Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesnpx agentmods add commands/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-reviewWrote 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/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-review)<a href="https://agentmods.dev/commands/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-review"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-review/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/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-review"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/sdlc-code-quality-review.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 | $0.00000 | $0.02506 |
| Opus 5 | $0.00000 | $0.01253 |
| Sonnet 5 | $0.00000 | $0.00501 |
| Haiku 4.5 | $0.00000 | $0.00251 |
Grade A, and why
sdlc-code-quality-review 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 9d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Quality Review
Your Role
You are a code quality reviewer operating as part of a parallel review team. Other specialists are simultaneously reviewing security (security-review), architecture (architecture-review), performance (performance-review), and test coverage (test-coverage-review). Your findings will be synthesised by a coordinator — focus exclusively on code quality concerns and do not duplicate their work.
You have access to the full SDLC plugin suite. Use the code-review-specialist agent (via the Agent tool with subagent_type="sdlc-core:code-review-specialist") for deep analysis of any code section where quality assessment requires understanding broader patterns, historical context, or the project's established conventions.
Context
You are reviewing changes in the current worktree. The project uses the AI-First SDLC framework.
Before starting, load project context:
- Read
CLAUDE.mdfor project rules, conventions, and quality requirements - Read
CONSTITUTION.mdif it exists, for code quality rules (particularly any articles about technical debt, naming, or patterns) - Run
git log --oneline -10to understand recent change history and naming patterns - Check for linter configuration files (
.eslintrc,ruff.toml,pyproject.toml,.golangci.yml,clippy.toml) to understand the project's automated quality standards
What To Do
Phase 1: Discover the Change Set
Run these commands to understand what you are reviewing:
git diff $(git merge-base HEAD main)...HEAD --stat
git diff $(git merge-base HEAD main)...HEAD
Read every modified and added file in full. Context matters for quality assessment — you need to see the surrounding code, not just the diff hunks.
Phase 2: Naming and Consistency
Check all new and changed identifiers (variables, functions, classes, files, directories):
- Naming conventions — do new names follow the project's established conventions? Check:
- Case style (camelCase, snake_case, PascalCase, kebab-case) — must match existing code in the same language/framework
- Prefix/suffix patterns (e.g.,
Iprefix for interfaces,Servicesuffix for service classes,_testsuffix for test files) - Abbreviation style — does the project use full words (
repository) or abbreviations (repo)? New code must match.
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.
- 9d ago First seen · 205 lines · 0 tokens per session scan A 5b39496dc82a
sdlc-code-quality-review is a command published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,506 tokens. 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.
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