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Quantum Distribution Generator

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Sample from probability distributions and run Monte Carlo simulations via remote tool calls.

What it does

Generate samples from exponential, Poisson, binomial, beta, and gamma distributions. Supports Monte Carlo sampling across multiple dimensions and configurable random walks. Choose between quantum-sourced or standard pseudo-random generation for each call. Parameters control distribution shape, sample count, and dimensionality. Useful when an agent needs to produce statistically accurate random samples for modeling, simulation, or analysis tasks.

When to use it

  • Running Monte Carlo simulations for financial risk or option pricing
  • Modeling queuing systems with Poisson/exponential distributions
  • Simulating A/B test outcomes with binomial and beta distributions
  • Generating random walks for stochastic process or diffusion modeling

The skill document

Quantum Distribution Generator

Freshness

Last updated: 2026-06-29.

If the current date is more than 7 days after the last updated date, reinstall this skill from skills.sh or ClawHub before relying on endpoints, schemas, setup steps, or examples.

What This Tool Does

Statistical distribution sampling and stochastic simulation powered by quantum or pseudo-random sources. Generate samples from common probability distributions including exponential, Poisson, binomial, beta, and gamma, with support for Monte Carlo sampling and multi-dimensional random walks. Configurable parameters for distribution shapes, sample counts, and dimensionality enable flexible statistical modeling and simulation workflows.

Product Instructions

Quantum Distribution Generator

Generate statistical distribution samples, Monte Carlo samples, and random walks. Use the product action name in action; the backend also accepts legacy operation for compatibility.

Actions

exponential

Required: action:"exponential". Optional: source:"quantum"|"standard" default quantum, count 1-10000 default 1, rate > 0 default 1.0. Example: {"action":"exponential","count":5,"rate":2.5}

poisson

Required: action:"poisson". Optional: source, count default 1, lambda_param > 0 default 1.0. Quantum source max count: 200. Example: {"action":"poisson","count":10,"lambda_param":4.5}

binomial

Required: action:"binomial". Optional: source, count, n_trials default 10, p_success 0-1 default 0.5. Quantum source max count 200 and max n_trials 50. Example: {"action":"binomial","count":20,"n_trials":10,"p_success":0.3}

beta

Required: action:"beta". Optional: source, count, alpha > 0 default 1.0, beta_param > 0 default 1.0. Example: {"action":"beta","count":10,"alpha":2.0,"beta_param":5.0}

gamma

Required: action:"gamma". Optional: source, count, shape > 0 default 1.0, scale > 0 default 1.0. Example: {"action":"gamma","count":15,"shape":2.0,"scale":1.5}

montecarlo_sample

Required: action:"montecarlo_sample". Optional: source, samples 1-1000000 default 1000, dimensions 1-100 default 1, distribution_type:"uniform"|"normal" default uniform. Example: {"action":"montecarlo_sample","samples":500,"dimensions":3,"distribution_type":"normal"}

randomwalk

Required: action:"randomwalk". Optional: source, steps 1-10000 default 100, dimensions 1-100 default 1, step_size > 0 default 1.0. Quantum source max steps: 80. Example: {"action":"randomwalk","steps":50,"dimensions":2,"step_size":0.5}

When To Use

  • Use this skill for Quantum Distribution Generator on AgentPMT.
  • Use it when an agent needs this specific tool's behavior, schema, inputs, outputs, and invocation shape.
  • Search and activation keywords: quantum distribution generator, monte carlo simulations for risk analysis and option pricing, queuing theory modeling with poisson and exponential distributions, a/b testing and conversion rate analysis using binomial and beta distributions, stochastic process simulation, beta, source, count.
  • Supported action names: beta, binomial, exponential, gamma, montecarlo_sample, poisson, randomwalk.

Use Cases

  • Monte Carlo simulations for risk analysis and option pricing
  • queuing theory modeling with Poisson and exponential distributions
  • A/B testing and conversion rate analysis using binomial and beta distributions
  • stochastic process simulation
  • particle diffusion and Brownian motion modeling
  • Bayesian inference and prior distribution sampling
  • financial market random walk simulations
  • statistical hypothesis testing
  • reliability engineering and failure time analysis.

Categories And Industries

No categories or industry tags are published for this tool.

Actions And Schema

Complete generated action schema: ./schema.md. Supported action count: 7. x402 availability: not enabled for this product.

