### Install Rust Development Tools

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Install necessary Rust components like rustfmt for code formatting and clippy for linting. These tools are essential for maintaining code quality.

```bash
rustup component add rustfmt clippy
```

--------------------------------

### Install Antifragile Dependency

Source: https://github.com/minikin/antifragile/blob/main/README.md

Add the library to your Cargo.toml file.

```toml
[dependencies]
antifragile = "0.0.1"
```

--------------------------------

### Conventional Commit Examples

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Provides examples of commit messages following the conventional commits format. These illustrate how to structure messages for different types of changes.

```markdown
feat(core): add new is_nullish method
fix(serde): correct deserialization of absent fields
docs(readme): update usage examples
test(conversion): add tests for from_nullable
```

--------------------------------

### Quick Start System Analysis

Source: https://github.com/minikin/antifragile/blob/main/README.md

Define a system by implementing the Antifragile trait and classify its response to stress.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis};

// Define a system with convex response (benefits from volatility)
struct ConvexSystem;

impl Antifragile for ConvexSystem {
    type Stressor = f64;
    type Payoff = f64;

    fn payoff(&self, x: Self::Stressor) -> Self::Payoff {
        x * x  // Quadratic: convex response
    }
}

let system = ConvexSystem;
assert_eq!(system.classify(10.0, 1.0), Triad::Antifragile);
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Clone the antifragile repository and navigate into the project directory. This is the first step for setting up the development environment.

```bash
git clone https://github.com/minikin/antifragile.git
cd antifragile
```

--------------------------------

### Build and Open Documentation

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Generate project documentation and automatically open it in the default web browser. This command includes documentation for all features but excludes dependencies.

```bash
cargo doc --all-features --no-deps --open
```

--------------------------------

### Deploy services with Docker Compose

Source: https://github.com/minikin/antifragile/blob/main/examples/adaptive-pricing-api/README.md

Commands to initialize the environment and execute load tests.

```bash
# Start services and run load test
docker compose --profile loadtest up

# Or start services only, then run load test separately
docker compose up -d
docker compose --profile loadtest up loadtest
```

--------------------------------

### Enable Serde Support

Source: https://github.com/minikin/antifragile/blob/main/README.md

Include the serde feature flag for serialization support.

```toml
[dependencies]
antifragile = { version = "0.0.1", features = ["serde"] }
```

--------------------------------

### Interact with the Adaptive Pricing API

Source: https://github.com/minikin/antifragile/blob/main/examples/adaptive-pricing-api/README.md

Endpoints for calculating prices and retrieving system metrics.

```bash
curl -X POST http://localhost:3000/price \
  -H "Content-Type: application/json" \
  -d '{"product_id": "widget", "quantity": 10}'

curl http://localhost:3000/antifragile/status
curl http://localhost:3000/antifragile/curve
```

--------------------------------

### Verified Wrapper Usage

Source: https://context7.com/minikin/antifragile/llms.txt

Demonstrates wrapping a system with its verified Triad classification, caching results, and checking classification persistence. Shows access to inner system via Deref and explicit methods, re-verification, and unwrapping.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis, Verified};

struct ConvexSystem;
impl Antifragile for ConvexSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

// Create verified system with classification at operating point 10.0, delta 1.0
let mut verified = Verified::check(ConvexSystem, 10.0, 1.0);

// Access cached classification (no recomputation)
assert_eq!(verified.classification(), Triad::Antifragile);

// Use predicate methods
assert!(verified.is_antifragile());
assert!(!verified.is_fragile());
assert!(!verified.is_robust());

// Access inner system through Deref
let payoff_at_5 = verified.payoff(5.0);  // 25.0
assert!((payoff_at_5 - 25.0).abs() < f64::EPSILON);

// Access inner system explicitly
let inner: &ConvexSystem = verified.inner();
let _ = inner.payoff(3.0);

// Check if classification holds at different point
assert!(verified.still_holds(5.0, 1.0));   // Still antifragile at x=5
assert!(!verified.still_holds(10.0, 0.0)); // With delta=0, becomes Robust

// Re-verify at a new operating point (updates stored classification)
verified.re_verify(10.0, 0.0);
assert_eq!(verified.classification(), Triad::Robust);

// Unwrap to get inner system
let system = verified.into_inner();
let _ = system.payoff(10.0);

```

--------------------------------

### Antifragile Crate Feature Flags

Source: https://context7.com/minikin/antifragile/llms.txt

Illustrates how to configure the antifragile crate using Cargo.toml for different environments and features. Shows default std support, no_std environments, and enabling serde serialization.

