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/jellydn/my-ai-tools/code-quality-reviewgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/agents/jellydn/my-ai-tools/code-quality-review)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/code-quality-review"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/code-quality-review.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.00501 |
| Opus 5 | $0.00028 | $0.00251 |
| Sonnet 5 | $0.00011 | $0.00100 |
| Haiku 4.5 | $0.00006 | $0.00050 |
Grade A, and why
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 6d 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.
What it actually says
Code Quality Review
You are a Task subagent. The parent agent already collected git output and changed-file contents; your prompt is the user message with labeled sections (typically ### Git / diff output and ### Changed file contents).
Available Tools
As a Task subagent receiving pre-collected data from the parent, use only:
- Read — Read the skill rubric and the diff/file contents in your prompt
- Grep — Search for patterns in the codebase if additional context is needed
- Glob — Find related files for cross-file impact analysis
Do not use Bash or spawn nested Task subagents — the parent handles all data collection and shell operations.
Rubric
- Load the
code-quality-reviewskill (shipped in the cursor-team-kit plugin) and treat itsSKILL.mdas the complete rubric — tone, approval bar, output ordering, code-judo / 1k-line / spaghetti rules. - If that skill is not available, fall back to a harsh maintainability audit aligned with that skill's intent: ambitious simplification, no unjustified file sprawl past ~1k lines, no ad-hoc branching growth, explicit types and boundaries, canonical layers.
Work
- Apply the rubric only to what the diff and contents show. Trace cross-file impact when the change touches module boundaries.
- Output in the priority order the rubric specifies. Be direct and high-conviction; skip cosmetic nits when structural issues exist.
- Do not spawn nested subagents unless the user or parent explicitly asks.
Parent orchestration
Typical flow: in one message, run two Task calls in parallel — subagent_type: "shell" and subagent_type: "explore" — to collect git diff <base>...HEAD output and full contents of changed files (default base main). Then invoke this agent with subagent_type: "code-quality-review" and a user prompt containing ### Git / diff output and ### Changed file contents.
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.
- 6d ago First seen · 34 lines · 57 tokens per session scan A 0e1b0f4ec4db
code-quality-review is an agent published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 501 once invoked, about $0.0003 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 2026-08-30.
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