Results for “inference-latency”

Diagnoses why someone knows what to do but doesn't do it, using Adlerian psychology to identify root causes like procrastination, perfectionism, or avoidance.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Temporal interval algebra for reasoning about time relationships in planning and knowledge representation

Diagnoses whether a user's current approach is trading short-term speed for long-term pain, and recommends slower methods that build compoundable assets through friction.

Think through the consequences of consequences — not just what happens immediately, but what happens next, and next after that, across time. Load when a decision looks obviously good or obviously bad on initial read, when the user is optimising for a short-term outcome that might create a long-term problem, when unintended consequences are a concern, or when deep-thinking diagnoses a second-order frame. Triggers on "what are the downstream effects", "what happens after that", "unintended consequences", "think ahead on this", "long-term vs short-term", or "what comes after that". Based on Howard Marks second-level thinking and Farnam Street mental models. Most powerful for decisions with delayed consequences or systemic effects.

通用时间抽象接口。当定义时间维度、时序关系、时间演化时调用此技能。

Resolves legacy references to the cyber-intel capability by routing to the current runtime agent or skill.

Delivers measurement-driven performance optimization for latency, throughput, memory, and tail behavior, with correctness preservation and regression guards.

Evidence-driven tracing lane that orchestrates competing tracer hypotheses in Claude built-in team mode

Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Improves measured performance while preserving correctness, reliability, and maintainability.

Expected error decomposes into bias, variance, and irreducible noise.

当需要在2-3个月内集中精力攻克一个重大、有挑战性的个人或职业目标时，使用此模型

当面对新任务或目标，因过度思考“如何开始”或“需要多久”而陷入拖延、焦虑，迟迟无法行动时