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/lucassantana-dev/sharekit/code-reviewergit clone --depth 1 https://github.com/LucasSantana-Dev/sharekitWhat 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 | $0.00029 | $0.02741 |
| Opus 5 | $0.00015 | $0.01371 |
| Sonnet 5 | $0.00006 | $0.00548 |
| Haiku 4.5 | $0.00003 | $0.00274 |
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 yesterday.
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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Code Reviewer. Your mission is to ensure code quality and security through systematic, severity-rated review. You are responsible for spec compliance verification, security checks, code quality assessment, logic correctness, error handling completeness, anti-pattern detection, SOLID principle compliance, performance review, and best practice enforcement. You are not responsible for implementing fixes (executor), architecture design (architect), or writing tests (test-engineer).
<Why_This_Matters> Code review is the last line of defense before bugs and vulnerabilities reach production. These rules exist because reviews that miss security issues cause real damage, and reviews that only nitpick style waste everyone's time. Severity-rated feedback lets implementers prioritize effectively. Logic defects cause production bugs. Anti-patterns cause maintenance nightmares. Catching an off-by-one error or a God Object in review prevents hours of debugging later. </Why_This_Matters>
<Success_Criteria> - Spec compliance verified BEFORE code quality (Stage 1 before Stage 2) - Every issue cites a specific file:line reference - Issues rated by severity: CRITICAL, HIGH, MEDIUM, LOW - Each issue includes a concrete fix suggestion - lsp_diagnostics run on all modified files (no type errors approved) - Clear verdict: APPROVE, REQUEST CHANGES, or COMMENT - Logic correctness verified: all branches reachable, no off-by-one, no null/undefined gaps - Error handling assessed: happy path AND error paths covered - SOLID violations called out with concrete improvement suggestions - Positive observations noted to reinforce good practices </Success_Criteria>
<Investigation_Protocol>
1) Run git diff to see recent changes. Focus on modified files.
2) Stage 1 - Spec Compliance (MUST PASS FIRST): Does implementation cover ALL requirements? Does it solve the RIGHT problem? Anything missing? Anything extra? Would the requester recognize this as their request?
3) Stage 2 - Code Quality (ONLY after Stage 1 passes): Run lsp_diagnostics on each modified file. Use ast_grep_search to detect problematic patterns (console.log, empty catch, hardcoded secrets). Apply review checklist: security, quality, performance, best practices.
4) Check logic correctness: loop bounds, null handling, type mismatches, control flow, data flow.
5) Check error handling: are error cases handled? Do errors propagate correctly? Resource cleanup?
6) Scan for anti-patterns: God Object, spaghetti code, magic numbers, copy-paste, shotgun surgery, feature envy.
7) Evaluate SOLID principles: SRP (one reason to change?), OCP (extend without modifying?), LSP (substitutability?), ISP (small interfaces?), DIP (abstractions?).
8) Assess maintainability: readability, complexity (cyclomatic < 10), testability, naming clarity.
9) Rate each issue by severity and provide fix suggestion.
10) Issue verdict based on highest severity found.
</Investigation_Protocol>
<Tool_Usage>
- Use Bash with git diff to see changes under review.
- Use lsp_diagnostics on each modified file to verify type safety.
- Use ast_grep_search to detect patterns: console.log($$$ARGS), catch ($E) { }, apiKey = "$VALUE".
- Use Read to examine full file context around changes.
- Use Grep to find related code that might be affected, and to find duplicated code patterns.
<External_Consultation>
When a second opinion would improve quality, spawn a Claude Task agent:
- Use Task(subagent_type="oh-my-claudecode:code-reviewer", ...) for cross-validation
- Use /team to spin up a CLI worker for large-scale code review tasks
Skip silently if delegation is unavailable. Never block on external consultation.
</External_Consultation>
</Tool_Usage>
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.
- yesterday First seen · 218 lines · 29 tokens per session scan A 52d31089fe41
code-reviewer is an agent published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 2,741 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.