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feat: modernize issue templates and add AI agent configurations (#47)
## Summary This PR modernizes our GitHub issue templates and introduces AI agent configurations to enhance development workflow automation. ## Key Changes ### GitHub Issue Templates Modernization - **Migrated from Markdown to YAML format**: Replaced legacy `.md` templates with structured `.yml` templates for better form handling and data validation - **Enhanced template coverage**: Added comprehensive templates for: - Bug reports with structured fields and validation - Feature requests with clear requirement gathering - RFC (Request for Comments) for design discussions - Specification documents for technical requirements - General tasks with priority and assignment tracking - **Added template configuration**: Introduced `config.yml` to control template selection and provide additional resources ### AI Agent Integration - **Claude AI commands**: Added structured command templates for: - Automated issue creation workflows - Staged code review processes - Testing guidance and automation - **Development workflow enhancement**: These configurations enable AI-assisted development tasks while maintaining code quality standards ### Infrastructure Updates - **Updated .gitignore**: Added exclusions for Claude and Gemini AI configuration files to prevent accidental commits of sensitive or local-specific settings ## Benefits - **Improved issue quality**: Structured YAML templates provide better guidance and ensure consistent information gathering - **Enhanced developer experience**: AI agent configurations streamline common development tasks - **Better project organization**: Comprehensive template types support different workflow needs from bug tracking to technical specifications ## Breaking Changes - Legacy Markdown issue templates (`.md` format) have been removed - Users bookmarking old issue template URLs may need to update their links ## Testing Notes - Verify new issue templates render correctly in GitHub's issue creation interface - Test template validation and required field enforcement - Confirm AI agent commands function as expected in supported development environments This modernization improves our project's issue management capabilities while introducing AI-assisted development tools to enhance productivity and code quality. --- 🤖 Generated with [Claude Code](https://claude.ai/code) **Branch Info:** - Source: `feature/issue-agent-templates` - Target: `main` - Type: feature Co-Authored-By: Claude <noreply@anthropic.com>
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‎.claude/commands/create-issue.md‎

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Create a GitHub issue for the robosystems-python-client repository based on the user's input.
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## Instructions
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1. **Determine Issue Type** - Based on the user's description, determine which type to use:
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- **Bug**: Defects or unexpected behavior
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- **Task**: Specific, bounded work items that can be completed in one PR
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- **Feature**: User-facing feature suggestions
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- **Spec**: Features requiring technical design, multiple phases, or architectural changes
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- **RFC**: Significant design proposals needing team discussion before implementation
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2. **Gather Context** - If the user provides a file path or references existing code:
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- Read the relevant files to understand the current implementation
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- Check client methods in `/robosystems_client/`
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- Review tests in `/tests/`
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3. **Draft the Issue** - Use the YAML templates in `.github/ISSUE_TEMPLATE/`:
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- `bug.yml` - Include reproduction steps, version info
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- `task.yml` - Be specific about scope and acceptance criteria
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- `feature.yml` - Simple "I wish I could..." format
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- `spec.yml` - Fill in all sections with technical detail
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- `rfc.yml` - Comprehensive design with alternatives considered
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4. **Sanitize for Public Visibility** - Before creating:
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- Remove any internal API details or secrets
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- Generalize any sensitive information
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- Keep technical implementation details (these are fine to share)
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5. **Create the Issue** - Use `gh issue create` with:
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- Clear, concise title (no prefixes like [SPEC] - types handle categorization)
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- Well-formatted markdown body matching the template structure
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- Appropriate metadata labels (see below)
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6. **Set Issue Type** - After creation, set the issue type via GraphQL:
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```bash
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# Get issue ID
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gh api graphql -f query='{ repository(owner: "RoboFinSystems", name: "robosystems-python-client") { issue(number: NUMBER) { id } } }'
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# Set type (use correct type ID)
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gh api graphql -f query='mutation { updateIssue(input: { id: "ISSUE_ID", issueTypeId: "TYPE_ID" }) { issue { number } } }'
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```
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Issue Type IDs (org-level, shared across repos):
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- Task: `IT_kwDODL_jkM4BnUUo`
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- Bug: `IT_kwDODL_jkM4BnUUp`
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- Feature: `IT_kwDODL_jkM4BnUUq`
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- Spec: `IT_kwDODL_jkM4B0XrY`
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- RFC: `IT_kwDODL_jkM4B0XrZ`
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## Labels
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Issue types handle primary categorization. Use labels for metadata.
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**Area** (which part of the client):
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- `area:client` - Core client functionality
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- `area:api` - API method implementations
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- `area:types` - Pydantic models and type definitions
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- `area:auth` - Authentication handling
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- `area:errors` - Error handling
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- `area:testing` - Test coverage
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- `area:docs` - Documentation
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- `area:packaging` - pyproject.toml, releases
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- `area:ci-cd` - Workflows, releases
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**Priority** (when to do it):
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- `priority:critical` - Drop everything
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- `priority:high` - Next up
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- `priority:low` - Backlog
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**Size** (how long):
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- `size:small` - < 1 day
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- `size:medium` - 1-3 days
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- `size:large` - > 3 days
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**Status**:
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- `blocked` - Waiting on something
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- `needs-review` - Ready for review
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## Output Format
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After creating the issue, provide:
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1. The issue URL
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2. Brief summary of what was created
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3. Issue type and labels applied
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4. Any suggested follow-up tasks or related issues to create
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$ARGUMENTS

