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/armanzeroeight/fastagent-pluginsWrote 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/armanzeroeight/fastagent-plugins/quality-check)<a href="https://agentmods.dev/commands/armanzeroeight/fastagent-plugins/quality-check"><img src="https://agentmods.dev/badge/commands/armanzeroeight/fastagent-plugins/quality-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/armanzeroeight/fastagent-plugins/quality-check"><img src="https://agentmods.dev/badge/commands/armanzeroeight/fastagent-plugins/quality-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 | $0.00016 | $0.00772 |
| Opus 5.5 | $0.00006 | $0.00309 |
| Sonnet 5.5 | $0.00003 | $0.00154 |
| Haiku 4.5 | $0.00002 | $0.00077 |
Grade A, and why
quality-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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Check
Context
- Project type: !
ls package.json 2>/dev/null && echo "node" || (ls setup.py pyproject.toml 2>/dev/null && echo "python" || echo "unknown") - File count: !
find src -type f \( -name "*.js" -o -name "*.ts" -o -name "*.py" \) 2>/dev/null | wc -l
Your Task
Analyze code quality and provide actionable recommendations for improvement.
Arguments
$1: Analysis type (optional, default: "all")smells- Detect code smells onlycomplexity- Analyze complexity onlyall- Run all analyses
$2: Severity threshold (optional, default: "medium")low- Report all issuesmedium- Report medium and high severityhigh- Report only high severity issues
Steps
-
Detect project type and locate source files
-
Based on analysis type ($1):
For code smells:
- Scan for long methods (>50 lines)
- Identify god classes (>20 methods)
- Detect duplicated code
- Find magic numbers
- Check for deep nesting (>4 levels)
For complexity:
- Calculate cyclomatic complexity
- Identify functions with complexity >10
- Analyze cognitive complexity
- Find deeply nested code
-
Categorize findings by severity
-
Filter by threshold ($2)
-
Provide refactoring recommendations
Output Format
# Code Quality Report
**Project:** [name]
**Files Analyzed:** [count]
**Issues Found:** [count]
## Summary
| Severity | Code Smells | Complexity | Total |
|----------|-------------|------------|-------|
| High | X | X | X |
| Medium | X | X | X |
| Low | X | X | X |
## High Priority Issues
### God Class: UserManager (src/services/UserManager.js)
- **Problem:** 523 lines, 18 methods, too many responsibilities
- **Impact:** Hard to maintain, test, and understand
- **Recommendation:** Split into UserService, AuthService, EmailService
- **Effort:** Medium (2-3 days)
### High Complexity: processOrder() (src/orders/processor.js:45)
- **Problem:** Cyclomatic complexity 28, cognitive complexity 35
- **Impact:** Error-prone, hard to test
- **Recommendation:** Extract validation, calculation, persistence
- **Effort:** Low (4-6 hours)
## Recommendations
1. **Immediate:** Refactor processOrder() - high complexity, high risk
2. **This Sprint:** Split UserManager god class
3. **Next Sprint:** Address duplicated code in payment processing
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 · 108 lines · 16 tokens per session scan A b8b6480f04f6
quality-check is a command published in the GitHub repository armanzeroeight/fastagent-plugins (29 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 772 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
proof-capture
Capture PR-ready proof artifacts for a feature, with an attested fail-closed controller in strict mode.
bug-check
Run automated tests and build checks first, then agent code review. For each bug found, propose or document a regression test.
aidd-churn
Rank files by hotspot score to identify prime candidates for refactoring before PR review.
aidd-pr
Review a PR, resolve addressed comments, and generate /aidd-fix delegation prompts for remaining issues.
pre-pr-check
Perform a comprehensive pre-PR check: summarize changes, analyze risk, and generate a review checklist.
review-router
Route to the appropriate quality or review command based on the user's intent.