daily-coding
Daily Coding is a quality assurance checklist skill for implementing features, writing new code, or modifying existing source code. Use it when responding to requests like "write a...", "implement...", "add...", or "modify..." that involve actual code changes. The skill guides developers through reading target files before modification, maintaining type safety, checking security vulnerabilities, verifying execution, and cleaning up debug statements before completion.
git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholar /tmp/daily-coding && cp -r /tmp/daily-coding/skills/daily-coding ~/.claude/skills/daily-codingSKILL.md
# Daily Coding Checklist
A minimal coding quality assurance checklist ensuring every code modification follows best practices.
## When to Use
Use this skill for:
- Implementing new features
- Adding code or modifying existing code
- User requests like "write a...", "implement...", "add...", or "modify..."
- Any coding task that involves Edit or Write tools
## When Not to Use
Do not use this skill for:
- Pure reading or understanding tasks with no modification intent
- Work already covered by specialized skills such as `bug-detective`, `architecture-design`, or `verification-loop`
- Configuration-only changes
- Documentation-only writing
## Core Checklist
### Before Starting
- [ ] **Read before modify** - Must read target file with Read tool before making changes
- [ ] **Understand context** - Confirm understanding of existing code logic and design intent
### During Coding
- [ ] **Minimal changes** - Only change what's necessary, no over-engineering, no unrelated features
- [ ] **Type safety** - Add type hints for Python, avoid `any` in TypeScript
- [ ] **Security check** - Avoid command injection, XSS, SQL injection vulnerabilities
### After Completion
- [ ] **Verify execution** - Ensure code runs correctly with no syntax errors
- [ ] **Clean up** - Remove print/console.log debug statements and temporary files
- [ ] **Brief summary** - Inform user what was changed and the scope of impact
## Quick Reference
### Common Mistakes to Avoid
```python
# ❌ Don't
def process(data=[]): # Mutable default argument
pass
# ✅ Should
def process(data: list | None = None):
data = data or []
```
```python
# ❌ Don't
except: # Bare except
pass
# ✅ Should
except ValueError as e:
logger.error(f"Processing failed: {e}")
raise
```
### Security Check Points
- User input must be validated/escaped
- Use pathlib for file paths, avoid path traversal
- Never hardcode sensitive info (API keys, passwords)Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code. MUST BE USED for all code changes.
Use this agent when the user provides a Kaggle competition URL or asks to learn from Kaggle winning solutions. Examples:
Use this agent when the user asks to "conduct literature review", "search for papers", "analyze research papers", "identify research gaps", "review related work", or mentions starting a research project. This agent integrates with Zotero for automated paper collection, organization, and full-text analysis. Examples:
Use this agent when the user provides a research paper (PDF/DOCX/arXiv link) or asks to learn writing patterns from papers, extract venue-specific writing signals, study paper structure, or mine rebuttal strategies. The agent writes extracted knowledge into the active installed paper-miner writing memory for ml-paper-writing. It does not maintain project-specific writing memory.
Use this agent when the user asks to "write rebuttal", "respond to reviewers", "analyze review comments", or needs help with academic paper review response. This agent specializes in systematic rebuttal writing with professional tone and structured responses.
Test-driven development guide for writing tests first, implementing the smallest passing change, and keeping verification tight. Use when the user explicitly wants TDD or when a task should be driven by failing tests before code.
Run a blocker-first post-experiment workflow: validate evidence, produce strict statistical analysis when possible, and generate a decision-oriented results report only when the analysis bundle is sufficient. Uses results-analysis + results-report as a gated two-stage workflow.
Commit changes following Conventional Commits format (local only, no push).