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 skills add khalilbenaz/claude-skills-collection --skill code-reviewergit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote 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/khalilbenaz/claude-skills-collection/code-reviewer)<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/code-reviewer"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/code-reviewer/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/khalilbenaz/claude-skills-collection/code-reviewer"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/code-reviewer.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 | $0.00107 | $0.01687 |
| Opus 5 | $0.00053 | $0.00843 |
| Sonnet 5 | $0.00021 | $0.00337 |
| Haiku 4.5 | $0.00011 | $0.00169 |
Grade A, and why
code-reviewer 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer
Workflow de revue (étapes dans l'ordre)
1. Contexte avant tout
Avant d'analyser une ligne : identifier le langage, le framework, et l'intention du code. Si le contexte manque et est déterminant, poser UNE question ciblée. Sinon, déduire et avancer.
2. Analyse sur 5 axes — ordre de criticité décroissant
🔴 Axe 1 — Bugs & correctness
- Race conditions, nullpointer / undefined, edge cases non gérés (tableau vide, valeur négative, overflow)
- Mauvaise gestion des erreurs :
catchvide, erreur avalée, retry sans backoff - Off-by-one, mauvaise comparaison (
==vs===,=au lieu de==)
Exemple concret :
# ❌ Bug silencieux
def get_user(id):
try:
return db.query(f"SELECT * FROM users WHERE id={id}")
except:
pass # exception avalée, retourne None sans le signaler
# ✅ Correct
def get_user(user_id: int) -> User | None:
try:
return db.query("SELECT * FROM users WHERE id = ?", (user_id,))
except DatabaseError as e:
logger.error("get_user failed: %s", e)
raise
🔴 Axe 2 — Sécurité
- Injection SQL/NoSQL/command : interpolation de chaîne dans une requête → requête paramétrée
- Secrets en dur : clé API, mot de passe dans le code → variable d'environnement / vault
- Données sensibles exposées dans les logs ou les réponses API
- Autorisation manquante (endpoint accessible sans auth)
- Désérialisation non sécurisée, path traversal
Commande rapide audit dépendances :
# npm / Node
npm audit --audit-level=high
# Python
pip-audit
# .NET
dotnet list package --vulnerable
🟡 Axe 3 — Performance
- Complexité algorithmique : O(n²) évitable, boucle dans une boucle avec accès DB
- N+1 : requête dans une boucle → eager load ou batch
- Allocation inutile en boucle critique (création d'objets, concaténation de string)
- Pas de cache sur des résultats coûteux et stables
Exemple N+1 → batch :
// ❌ N+1
for (const order of orders) {
order.user = await db.users.findById(order.userId); // 1 requête/itération
}
// ✅ Batch
const ids = orders.map(o => o.userId);
const users = await db.users.findByIds(ids); // 1 requête
const userMap = Object.fromEntries(users.map(u => [u.id, u]));
orders.forEach(o => (o.user = userMap[o.userId]));
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 · 166 lines · 107 tokens per session scan A 6059ae47f04e
code-reviewer is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 19d ago), licensed MIT. It adds 107 tokens to every session and 1,687 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
review
Reviews code for quality, security, correctness. Triggers: code review, quality review, security review, review PR, review branch.
architecture-audit
Audits codebase for architectural friction, shallow modules; proposes RFCs. Triggers: improve architecture, shallow modules, deepen modules, reduce coupling.
refactor
Refactors code for quality and maintainability. Triggers: refactor, clean up, restructure, improve code, modernize.
clean-code
Code quality: meaningful names, SRP, DRY, small functions, guard clauses, refactoring. Triggers: clean code, naming, code smell, SRP, DRY, long function, god class, dead code.
predict
Analyzes diffs for regression risk and blast radius, generates risk-scored impact report. Triggers: PR review, code change risk, breaking change, blast radius, regression check.
refactor-plan
Creates detailed refactor plan with tiny commits via interview, files as GitHub RFC. Triggers: refactor plan, refactoring RFC, incremental refactor, safe steps.