Quark Engine presents analysis reports for Android malware families, describing their behaviours, such as credential theft, remote control, data exfiltration, and ransomware activity. Security researchers use it to examine and document how malicious Android applications operate.
Borrowing it
Nothing to install: this file belongs to ev-flow/quark-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ev-flow/quark-engine/master/.claude/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/ev-flow/quark-engineWrote 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/skills/ev-flow/quark-engine/code-review)<a href="https://agentmods.dev/skills/ev-flow/quark-engine/code-review"><img src="https://agentmods.dev/badge/skills/ev-flow/quark-engine/code-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/skills/ev-flow/quark-engine/code-review"><img src="https://agentmods.dev/badge/skills/ev-flow/quark-engine/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01503 |
| Opus 5.5 | $0.00006 | $0.00601 |
| Sonnet 5.5 | $0.00003 | $0.00301 |
| Haiku 4.5 | $0.00002 | $0.00150 |
Grade A, and why
code-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 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/summary.md 280 B
- scripts/check_commit.sh 2.0 KB runs code
- scripts/check_coverage.sh 1.3 KB runs code
- scripts/check_security.sh 1.7 KB runs code
- scripts/check_spelling.sh 1.5 KB runs code
- scripts/check_style.sh 1.9 KB runs code
- scripts/check_types.sh 1.5 KB runs code
- scripts/run_all.sh 3.1 KB runs code
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 · 102 lines · 16 tokens per session scan A 32c100afbfda
code-review is a skill published in the GitHub repository ev-flow/quark-engine (1,734 stars, last pushed yesterday), licensed GPL-3.0. It adds 16 tokens to every session and 1,503 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 skills, from other repositories
engineer-bot-getting-started
An onboarding guide for an engineering delivery assistant. It helps define a repository, the allowed level of access, and the work needed before reviewing a code change.
nightly-audit-engineer-getting-started
A first-conversation guide for setting up a code-audit assistant. A code audit is a focused review of source code for defects or risky behavior, with the needed repository, scope, concerns, permissions, and schedule clarified first.
nightly-audit-engineer-ops
A workflow for reviewing code for reproducible quality problems and recording the evidence behind each finding. It follows data through inputs, permissions, storage, asynchronous work, and recovery, then suggests the smallest authorized fix.
nightly-audit-engineer-evidence
A small local checker that creates stable identifiers for code-audit findings and marks duplicates within the current batch or among previously seen findings. It processes supplied JSON with Python and does not infer whether a problem was fixed.
engineer-bot-evidence
A local checker for confirming that delivery checks and supporting evidence belong to the current software version. It does not contact Git, continuous-integration systems, or the internet.
shepherd
Use when asked to shepherd, babysit, monitor, or poll open pull requests or merge requests, including triaging review feedback, CI failures, and routine follow-up.