‎.claude/commands/staged-review.md‎

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Review all staged changes (`git diff --cached`) with focus on these contexts:
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## Client Implementation Context
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**API Methods:**
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- Are new methods properly typed with Pydantic models?
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- Is error handling consistent?
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- Are async/sync variants implemented correctly?
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**Type Definitions:**
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- Are Pydantic models properly defined?
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- Are types properly exported in `__init__.py`?
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- Is backwards compatibility maintained?
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**Code Quality:**
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- Does the code follow existing patterns?
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- Is the code properly formatted (ruff)?
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- Are type hints complete (basedpyright)?
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## Testing Context
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- Do new methods have corresponding tests?
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- Are edge cases covered?
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- Is test coverage maintained?
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## Documentation Context
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- Is README updated for new features?
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- Are docstrings complete?
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- Are examples provided for new methods?
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## Packaging Context
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- Is `pyproject.toml` updated if needed?
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- Are dependencies properly specified?
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## Output
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Provide a summary with:
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1. **Issues**: Problems that should be fixed before commit
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2. **Suggestions**: Improvements that aren't blocking
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3. **Questions**: Anything unclear that needs clarification

‎.claude/commands/test.md‎

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Run `just test-all` and systematically fix all failures to achieve 100% completion.
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## Strategy
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1. **Run full suite first**: `just test-all` to see the full picture
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2. **Fix by module**: When errors exist, use `just test` to iterate faster
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3. **Fix in order**: Linting/formatting → Type errors → Test failures
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4. **Stop when done**: Once `just test-all` passes completely, stop immediately
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## Key Commands
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- `just test-all` - Full suite (tests + lint + format + typecheck)
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- `just test` - Run pytest
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- `just lint fix` - Auto-fix linting issues
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- `just format` - Auto-fix formatting
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- `just typecheck` - Run basedpyright
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## Goal
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100% pass rate on `just test-all` with no errors of any kind.

‎.github/ISSUE_TEMPLATE/bug.yml‎

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name: Bug
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description: Something isn't working
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type: Bug
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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 reporting a bug! Please fill out the details below.
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- type: textarea
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id: summary
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attributes:
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label: Summary
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description: What's broken?
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placeholder: Describe the issue...
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validations:
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required: true
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- type: dropdown
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id: impact
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attributes:
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label: Impact
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options:
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- Critical
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- High
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- Medium
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- Low
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validations:
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required: true
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- type: textarea
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id: version
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attributes:
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label: Version
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description: What version are you using?
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placeholder: |
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robosystems-client version: x.x.x
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Python version: x.x.x
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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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placeholder: |
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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: expected
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attributes:
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label: Expected Behavior
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placeholder: What should happen?
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validations:
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required: true
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- type: textarea
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id: actual
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attributes:
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label: Actual Behavior
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placeholder: What actually happens?
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validations:
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required: true
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- type: textarea
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id: logs
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attributes:
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label: Error Messages / Logs
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render: shell
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placeholder: Paste any relevant error messages or stack traces here
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- type: textarea
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id: context
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attributes:
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label: Additional Context
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placeholder: Client configuration, environment, workarounds tried...

‎.github/ISSUE_TEMPLATE/bug_report.md‎

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‎.github/ISSUE_TEMPLATE/config.yml‎

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blank_issues_enabled: false
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contact_links:
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- name: Discussion
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url: https://github.com/orgs/RoboFinSystems/discussions
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about: For questions or general discussion, start a GitHub Discussion instead

‎.github/ISSUE_TEMPLATE/feature.yml‎

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name: Feature
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description: I wish I could...
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type: Feature
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body:
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- type: textarea
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id: request
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attributes:
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label: What would you like?
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description: A clear description of what you wish you could do.
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placeholder: I wish I could...
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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?
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description: What problem would this solve for you?
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placeholder: This would help because...
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validations:
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required: true
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- type: textarea
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id: ideas
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attributes:
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label: Any ideas on how?
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description: Optional - if you have thoughts on implementation, share them here.
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placeholder: One way to do this might be...
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- type: textarea
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id: context
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attributes:
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label: Additional Context
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placeholder: Examples, links, or references that help explain the request.

‎.github/ISSUE_TEMPLATE/feature_request.md‎

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