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home / skills / agentscope-ai / agentscope-java / calculation-skill

calculation-skill skill

/agentscope-core/src/test/resources/test-skills/calculation-skill

This skill helps perform mathematical calculations including basic arithmetic, advanced functions, and statistical analysis to support computational tasks.

npx playbooks add skill agentscope-ai/agentscope-java --skill calculation-skill

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
291 B
---
name: calculation-skill
description: A skill for mathematical calculations
---
# Calculation Skill

This skill provides mathematical calculation capabilities:
- Basic arithmetic operations
- Advanced mathematical functions
- Statistical analysis

Use this skill for computational tasks.

Overview

This skill provides reliable mathematical calculation capabilities for agents and applications. It supports basic arithmetic, advanced mathematical functions, and common statistical operations. Designed to integrate into Java-based agent workflows, it focuses on correctness and predictable numerical behavior.

How this skill works

The skill exposes functions for arithmetic (add, subtract, multiply, divide), higher-level math (exponentiation, roots, trigonometry, logarithms), and statistical summaries (mean, median, variance, standard deviation). Inputs are validated and numeric edge cases (division by zero, NaN, infinities) are handled explicitly. Results are returned in primitive numeric types or structured summaries to fit downstream agent logic.

When to use it

  • Performing arithmetic or algebraic computations within an agent pipeline.
  • Computing statistical summaries from datasets handled by an agent.
  • Evaluating mathematical expressions during decision-making or simulation.
  • Preprocessing numeric data before passing results to a model or rule engine.
  • Rapid prototyping of numeric logic inside Java-based LLM applications.

Best practices

  • Validate input ranges before performing sensitive operations like root extraction or logarithms.
  • Choose appropriate numeric types (double vs. BigDecimal) when precision is critical.
  • Handle exceptional results explicitly and return clear error indicators for downstream logic.
  • Prefer batched operations for large datasets to reduce overhead and improve performance.
  • Document units and expected input scales to avoid misinterpretation of results.

Example use cases

  • Calculate aggregate metrics (mean, median, variance) for a dataset fed by an agent.
  • Evaluate complex expressions needed to score or rank actions during planning.
  • Perform trigonometric and exponential calculations in simulation or modeling tasks.
  • Quick arithmetic checks and transformations before sending numeric features to a model.
  • Generate numerical reports or summaries as part of an automated analysis workflow.

FAQ

What numeric types does the skill return?

Results are typically returned as standard numeric types; when high precision is required choose or configure BigDecimal-based operations.

How are errors handled?

The skill validates inputs and returns clear error indicators or exceptions for invalid operations such as division by zero or out-of-domain inputs.