeholder: "e.g. Figure 2"
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validations:
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required: true
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- type: dropdown
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id: category
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attributes:
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label: Diagram category
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description: Which category best fits this diagram?
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options:
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- Agent & Reasoning
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- Vision & Perception
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- Generative & Learning
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- Science & Applications
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- Not sure
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validations:
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required: true
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- type: textarea
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id: why
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attributes:
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label: Why is this a good reference example?
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description: What makes this diagram clear, well-designed, or representative of its category?
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validations:
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required: false
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- type: checkboxes
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id: checks
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attributes:
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label: Checklist
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options:
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- label: The paper is publicly available
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required: true
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- label: The diagram is a methodology/architecture diagram (not a results plot or data visualization)
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required: true
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- label: The diagram has a landscape aspect ratio (roughly 1.5 to 2.5 width/height)
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required: true

‎.github/PULL_REQUEST_TEMPLATE/pull_request_template.md‎

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## What does this PR do?
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<!-- Brief description of the change. Link to related issue if applicable: Fixes #123 -->
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## Type of change
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- [ ] Bug fix
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- [ ] New feature
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- [ ] Reference dataset addition
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- [ ] Documentation update
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- [ ] Refactor (no functional change)
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- [ ] New provider support
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## Changes made
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<!-- List the specific changes. Be concrete. -->
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-
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## How to test
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<!-- How can a reviewer verify this works? -->
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1.
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2.
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## Checklist
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- [ ] `pytest tests/ -v` passes
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- [ ] `ruff check paperbanana/ tests/ scripts/` passes
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- [ ] I've added/updated tests for new functionality (if applicable)
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- [ ] I've updated documentation (if applicable)
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- [ ] For reference dataset additions: verified methodology text matches diagram, aspect ratio is within [1.5, 2.5], metadata.json is complete

‎CODE_OF_CONDUCT.md‎

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# Code of Conduct
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## Our Pledge
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We are committed to making participation in this project a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
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## Our Standards
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**Behavior that contributes to a positive environment:**
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- Using welcoming and inclusive language
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- Being respectful of differing viewpoints and experiences
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- Accepting constructive criticism gracefully
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- Focusing on what is best for the project and community
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- Showing empathy toward other community members
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**Unacceptable behavior:**
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- The use of sexualized language or imagery and unwelcome sexual attention or advances
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- Trolling, insulting or derogatory comments, and personal or political attacks
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- Public or private harassment
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- Publishing others' private information without explicit permission
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- Other conduct which could reasonably be considered inappropriate in a professional setting
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## Scope
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This Code of Conduct applies within all project spaces, including issues, pull requests, discussions, and any other communication channels associated with this project. It also applies when an individual is representing the project in public spaces.
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## Enforcement
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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project maintainers at **dip@llmsresearch.com**. All complaints will be reviewed and investigated and will result in a response deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident.
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## Attribution
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This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.1.

‎SECURITY.md‎

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# Security Policy
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## Supported Versions
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| Version | Supported |
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| ------- | --------- |
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| latest (main branch) | Yes |
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## Reporting a Vulnerability
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If you discover a security vulnerability in PaperBanana, please report it responsibly.
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**Do not open a public issue.** Instead, email **dip@llmsresearch.com** with:
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- A description of the vulnerability
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- Steps to reproduce
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- Potential impact
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- Suggested fix, if you have one
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We will acknowledge receipt within 48 hours and aim to provide a fix or mitigation plan within 7 days for critical issues.
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## Security Considerations
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PaperBanana interacts with external APIs and processes user-provided text. Users should be aware of the following:
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**API Keys**: Your Google Gemini API key is stored locally in `.env`. Never commit this file to version control. The `.gitignore` already excludes it.
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**Generated Code Execution**: The `paperbanana plot` command generates and executes Matplotlib code via Gemini. While the execution environment is sandboxed to plotting functions, users should review generated code before running it in production environments.
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**MCP Server**: The MCP server exposes PaperBanana's functionality to IDE clients. It runs locally and does not accept remote connections by default. Do not expose the MCP server to untrusted networks.
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**Reference Data**: The reference dataset contains publicly available figures from arXiv papers, used under fair use for research purposes. No proprietary or restricted data is included.
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## Dependencies
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We monitor dependencies for known vulnerabilities through GitHub's Dependabot. If you notice a vulnerability in one of our dependencies, please report it through the process above.

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