Project Starter is a workflow plugin that equips coding agents with specialized subagents, reusable skills, commands, hooks, and output styles for software projects. It is for developers who want structured assistance with tasks such as planning, debugging, code review, security, and automation across several coding-agent platforms. Catalogue add-ons are components of its own workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/CloudAI-X/claude-workflow-v2Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/cloudai-x/claude-workflow-v2/lint-check)<a href="https://agentmods.dev/commands/cloudai-x/claude-workflow-v2/lint-check"><img src="https://agentmods.dev/badge/commands/cloudai-x/claude-workflow-v2/lint-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/cloudai-x/claude-workflow-v2/lint-check"><img src="https://agentmods.dev/badge/commands/cloudai-x/claude-workflow-v2/lint-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00631 |
| Opus 5.5 | $0.00009 | $0.00252 |
| Sonnet 5.5 | $0.00004 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
lint-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lint Check
Run linting and code quality checks. Catches issues before they reach CI.
Phase 1: Detect Linting Tools
Find project's linting configuration:
# JavaScript/TypeScript
ls .eslintrc* eslint.config.* .prettierrc* 2>/dev/null
# Python
ls pyproject.toml .flake8 .ruff.toml setup.cfg 2>/dev/null
# Go
ls .golangci.yml .golangci.yaml 2>/dev/null
# Rust
ls rustfmt.toml .rustfmt.toml clippy.toml 2>/dev/null
Phase 2: Run Linters
JavaScript/TypeScript
npx eslint . --ext .js,.ts,.tsx --format stylish
npx prettier --check .
Python
ruff check .
# or fallback
flake8 .
black --check .
mypy .
Go
golangci-lint run
gofmt -l .
go vet ./...
Rust
cargo clippy -- -D warnings
cargo fmt --check
Phase 3: Categorize Issues
Critical (Must Fix)
- Security vulnerabilities
- Undefined variables
- Type errors
- Potential null pointer issues
- SQL injection patterns
Warnings (Should Fix)
- Unused variables/imports
- Missing return types
- Inconsistent naming
- Complex expressions
Style (Auto-fixable)
- Formatting issues
- Import ordering
- Trailing whitespace
- Line length
Phase 4: Auto-Fix Option
Offer to auto-fix style issues:
# JavaScript/TypeScript
npx eslint . --fix
npx prettier --write .
# Python
ruff check . --fix
black .
isort .
# Go
gofmt -w .
# Rust
cargo fmt
Output Format
## Lint Results: [PASS/FAIL/WARNINGS]
### Summary
- Errors: X
- Warnings: Y
- Auto-fixable: Z
### Critical Issues (Must Fix)
1. **[Rule]** - `file:line`
- Message: [linter message]
- Fix: [how to fix]
### Warnings (Should Fix)
1. **[Rule]** - `file:line`
- Message: [message]
### Auto-Fixed (if --fix was run)
- [List of auto-fixed issues]
### Commands Used
- [List all linting commands run]
### Recommendation
[ ] Ready to commit
[ ] Fix critical issues first
[ ] Run auto-fix and review changes
Usage
This command ships with the project-starter plugin. Invoke with: /project-starter:lint-check
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 140 lines · 22 tokens per session scan A a6b3d03664d7
lint-check is a command published in the GitHub repository CloudAI-X/claude-workflow-v2 (1,420 stars, last pushed 15d ago), licensed MIT. It adds 22 tokens to every session and 631 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
performance-optimization
Orchestrate end-to-end application performance optimization from profiling to monitoring.
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
ui-aqa-flow-test-report-analysis
Phase 7 Test Report Analysis of ui-aqa-flow.
api-aqa-flow-execution-and-report-analysis
Phase 6 Execution & Report Analysis of api-aqa-flow (USER INTERACTION REQUIRED).
analyze-failures
Triage Katalon True Platform/TestOps test failures and file defects. Use when you need to investigate failed test results, classify each failure as product defect vs automation defect vs environment/data issue, cluster failures by common signature, find likely root cause from execution data, and optionally create…
test-maintenance
Maintain and evolve a Katalon True Platform/TestOps regression suite as the application changes. Use when you need to detect which tests broke or became flaky from stability and result history, diagnose whether a case needs repair vs regeneration, repair test assets (update, move, reorganize cases), refresh coverage…