More screenshots

    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    EmberDiff

    4

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

    $8

    Secure checkout via Stripe

    30-day refund guarantee

    Converts to your local currency at checkout

    0 installsSecurity scanned
    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.

    What you get

    Identify security regressions and validation removals in PRs.Calculate confidence scores for identified code risks.Detect logic contradictions across multiple commits.Generate minimal, evidence-based remediation patches.

    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

    Truncates analysis at 500 lines for large diffs. Requires explicit file paths or function signatures for external context resolution.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

      Free skills download straight away. Paid skills unlock right after purchase.

    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    30-day refund

    Not a fit? Get your money back

    Trust & safety

    Security scanned

    Verified clean 2 days ago

    • Passed all security checks, Safe to install

    Listed2 days ago

    What's inside

    Frequently Asked Questions