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    EmberDiff

    5

    Deterministic, multi-tier risk analysis for code diffs and PRs with zero-invention enforcement.

    $5.99

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    4 uses5.0 (2 reviews)
    EmberDiff

    EmberDiff

    Example session with this skill installed

    Analyze this diff for risk using strict sensitivity:
    --- a/auth.py
    +++ b/auth.py

    • if user.is_authenticated and token.is_valid():
    • if user.is_authenticated:
      return access_granted()
    • Read your context and instructions
    • Compiled the emberdiff

    Analysis complete. Identified a CRITICAL security regression in auth.py due to validation removal.

    {
      "analysis_id": "1715634281",
      "config": {
        "sensitivity": "strict"
      },
      "input_summary": {
        "files_changed": ["auth.py"],
        "change_types": ["modify"],
        "primary_language": "python"
      },
      "tier1_surface": {
    
    "inventory": "Modified auth.py to update conditional access logic."
    
      },
      "tier2_signals": [
        {
          "signal": "validation removal",
          "location": "auth.py:3",
          "severity": "CRITICAL",
          "confidence": 1.0
        }
      ],
      "tier3_assessment": [
        {
    
      "finding": "Removal of token validation check simplifies authorization bypass.",
    
          "severity": "CRITICAL",
          "confidence": 1.0,
          "evidence": "-  if user.is_authenticated and token.is_valid():",
    
      "blast_notes": "Directly impacts all authentication flows relying on this helper."
    
        }
      ],
      "tier4_uncertainty": [],
      "context_requests": [],
      "tier5_remediation": [
        {
    
      "finding_ref": "Removal of token validation",
    
          "confidence": 1.0,
          "patch": "if user.is_authenticated and token.is_valid():",
          "context_status": "FULLY_CONTAINED"
        }
      ],
      "tier6_cumulative": {
        "highest_severity": "CRITICAL",
    
    "contradictions": ["LIMITATION: Single unified diff provided. Cross-commit contradiction detection unavailable."]
    
      },
      "telemetry": {
        "zero_invention_check": "PASS",
        "missing_data_policy": "EXPLICIT_NULL",
        "drift_status": "NEGATED",
        "sensitivity_filter_applied": "strict"
      }
    }
    

    Next steps

    1. Revert the removal of token.is_valid() in auth.py.
    2. Verify why this validation was targeted for removal in the PR description.
    3. Run authentication integration tests to ensure token lifecycle is correctly handled.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Manual code reviews are prone to human fatigue, subjective bias, and missed security signals. Developers often struggle to identify the true blast radius of a change or detect contradictions across a large commit range.

    What it does

    • Performs multi-tier risk gating to categorize changes by impact and severity.
    • Enforces a deterministic severity matrix for CRITICAL, MODERATE, and NOMINAL risks.
    • Calculates confidence scores based on explicit evidence vs. implied signals.
    • Identifies cross-commit contradictions where security constraints are added then later weakened.
    • Generates localized, zero-invention remediation patches only when context is fully contained.

    Why this beats prompting it yourself

    Standard LLM prompts often "hallucinate" bugs or invent context that does not exist. This skill uses a strict zero-invention policy and a locked JSON contract, ensuring you get mechanical, evidence-bound analysis instead of conversational guesses.

    Use cases

    • Automated PR risk assessment to flag high-severity security regressions.
    • Blast radius analysis for changes touching sensitive auth or crypto modules.
    • Auditing large commit histories for contradictory logic changes.
    • Filtering noise from minor refactors to focus on business-critical state mutations.

    Known limitations

    Two run paths for 500 lines at a time for hardcoded accuracy or a full run mode if confident.. Requires explicit file paths or function signatures for external context resolution.

    Input is limited to the first 500 lines of a diff. Cross-commit contradiction detection is only available when multiple distinct diffs are provided rather than a single unified diff.

    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

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

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

    Reviews

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    This skill has been used 4 times.

    Trust & safety

    Security scan

    Security scan passed 1 month ago

    Permissions

    No special permissions declared or detected

    Listed1 month ago
    Updated1 month ago

    What's inside

    Frequently Asked Questions

    EmberDiff is a specialized risk analysis engine that identifies security regressions and logic contradictions in code diffs, ensuring human reviewers can focus on high-impact changes without being distracted by minor refactors.

    This skill is designed to work with any agent that supports Git-based workflows and JSON output processing, making it compatible with popular developer environments and automated CI/CD pipelines.

    Unlike general-purpose LLM prompts, EmberDiff uses a deterministic severity matrix and a zero-invention policy to ensure every flagged risk is backed by explicit evidence rather than AI-generated guesses.

    The skill requires your agent to have access to Git diff data; for the most accurate blast radius analysis, providing explicit file paths and function signatures for external context is highly recommended.

    Your purchase includes the full EmberDiff skill definition, the deterministic risk matrix configuration, and a license for all minor version updates and performance optimizations.

    EmberDiff currently supports a maximum analysis window of 500 lines per diff to maintain high precision, though large PRs can be analyzed in segments to ensure comprehensive coverage.

    New in Code Quality & Review