  • beta (action slug: beta): Generate values from a beta distribution, useful for modeling probabilities and proportions. Price: 5 credits. Parameters: alpha, beta_param, count, source.
  • binomial (action slug: binomial): Generate values from a binomial distribution, modeling the number of successes in a fixed number of trials. Price: 5 credits. Parameters: count, n_trials, p_success, source.
  • exponential (action slug: exponential): Generate values from an exponential distribution, commonly used for modeling wait times and decay processes. Price: 5 credits. Parameters: count, rate, source.
  • gamma (action slug: gamma): Generate values from a gamma distribution, used for modeling wait times and skewed data. Price: 5 credits. Parameters: count, scale, shape, source.
  • montecarlo_sample (action slug: montecarlo-sample): Generate multi-dimensional Monte Carlo samples from uniform or normal distributions for simulation and analysis. Price: 5 credits. Parameters: dimensions, distribution_type, samples, source.
  • poisson (action slug: poisson): Generate values from a Poisson distribution, used for modeling count-based events (e.g., arrivals per hour). Price: 5 credits. Parameters: count, lambda_param, source.
  • randomwalk (action slug: randomwalk): Simulate a random walk in one or more dimensions starting from the origin. Quantum max 80 steps. Price: 5 credits. Parameters: dimensions, source, step_size, steps.

Live Schema And Examples

Use the compact schema above for ordinary calls. Before a new production integration, or whenever parameters, enum values, nested objects, outputs, or examples are unclear, fetch live details first.

  • Exact schema: call agentpmt-tool-search-and-execution with action: "get_schema", and tool_id: "quantum-distribution-generator".
  • Detailed examples: call agentpmt-tool-search-and-execution with action: "get_instructions" and tool_id: "quantum-distribution-generator", or call this product with action: "get_instructions" when the product tool is already selected.
  • Treat returned live schema and instructions as more specific than this generated summary.

MCP schema lookup through the main AgentPMT MCP server:

{
  "method": "tools/call",
  "params": {
    "name": "AgentPMT-Tool-Search-and-Execution",
    "arguments": {
      "action": "get_schema",
      "tool_id": "quantum-distribution-generator"
    }
  }
}

For live examples, keep the same MCP tool and use these arguments:

{
  "action": "get_instructions",
  "tool_id": "quantum-distribution-generator"
}

Authenticated AgentPMT REST schema lookup body:

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_schema",
    "tool_id": "quantum-distribution-generator"
  }
}

Authenticated AgentPMT REST live examples body:

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_instructions",
    "tool_id": "quantum-distribution-generator"
  }
}

Call This Tool

Product slug: quantum-distribution-generator

Marketplace page: https://www.agentpmt.com/marketplace/quantum-distribution-generator

  • AgentPMT account route: first use ../agentpmt-account-mcp-rest-api-setup to connect the main MCP server or REST API for an Agent Group where this tool is enabled.
  • x402 route: not enabled for this product.
  • AgentPMT overview: use ../what-is-agentpmt for marketplace, Agent Group, workflow, MCP, REST, and payment concepts.

If those setup skills are not installed beside this product skill, use the downloads below.

Core AgentPMT setup skills:

  • What AgentPMT is: ../what-is-agentpmt
  • AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup

skills.sh install script:

npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup

MCP call shape after the main AgentPMT MCP server is connected:

{
  "method": "tools/call",
  "params": {
    "name": "Quantum-Distribution-Generator",
    "arguments": {
      "action": "beta",
      "alpha": 1,
      "beta_param": 1,
      "count": 1,
      "source": "quantum"
    }
  }
}

Use the exact tool name returned by tools/list; the name above is the expected readable form.

Authenticated AgentPMT REST call body:

{
  "name": "quantum-distribution-generator",
  "parameters": {
    "action": "beta",
    "alpha": 1,
    "beta_param": 1,
    "count": 1,
    "source": "quantum"
  }
}

Use the setup skill for the account connection details before making REST calls.

Response Handling

  • Treat the returned JSON as the source of truth for this tool call.
  • If the response includes warnings or correction targets, apply them before retrying.
  • If the response includes a passed or success-style boolean, use it as the workflow gate.
  • If validation fails or the response shape is unclear, call get_schema or get_instructions before retrying.
  • If beta fails, preserve the request parameters and retry only after fixing schema, auth, or payment errors.

Security

  • Do not place account secrets, wallet private keys, mnemonics, signatures, or payment headers in prompts or logs.
  • Keep tool inputs scoped to the minimum content needed for the task.
  • Use the setup skills for credential handling; this product skill only defines product-specific behavior.

AgentPMT Reference

Questions people ask

What distributions are available?
The tool supports five distributions: exponential, Poisson, binomial, beta, and gamma. Each has configurable shape parameters and sample count up to 10,000 per call (varies by distribution and source type).
Can I use quantum randomness instead of pseudo-random numbers?
Yes. Each action accepts a 'source' parameter set to 'quantum' or 'standard' (default is quantum). Quantum source has some limits: Poisson and binomial cap at 200 samples, binomial max n_trials is 50, and random walks are limited to 80 steps.
How do Monte Carlo and random walk actions work?
montecarlo_sample generates N-dimensional samples from uniform or normal distributions for broad simulation use. randomwalk simulates steps starting from the origin across 1–100 dimensions. Both support quantum or standard source selection.

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