菜单
Produce a board / executive security presentation — risk posture and direction, top risks in business terms, program progress against strategy, the metrics that matter, and investment asks tied to risk. Use to prepare for a board or leadership meeting. Audience is non-technical decision-makers.
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 | board-deck |
| description | Produce a board / executive security presentation — risk posture and direction, top risks in business terms, program progress against strategy, the metrics that matter, and investment asks tied to risk. Use to prepare for a board or leadership meeting. Audience is non-technical decision-makers. |
A concise, decision-oriented deck that gives the board what they need: how exposed are we, are we improving, and what should they fund or decide — in business language.
cyber-risk-quantification).security-strategy).cyber-risk-quantification) and program status
(security-strategy); translate to business language, drop jargon.security-diagramming:infographic; assemble narrative via
security-reporting:executive-summary.A board deck (slide outline + content + visual specs) plus a one-page executive
summary. Render visuals with security-diagramming.
Boards optimize for decisions under uncertainty — lead with posture and the ask, not a tour of activity. Keep the metric set small and stable so trends are comparable meeting to meeting. Every ask ties to a risk and the cost of not acting.
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.