Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when drafting GitHub issues for the Positron repository. It provides workflows for searching duplicates, selecting appropriate labels, gathering complete context through questioning, and writing terse, fluff-free issues that precisely describe what is needed or wrong. The skill prepares issues for manual submission by the user. Use this skill when the user asks to draft or prepare an issue for Positron.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill guides the drafting of high-quality GitHub issues for the Positron IDE repository. It ensures issues are:
Use this skill when:
Do NOT use this skill for:
positron-intake-rotation instead)Read operations (ALLOWED):
gh CLIgh CLIWrite operations (NOT ALLOWED):
gh issue createInstead: Prepare issues in markdown files or clipboard-ready format for manual user submission.
Important: This skill prepares issues for manual submission. It does NOT automatically create GitHub issues. The user maintains full control over submitting to GitHub.
Follow this workflow for every issue drafting request:
Before drafting anything, ensure all necessary information is available. Use iterative questioning if needed.
For Bug Reports, obtain:
For Feature Requests, obtain:
Ask specific questions when information is missing:
Never make assumptions. If unclear, ask rather than guess.
Use scripts/search_duplicates.sh to search for existing issues and discussions:
cd /path/to/positron/.claude/skills/positron-issue-creator
./scripts/search_duplicates.sh "keywords from issue"
Review results carefully:
Present findings to user:
If duplicate found:
If no duplicates:
Use scripts/fetch_labels.sh to retrieve current repository labels:
cd /path/to/positron/.claude/skills/positron-issue-creator
./scripts/fetch_labels.sh
Choose labels based on:
Area labels (select 1-2):
area: console - Console/REPL functionalityarea: notebook - Jupyter notebook integrationarea: editor - Text editor functionalityarea: plots - Plot viewer and visualizationarea: data-explorer - Data viewer and explorerarea: connections - Database connectionsarea: help - Help pane and documentationarea: ui - General UI/UX issuesfetch_labels.sh for complete optionsType label (select 1):
Bug - Something doesn't work as intendedFeature Request - New capability or enhancementDocumentation - Documentation improvementsPerformance - Works but too slowlyOther considerations:
Use the templates in references/issue_templates.md as starting points, but adapt to the specific issue.
Load templates when needed:
Follow writing guidelines from references/writing_guidelines.md:
Core principles:
Title guidelines:
[Component] fails when [condition]Add [feature] to [component]Body structure:
Example bug report:
Title: Console freezes when printing dataframes with 100k+ rows
The console becomes unresponsive when printing large dataframes.
## Steps to reproduce
1. Create dataframe: `df = pd.DataFrame({'a': range(100000)})`
2. Print it: `print(df)`
3. Console freezes, UI becomes unresponsive
## System details
- Positron 2024.10.0 Build 123
- macOS 14.5
- Python 3.11.6
## Error messages
Developer Console shows: "Maximum call stack size exceeded"
Example feature request:
Title: Add keyboard shortcut to insert markdown cell in notebooks
Currently inserting markdown cells requires clicking the dropdown menu.
Keyboard shortcut would improve notebook authoring workflow.
## Proposed behavior
- Add keyboard shortcut (e.g., Cmd+M or Ctrl+M)
- Should work when focus is in notebook
- Should insert cell below current cell
## Context
Similar to Jupyter's 'M' key in command mode. Notebook workflows
frequently alternate between code and markdown cells.
Common anti-patterns to avoid:
Review checklist before presenting:
Show the complete drafted issue including:
Ask user: "Does this accurately capture the issue? Would you like any changes before I create it?"
Allow for iteration:
Make requested changes and show updated draft.
Once user approves the draft, offer options for how they want to use it:
Ask the user: "How would you like me to prepare this issue?"
Create a markdown file with all issue details:
# Create file with timestamp in name for uniqueness
cat > "positron-issue-$(date +%Y%m%d-%H%M%S).md" <<'EOF'
---
title: Issue title here
labels: area: console, Bug
repository: posit-dev/positron
---
Full issue body here
with multiple lines
EOF
After saving:
https://github.com/posit-dev/positron/issues/newPresent the issue in a format ready to copy:
**Title:**
Issue title here
**Labels:**
area: console, Bug
**Body:**
Full issue body here
with multiple lines
---
Create this issue at: https://github.com/posit-dev/positron/issues/new
After presenting:
Be thorough but not pedantic:
When uncertain:
Never guess or assume:
Be patient with iteration:
Optimize for scanability:
Respect reader's time:
Be precise:
If user describes a security vulnerability:
Treat as feature requests but focus on:
Label with Documentation type.
Use bug report template but emphasize:
Label with Performance type.
If user describes several related but distinct issues:
scripts/fetch_labels.shRetrieves all repository labels for categorization.
Usage:
./scripts/fetch_labels.sh # Human-readable output
./scripts/fetch_labels.sh --json # JSON output for parsing
scripts/search_duplicates.shSearches for potential duplicate issues and discussions.
Usage:
./scripts/search_duplicates.sh "search terms"
./scripts/search_duplicates.sh "search terms" --limit 30
Returns:
Load these reference documents when drafting issues:
references/issue_templates.md - Templates for bugs, features, and hybrid issuesreferences/writing_guidelines.md - Detailed writing guidance, anti-patterns, examplesA successful issue draft means:
1. Gather Context
↓
Ask specific questions until all info available
↓
2. Search Duplicates
↓
Run scripts/search_duplicates.sh
↓
Show results to user, confirm not duplicate
↓
3. Select Labels
↓
Run scripts/fetch_labels.sh
↓
Choose appropriate area and type labels
↓
4. Draft Issue
↓
Use templates from references/
↓
Follow writing guidelines (terse, specific, direct)
↓
5. Present Draft
↓
Show complete draft to user
↓
Iterate based on feedback
↓
6. Prepare for Submission
↓
Offer markdown file or clipboard format
↓
Save to file OR format for copying
↓
Provide GitHub link and submission instructions
Remember: The goal is to draft clear, actionable issues that respect both the reporter's intent and the development team's time. The user maintains full control over the final submission to GitHub.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。