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mathguard - Math-heavy optimization for large-scale algorithms

Guides exact or bounded-approximation algorithm choices for large-scale, math-heavy workloads with explicit errors, bounds, and trade-offs.

Tags

Updated: 2026-10-04
algorithmsprobabilistic data structuresapproximate algorithmsBloom filterHyperLogLogFFTperformance

Capabilities

Assess classical algorithmic floorsSelect named math techniquesDeclare exact or approximate modesSpecify ε/δ error parameters

Typical Inputs

Problem scaleWorkload characteristicsClassical algorithm and bound

Typical Outputs

Algorithm recommendationExactness and error declarationAsymptotic bound justification

What this skill does

  • Assess classical algorithmic floors
  • Select named math techniques
  • Declare exact or approximate modes
  • Specify ε/δ error parameters
  • Explain asymptotic bounds
  • State optimization trade-offs
  • Reject unsafe approximations
  • Provide code or pseudocode

Inputs

  • Problem scale
  • Workload characteristics
  • Classical algorithm and bound
  • Exactness requirements
  • Caller error tolerance
  • Performance bottleneck
  • Team familiarity

Outputs

  • Algorithm recommendation
  • Exactness and error declaration
  • Asymptotic bound justification
  • Trade-off statement
  • Code or pseudocode

Requirements

  • Compatible coding agent: Claude Code, Antigravity, Cursor, Gemini CLI, or Codex CLI
  • Prior lemmaly assessment

Source

  • Spec: SKILL.md

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