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 managing the file-based todo tracking system in the todos/ directory. It provides workflows for creating todos, managing status and dependencies, conducting triage, and integrating with slash commands and code review processes.
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。
The todos/ directory contains a file-based tracking system for managing code review feedback, technical debt, feature requests, and work items. Each todo is a markdown file with YAML frontmatter and structured sections.
This skill should be used when:
Todo files follow this naming pattern:
{issue_id}-{status}-{priority}-{description}.md
Components:
pending (needs triage), ready (approved), complete (done)p1 (critical), p2 (important), p3 (nice-to-have)Examples:
001-pending-p1-mailer-test.md
002-ready-p1-fix-n-plus-1.md
005-complete-p2-refactor-csv.md
Each todo is a markdown file with YAML frontmatter and structured sections. Use the template at assets/todo-template.md as a starting point when creating new todos.
Required sections:
Optional sections:
YAML frontmatter fields:
---
status: ready # pending | ready | complete
priority: p1 # p1 | p2 | p3
issue_id: "002"
tags: [rails, performance, database]
dependencies: ["001"] # Issue IDs this is blocked by
---
To create a new todo from findings or feedback:
ls todos/ | grep -o '^[0-9]\+' | sort -n | tail -1cp assets/todo-template.md todos/{NEXT_ID}-pending-{priority}-{description}.mdpending (needs triage) or ready (pre-approved)When to create a todo:
When to act immediately instead:
To triage pending todos:
ls todos/*-pending-*.mdmv {file}-pending-{pri}-{desc}.md {file}-ready-{pri}-{desc}.mdstatus: pending → status: readypending statusUse slash command: /triage for interactive approval workflow
To track dependencies:
dependencies: ["002", "005"] # This todo blocked by issues 002 and 005
dependencies: [] # No blockers - can work immediately
To check what blocks a todo:
grep "^dependencies:" todos/003-*.md
To find what a todo blocks:
grep -l 'dependencies:.*"002"' todos/*.md
To verify blockers are complete before starting:
for dep in 001 002 003; do
[ -f "todos/${dep}-complete-*.md" ] || echo "Issue $dep not complete"
done
When working on a todo, always add a work log entry:
### YYYY-MM-DD - Session Title
**By:** Claude Code / Developer Name
**Actions:**
- Specific changes made (include file:line references)
- Commands executed
- Tests run
- Results of investigation
**Learnings:**
- What worked / what didn't
- Patterns discovered
- Key insights for future work
Work logs serve as:
To mark a todo as complete:
mv {file}-ready-{pri}-{desc}.md {file}-complete-{pri}-{desc}.mdstatus: ready → status: completegrep -l 'dependencies:.*"002"' todos/*-ready-*.mdfeat: resolve issue 002| Trigger | Flow | Tool |
|---|---|---|
| Code review | /review → Findings → /triage → Todos | Review agent + skill |
| PR comments | /resolve_pr_parallel → Individual fixes → Todos | gh CLI + skill |
| Code TODOs | /resolve_todo_parallel → Fixes + Complex todos | Agent + skill |
| Planning | Brainstorm → Create todo → Work → Complete | Skill |
| Feedback | Discussion → Create todo → Triage → Work | Skill + slash |
Finding work:
# List highest priority unblocked work
grep -l 'dependencies: \[\]' todos/*-ready-p1-*.md
# List all pending items needing triage
ls todos/*-pending-*.md
# Find next issue ID
ls todos/ | grep -o '^[0-9]\+' | sort -n | tail -1 | awk '{printf "%03d", $1+1}'
# Count by status
for status in pending ready complete; do
echo "$status: $(ls -1 todos/*-$status-*.md 2>/dev/null | wc -l)"
done
Dependency management:
# What blocks this todo?
grep "^dependencies:" todos/003-*.md
# What does this todo block?
grep -l 'dependencies:.*"002"' todos/*.md
Searching:
# Search by tag
grep -l "tags:.*rails" todos/*.md
# Search by priority
ls todos/*-p1-*.md
# Full-text search
grep -r "payment" todos/
File-todos system (this skill):
todos/ directoryRails Todo model:
app/models/todo.rbTodoWrite tool:
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。