菜单
Establish or assess an org-wide secure-by-design program — paved roads / golden paths with secure defaults, automated guardrails, and developer enablement — so the secure way is the default, fast way. Use for engineering-org security strategy at scale (not a single system's design).
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | secure-by-design-program |
| description | Establish or assess an org-wide secure-by-design program — paved roads / golden paths with secure defaults, automated guardrails, and developer enablement — so the secure way is the default, fast way. Use for engineering-org security strategy at scale (not a single system's design). |
A program that makes security the path of least resistance across the engineering org: secure defaults built into platforms and templates, guardrails that prevent classes of issues automatically, and developers enabled to build securely without friction.
security-engineer:secure-pipeline),
admission control (k8s-security), org-wide cloud guardrails (cloud-security),
secure defaults that are hard to turn off.threat-modeling),
and training that targets the org's real defect patterns.A secure-by-design program plan: paved roads · guardrails · enablement · integration ·
metrics · phased rollout. Use security-reporting; diagram the target developer/
platform flow with security-diagramming.
Make security the default, not a gate to argue with — friction that developers route around isn't security. Eliminate whole classes of issues via paved roads and guardrails rather than finding them one by one. Measure adoption and escaped defects, not the existence of a policy.
Statically review a Claude Code / AI-agent setup for security misconfigurations — risky hooks, over-broad permissions, untrusted or cleartext MCP servers, hardcoded secrets, endpoint redirection, over-privileged agents/skills, and prompt-injection in steering files. Use when asked to audit a `.claude/` directory, `settings.json`, `.mcp.json`, a plugin/marketplace, or `CLAUDE.md`, or to add a config-security gate to CI. Drives the `agentscanner` CLI.
Review the training / MLOps pipeline for security weaknesses — data-poisoning surface, feature-store and data-source trust, experiment-tracking and model-registry access control, secrets, and reproducibility/provenance. Use when assessing how models are built and promoted, not how they're served or consumed.
Review the provenance and integrity of models, datasets, and ML artifacts pulled from hubs/registries — unsafe deserialization (pickle, PyTorch/Keras/joblib), untrusted model sources, model/dataset tampering, and signing. Use when an app loads third-party model weights or datasets, or before promoting a model.
Harden a deployed model inference endpoint — authn/authz, rate limiting and abuse control, input validation, and exposure to model-extraction / inversion / membership-inference attacks. Use when reviewing how a model is served (REST/gRPC, Triton/TorchServe/KServe, or a hosted inference API), not how it was trained.
Export a finished security artifact (report, executive summary, diagram, spreadsheet of findings) to Google Drive — right folder, clear naming, and shareable with the intended audience. Use when a deliverable needs to be handed to stakeholders who live in Drive/Docs/Sheets rather than a wiki or tracker.
Turn a security finding (vuln, pentest issue, review item) into a well-formed Jira issue — mapped severity→priority, remediation/repro in the body, labels and components set, and dedup-checked against existing issues. Use when findings need to become tracked, assignable work in Jira.