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/GDvega/super-android-kotlin-firebase-skillnpx agentmods add skills/gdvega/super-android-kotlin-firebase-skill/code-review-refactorWrote 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/gdvega/super-android-kotlin-firebase-skill/code-review-refactor)<a href="https://agentmods.dev/skills/gdvega/super-android-kotlin-firebase-skill/code-review-refactor"><img src="https://agentmods.dev/badge/skills/gdvega/super-android-kotlin-firebase-skill/code-review-refactor/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/gdvega/super-android-kotlin-firebase-skill/code-review-refactor"><img src="https://agentmods.dev/badge/skills/gdvega/super-android-kotlin-firebase-skill/code-review-refactor.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 | $0.00031 | $0.00570 |
| Opus 5.5 | $0.00012 | $0.00228 |
| Sonnet 5.5 | $0.00006 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
code-review-refactor 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Provide senior review findings and turn risky refactors into small verifiable steps.
When to use
- Reviewing a diff or project.
- Planning a refactor.
- Finding architecture, security, testing or performance risks.
- Reducing technical debt safely.
Inputs to inspect
- Diff, files or module tree.
- User goal and risk tolerance.
- Existing tests and failing commands.
- Architecture constraints.
Required workflow
- Read relevant files before judging.
- Lead with findings ordered by severity.
- Separate bugs from preferences.
- Propose the smallest safe refactor.
- Add tests/validation before broad cleanup.
Rules
- Findings first for reviews.
- Do not do cosmetic rewrites unless requested.
- Do not revert unrelated user changes.
- Preserve behavior unless asked to change it.
- Mention test gaps honestly.
Related existing skills
Local skills to invoke
- testing
- security-privacy
- compose-performance
- screen-ux-audit
- debugging
External companion skills to use when installed
Do not assume these companion skills are installed. Prefer the local skills above first, then consult Companion Skills for install and verification commands.
- vercel-labs/skills — use for Agent Skills CLI, packaging and multi-agent installation patterns when repository maintenance guidance is needed.
Files commonly touched
Any changed filesREADME/AGENTSbuild filestestsrules files
Commands to validate
./gradlew test
./gradlew lint
./gradlew assembleDebug
node scripts/validate-skills.mjs
Common mistakes to avoid
- Large unrelated refactors.
- No file/line evidence.
- Ignoring security or test gaps.
- Mixing personal style with correctness.
Checklist
- Findings prioritized.
- Evidence included.
- Safe plan given.
- Tests/commands included.
- Residual risks named.
Example prompts
- Use $super-android-kotlin-firebase to review this Android project.
- Use $super-android-kotlin-firebase to refactor this repository layer safely.
What ships with it
2 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.
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 · 31 tokens per session scan A d23aabcd2db8
code-review-refactor is a skill published in the GitHub repository GDvega/super-android-kotlin-firebase-skill (2 stars, last pushed 4mo ago), licensed MIT. It adds 31 tokens to every session and 570 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 2026-08-31.
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.
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.
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.
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.