What problem does it solve? Security reviews often miss real vulnerabilities or drown in noise because reviewers lack a systematic way to enumerate candidate findings. This Skill investigates code changes or repositories for technically plausible vulnerabilities, producing structured, evidence-backed candidate findings that later validation and attack-path analysis phases can act on. ## Core Features & Use Cases - Diff-Scoped Discovery: Reviews PR, commit, branch, and local patch diffs, generating rank input and deep-review file lists deterministically while staying anchored to changed code. - Repository-Wide and Deep-Pass Modes: Supports exhaustive repository scans, scoped-path scans, and independent discovery passes within deep multi-pass security scans. - Family-Specific Rules: Applies detailed checklists for deserialization, SSRF, path traversal, auth/SSO/SAML, archive extraction, injection, and other vulnerability families. - Use Case: During a pull request security review, run this Skill to enumerate each plausible vulnerability candidate with its source, sink, broken control, and CWE taxonomy, writing ledger receipts so later phases can prove coverage. ## Quick Start Ask the AI to run the finding-discovery phase on the current pull request diff and list all plausible security vulnerability candidates with their evidence.