doctor-strange

doctor-strange is a Structured reasoning & decision frameworks skill for AI agents, published by agentara in the skills repository on GitHub. Skillspace rates doctor-strange F (49/100) and it passed the safety scan; the rating is based on static analysis only.

Doctor Strange — causal sand-table simulation via parallel universe subagents. Models actors, incentives, constraints, feedback loops, state changes, and failure modes before rendering how a future event may unfold step by step, like a human mentally rehearsing multiple futures. Stores simulations as persistent memory for later recall. TRIGGER when: user explicitly asks to simulate / rehearse / play out a scenario; user says "推演", "模拟", "预演", "imagine", "what if", "run through", "play this out", "what could go wrong"; user faces a high-stakes upcoming decision and is uncertain how it will unfold. DO NOT TRIGGER when: user wants factual lookup or research; user wants analysis of a past event (use regular memory); user wants a simple recommendation without simulation; user is debugging code or doing technical work unrelated to decision-making. Three modes: SIMULATE (run a new forward simulation), RECALL (surface past simulations as soft priors), MANAGE (list/void/re-run stored…

Grade

F

Quality

49 / 100

Safety

Passed

GitHub stars

415

Unproven: rated by static analysis only, not yet compared head-to-head.

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