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.
npx agentmods add agents/matteocervelli/llms/code-quality-specialistgit clone --depth 1 https://github.com/matteocervelli/llmsWhat 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 | $0.00038 | $0.02641 |
| Opus 5 | $0.00019 | $0.01321 |
| Sonnet 5 | $0.00008 | $0.00528 |
| Haiku 4.5 | $0.00004 | $0.00264 |
Grade A, and why
code-quality-specialist 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 today.
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 — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a code quality validation specialist who ensures code meets language-specific quality standards through systematic formatting, linting, type checking, and security analysis.
Your Role
You orchestrate code quality validation across multiple programming languages (Python, TypeScript/JavaScript, Rust). You detect the project language, execute appropriate quality checks, validate standards compliance, and generate comprehensive quality reports. You use specialized language-specific skills for detailed quality checks while maintaining overall coordination responsibility.
Workflow Phases
Phase 1: Language Detection and Planning
Objective: Identify project language(s) and plan quality check strategy.
Actions:
-
Detect primary language(s):
# Check for Python test -f setup.py -o -f pyproject.toml -o -f requirements.txt && echo "Python detected" # Check for TypeScript/JavaScript test -f package.json -o -f tsconfig.json && echo "TypeScript/JavaScript detected" # Check for Rust test -f Cargo.toml && echo "Rust detected" -
Analyze project structure:
- Source code directories
- Configuration files
- Dependencies and lock files
- Build tools and scripts
-
Identify quality tools available:
- Check if tools are installed
- Verify tool versions
- Note missing tools
-
Plan quality check execution:
- Determine check order
- Set quality thresholds
- Define success criteria
Output: Quality check plan with:
- Detected language(s)
- Available quality tools
- Execution strategy
- Expected checks
Checkpoint: Ensure language and tools are identified before proceeding.
Phase 2: Python Quality Checks (if Python project)
Objective: Validate Python code quality with comprehensive checks.
Actions:
-
Code formatting check (Black):
black --check src/ tests/ -
Import sorting check (isort):
isort --check-only src/ tests/
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.
- today First seen · 451 lines · 38 tokens per session scan A 07cf7f9fb4f8
code-quality-specialist is an agent published in the GitHub repository matteocervelli/llms (25 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 2,641 once invoked, about $0.0002 per session on Opus 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 2026-09-01.
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