Identifies what could go wrong in a system before it is built or changed — the assets worth attacking, the entry points, the trust boundaries, and the controls that actually address the realistic threats. Use this when designing a feature or system, when a change touches authentication, data handling, payments, or external input, before a security review, or when deciding which security work is worth doing at all.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Done at design time this is cheap and changes the design. Done after launch it produces a list of things that are expensive to fix, so the timing is most of the value.
1. What are we building? A diagram of the actual data flow — not the org chart, not the marketing architecture. Components, the data moving between them, and where each store lives. If nobody can draw it, that is the first finding.
Mark the trust boundaries: every point where data crosses from something you control to something you do not, or from one privilege level to another. Almost every real vulnerability lives on a boundary.
2. What can go wrong? Walk each boundary and each asset. A usable prompt set:
Two that catch more real bugs than the classic list: what does the client enforce that the server does not, and what happens on the second attempt — replay, race, and double-submit.
3. What are we going to do about it? For each realistic threat: mitigate, transfer, avoid, or accept. Accepting is legitimate; accepting silently is not.
Prioritize by attacker effort against impact, not by how alarming it sounds. A trivially exploitable tenant-isolation bug outranks a theoretical timing attack every time.
4. Did we do a good job? Re-check the model when the design changes. A threat model that describes last quarter's architecture is worse than none, because it produces false confidence.
Model per feature or per boundary, not per system. A whole-system model is too big to finish and too vague to act on.
Timebox it. An hour on a specific feature with the engineers who will build it beats a week-long exercise producing a document nobody reads.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
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Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
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Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
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Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
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