code review risk auditor

    1

    Review code changes for security, performance, and logic defects using path-tracing and impact-based risk ranking.

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    code review risk auditor

    Example session with this skill installed

    Review the changes in this commit for security and performance risks: I added a new user search endpoint that queries the database using a raw string and returns the full user object.

    • Read your context and instructions
    • Compiled the code review risk

    I have audited the code changes for the user search endpoint. The review identified a critical security vulnerability and a high-impact performance bottleneck.

    Risk Audit Report

    #CategoryImpactConfidenceFile:LineIssue
    1SecurityCriticalVerifiedapi/users.ts:42SQL Injection via raw string interpolation
    2PerformanceHighProbabledb/query.ts:15N+1 query pattern in search results
    3MaintainabilityLowPossibleapi/users.ts:45Hardcoded page size limit

    Detailed Findings

    1. SQL Injection Vulnerability

    • Classification: Verified Issue
    • Risk Level: Critical
    • Affected Path: GET /search?q={input} -> searchUsers(input) -> db.execute()
    • Evidence: const query = 'SELECT * FROM users WHERE name = ' + input;
    • Proposed Fix:
    const query = 'SELECT * FROM users WHERE name = ?';
    return db.execute(query, [input]);
    
    • Rationale: Using parameterized queries prevents user input from being executed as SQL commands.

    2. Full Object Exposure

    • Classification: Context-Dependent Concern
    • Risk Level: High
    • Affected Path: searchUsers returns User[] including password_hash and secret_key.
    • Evidence: SELECT * returns all columns to the client.
    • Proposed Fix:
    const query = 'SELECT id, username, email FROM users WHERE name = ?';
    
    • Rationale: Reduces payload size and prevents accidental leakage of sensitive internal fields.

    Summary Statistics

    • Total Findings: 3
    • Risk Distribution: 1 Critical, 1 High, 1 Low
    • Recommended Actions:
      • Must-fix: Address the SQL injection immediately.
      • Should-fix: Implement column projection to limit data exposure.

    Next steps

    • Apply the parameterized query fix to the search endpoint.
    • Define a Data Transfer Object (DTO) for the user response.
    • Add an integration test for SQL injection payloads.

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    About this skill

    The problem

    Pull requests often harbor hidden regressions, concurrency bugs, and performance bottlenecks that standard linters miss. Manual reviews are time-consuming and prone to missing edge cases in complex execution paths.

    What it does

    • Analyzes intended behavior against actual implementation to find logic gaps.
    • Traces execution paths and dependency propagation to identify blast radius.
    • Detects edge cases including race conditions, null dereferences, and O(n²) patterns.
    • Ranks findings by impact and confidence levels to prioritize critical fixes.
    • Provides minimal, targeted code snippets to resolve identified defects.

    Why this beats prompting it yourself

    Generic prompts often hallucinate issues or provide vague architectural advice. This skill uses a structured risk-ranking framework and path-tracing logic to distinguish between verified bugs and context-dependent concerns, ensuring you only act on high-signal feedback.

    Use cases

    • Reviewing security-critical PRs for injection risks or auth bypasses.
    • Auditing legacy code refactors for behavioral regressions.
    • Identifying memory leaks and unnecessary allocations in performance-sensitive paths.
    • Evaluating third-party PRs for maintainability and naming convention alignment.

    Known limitations

    Requires enough code context to trace call chains. Large diffs without commit messages may reduce the accuracy of behavioral inference.

    Accuracy depends on provided context. Large diffs without commit messages may limit the ability to infer intended behavior. Requires traceable code paths to verify high-confidence issues.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

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    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed 2 months ago

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    Listed2 months ago

    What's inside

    Frequently Asked Questions

    Unlike standard linters that check for syntax, this skill uses path-tracing to follow data through your application, identifying complex risks like race conditions and logic gaps that only appear during execution.

    The skill is optimized for modern development environments and integrates seamlessly with any AI agent that supports file-reading and multi-step reasoning capabilities.

    The auditor supports most popular programming languages, though it performs best with languages like TypeScript, JavaScript, and Python where call chains and dependency propagation are clearly defined.

    Your purchase includes the specialized risk-ranking logic, the path-tracing framework, and a configuration guide to help you integrate the skill into your existing PR workflow.

    The skill ranks every finding by impact and confidence levels, allowing your team to focus on critical security flaws and performance blockers while ignoring low-signal noise.

    For the highest accuracy, ensure your PRs include relevant commit messages and enough surrounding code context for the skill to trace function calls and state changes across files.

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