“simulation”的搜索结果

为拥有钱包或交易执行权限的AI智能体设计并检查安全措施，涵盖外部输入防护、支出限额、发送前验证、停止条件、钱包隔离和私钥管理。

• 审计拥有交易权限的机器人
• 设计单笔及每日支出限额
• 设计发送前验证和自动停止条件

Develop visual effects with GLSL, a language for graphics programs called shaders. Apply ray marching, fluid simulation, particle systems, and rule-based generation to create hero visuals and still frames of motion.

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• Creating hero visuals for a page
• Developing fluid simulation visuals
• Building particle effects

Prepare and run molecular dynamics simulations with OpenMM, then analyze molecular motion with MDAnalysis. Examine protein flexibility, structural changes, and protein–ligand contacts through calculations and plots of simulated trajectories.

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• Investigating protein stability after a mutation
• Measuring structural changes and flexibility
• Tracking protein–ligand contacts

Assess an authorized Active Directory environment by simulating attacks against credentials, permissions, and trust relationships. Use BloodHound and related testing tools to identify paths to administrator access, evaluate detection capabilities, and document findings with remediation recommendations.

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• Assessing Active Directory security with written authorization
• Finding paths from ordinary users to administrator access
• Reviewing Kerberos and delegation settings

Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.

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• Explore research topics in manipulation or navigation
• Plan simulation studies before using physical robots
• Check novelty and feasibility of robotics ideas

Develop robotics research ideas through literature review, feasibility filtering, novelty checks, and external critique. Get ranked proposals with specific benchmarks, comparison methods, failure metrics, and simulation-first pilot plans matched to available infrastructure.

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• Exploring research topics in manipulation or navigation
• Choosing robotics ideas that fit available infrastructure
• Planning pilot studies with simulation or existing logs

Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.

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Run automatic SPICE simulations on subcircuits detected from KiCad schematic analysis — validates filter frequencies, divider ratios, opamp gains, LC resonance, and crystal load capacitance. Supports ngspice, LTspice, and Xyce (auto-detected). Generates testbenches, runs batch mode, produces structured pass/warn/fail report. Use when the user asks to simulate, verify, or validate any analog subcircuit — RC filters, LC filters, voltage dividers, opamp circuits, crystal oscillators. Also for "simulate my circuit", "run spice", "verify with simulation", "check my filter cutoff", "does this divider give the right voltage", "what's the bandwidth of this opamp stage". Consider suggesting simulation during design reviews when the schematic analyzer reports simulatable subcircuits and a SPICE simulator is available.

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Run automatic SPICE simulations on subcircuits detected from KiCad schematic analysis — validates filter frequencies, divider ratios, opamp gains, LC resonance, and crystal load capacitance. Supports ngspice, LTspice, and Xyce (auto-detected). Generates testbenches, runs batch mode, produces structured pass/warn/fail report. Use when the user asks to simulate, verify, or validate any analog subcircuit — RC filters, LC filters, voltage dividers, opamp circuits, crystal oscillators. Also for "simulate my circuit", "run spice", "verify with simulation", "check my filter cutoff", "does this divider give the right voltage", "what's the bandwidth of this opamp stage". Consider suggesting simulation during design reviews when the schematic analyzer reports simulatable subcircuits and a SPICE simulator is available.

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Import data into the Simulation Data Inspector (SDI) from MAT, CSV, or Excel files, from workspace variables, or from a Simulink simulation. Use when the user wants to view a logged file in SDI, load a bench-test log, or verify what a just-completed `Simulink.sdi.createRun` or `sim(model)` produced. Covers the `'file'`, `'vars'`, and `'namevalue'` sources of `createRun`, the auto-populate behavior after `sim()`, and post-import verification. Not for comparing existing runs (use `Simulink.sdi.compareRuns`); not for streaming data live during a running simulation (use `Simulink.sdi.createRunOrAddToStreamedRun` — a separate API); and not for authoring Gherkin or Simulink Test test cases, assertions, or regression tests (use `testing-simulink-models`). Do not activate on test-authoring or verification prompts even when no test-authoring skill is available — say so and stop.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Route a Bevy 0.20 simulation. Use when hunger and jobs are components, the sim hour is one resource, and needs run after the clock step in one chain. Keywords: Bevy, Simulation, SimClock, Hunger, Job, FixedUpdate, chain, genre.

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Thin Bevy genre router for Simulation. Keywords: Bevy, Simulation, genre, ECS, router.

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Use when you need to set up Gatling performance testing for a Java Maven project — including adding Gatling dependencies and the Gatling Maven plugin, creating Java simulations, running gatling:test, configuring a simulation class, and reviewing generated reports. This should trigger for requests such as Add Gatling performance testing; Apply Gatling performance testing; Create a Gatling simulation; Add Gatling support; Review Gatling performance test assertions and feeders. Part of Plinth Toolkit

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Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.

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Executes authorized attack simulations against Active Directory environments to identify misconfigurations, weak credentials, dangerous privilege paths, and exploitable trust relationships that could lead to domain compromise. The tester uses BloodHound for attack path analysis, Mimikatz for credential extraction, and Impacket for protocol-level attacks including Kerberoasting, AS-REP Roasting, and delegation abuse. Activates for requests involving Active Directory pentest, AD attack simulation, domain compromise testing, or Kerberos attack assessment.

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Circuit simulation workflows — SPICE simulation via ngspice, confidence scoring, RF chain analysis (scikit-rf), power/clock transient and AC analysis. Trigger on: "simulate", "run simulation", "spice", "check stability", "ripple analysis". Load the canonical sim skill from `project-skills/sim/SKILL.md`.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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A battle-tested methodology for running ANY business in real-time simulation as an autonomous operator agent. Pick a venture — a shop, restaurant, SaaS product, boutique, fund, agency, nonprofit, or creator business — and the agent operates it day by day: calendar discipline, real-world grounding, source-of-truth locking, delegated authority, an operational tracker, a named persona team, finance honesty, risk/cadence management, and self-correction. Triggers: "simulate a business", "run a business sim", "operator agent", "business simulation", "Day 0 setup".

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Spearphishing simulation is a targeted social engineering attack vector used by red teams to gain initial access. Unlike broad phishing campaigns, spearphishing uses OSINT-derived intelligence to craf

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Spearphishing simulation is a targeted social engineering attack vector used by red teams to gain initial access. Unlike broad phishing campaigns, spearphishing uses OSINT-derived intelligence to craf

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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