Language-native SAST linters - the first-party "linter as SAST" family that runs inside each ecosystem's standard toolchain with no separate scanner server: Bandit (Python, 60+ B-rules, severity x confidence filtering), gosec (Go, 40+ G-rules, AST + SSA taint tracking, golangci-lint integration), eslint-plugin-security + eslint-plugin-no-unsanitized (JS/TS, 14 detect-* rules + DOM-sink XSS), and PMD's Apex security ruleset (Salesforce, ApexSOQLInjection / ApexCRUDViolation / ApexSharingViolations). Covers the shared adoption pattern - install as a dev dependency, first scan, suppression-with-justification discipline, baseline-diff adoption for legacy code, SARIF output + CI gating - with per-tool depth in references. Use when a repo needs in-toolchain security linting for Python, Go, JavaScript/TypeScript, or Apex; for cross-language or cross-file taint analysis use semgrep-rules / codeql-queries instead.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
The skill contains explicit runtime install/download steps that fetch and execute remote code (e.g., go install github.com/securego/gosec/v2/cmd/gosec@latest, pre-commit hook repo: https://github.com/PyCQA/bandit, and a curl to download PMD: https://github.com/pmd/pmd/releases/download/pmd_releases%2F${PMD_VERSION}/pmd-dist-${PMD_VERSION}-bin.zip), so these external URLs are runtime dependencies that pull and run remote code.