```toml
# Cargo.toml

# Default: std enabled
[dependencies]
antifragile = "0.0.1"

# For no_std environments (embedded, WASM, etc.)
[dependencies]
antifragile = { version = "0.0.1", default-features = false }

# Enable serde serialization/deserialization
[dependencies]
antifragile = { version = "0.0.1", features = ["serde"] }

# Both no_std and serde
[dependencies]
antifragile = { version = "0.0.1", default-features = false, features = ["serde"] }

```

--------------------------------

### Format and Lint Code

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Apply code formatting using `rustfmt` and check for linting issues with `clippy`. These commands are part of the pre-commit checks to ensure code quality.

```bash
cargo fmt --all
```

```bash
cargo clippy --all-targets --all-features -- -D warnings
```

--------------------------------

### Configure no_std Environment

Source: https://github.com/minikin/antifragile/blob/main/README.md

Disable default features to use the library in no_std environments.

```toml
[dependencies]
antifragile = { version = "0.0.1", default-features = false }
```

--------------------------------

### Check Code Formatting

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Verify that the code adheres to the project's formatting standards using `rustfmt`. This command checks formatting without modifying files.

```bash
cargo fmt --all -- --check
```

--------------------------------

### Create Git Tag for Release

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Create an annotated Git tag for a new release version. This is a crucial step in the release process, marking a specific point in the project's history.

```bash
git tag -a v0.x.y -m "Release v0.x.y"
```

--------------------------------

### Run All Tests

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Execute all tests within the project, including those that depend on feature flags. This command ensures the entire codebase is functioning as expected.

```bash
cargo test --all-features
```

--------------------------------

### Serde Serialization/Deserialization with Triad

Source: https://context7.com/minikin/antifragile/llms.txt

Shows how to serialize and deserialize the Triad enum to/from JSON when the 'serde' feature is enabled. Requires importing Triad and serde_json.

```rust
// With serde feature enabled
#[cfg(feature = "serde")]
{
    use antifragile::Triad;
    use serde_json;

    let triad = Triad::Antifragile;

    // Serialize to JSON
    let json = serde_json::to_string(&triad).unwrap();  // "\"Antifragile\""

    // Deserialize from JSON
    let parsed: Triad = serde_json::from_str(&json).unwrap();
    assert_eq!(parsed, Triad::Antifragile);
}

```

--------------------------------

### Verify System Classification

Source: https://github.com/minikin/antifragile/blob/main/README.md

Use the Verified wrapper to store a system alongside its verified classification.

```rust
use antifragile::{Antifragile, Verified, TriadAnalysis};

struct MySystem;
impl Antifragile for MySystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

let verified = Verified::check(MySystem, 10.0, 1.0);
println!("Classification: {}", verified.classification());
```

--------------------------------

### Antifragile Trait Implementation

Source: https://context7.com/minikin/antifragile/llms.txt

Demonstrates how to implement the `Antifragile` trait for different system types, such as a financial call option (convex payoff) and an insurance portfolio (concave payoff).

```APIDOC
## Antifragile Trait

The core trait that all systems must implement to be analyzed for fragility. It defines the stressor type, payoff type, and the payoff function that maps stress levels to outcomes.

```rust
use antifragile::Antifragile;

// Define a financial call option with convex payoff
struct CallOption {
    strike: f64,
}

impl Antifragile for CallOption {
    type Stressor = f64;  // Underlying price
    type Payoff = f64;    // Option value

    fn payoff(&self, price: f64) -> f64 {
        (price - self.strike).max(0.0)
    }
}

// Define an insurance portfolio with concave payoff
struct InsurancePortfolio {
    premium_collected: f64,
    max_liability: f64,
}

impl Antifragile for InsurancePortfolio {
    type Stressor = f64;  // Claim rate
    type Payoff = f64;    // Profit

    fn payoff(&self, claim_rate: f64) -> f64 {
        self.premium_collected - (claim_rate * self.max_liability).min(self.max_liability)
    }
}

let option = CallOption { strike: 100.0 };
let portfolio = InsurancePortfolio {
    premium_collected: 1000.0,
    max_liability: 10000.0,
};

// Access payoff at different stress levels
println!("Option payoff at price 110: {}", option.payoff(110.0));  // 10.0
println!("Option payoff at price 90: {}", option.payoff(90.0));    // 0.0
println!("Portfolio profit at 5% claims: {}", portfolio.payoff(0.05)); // 500.0
```
```

--------------------------------

### Run Linter

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Execute the `clippy` linter to identify potential code errors and style issues. Warnings are treated as errors, enforcing stricter code quality.

```bash
cargo clippy --all-targets --all-features -- -D warnings
```

--------------------------------

### Run Specific Test Suites

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Execute specific types of tests, such as integration tests or unit tests, or target a particular test file. This is useful for focused testing during development.

```bash
# Run only integration tests

```

```bash
cargo test --tests --all-features
```

```bash

# Run only unit tests

```

```bash
cargo test --lib
```

```bash

# Run a specific test file

```

```bash
cargo test --test basic_tests
```

--------------------------------

### Analyze Financial Options for Antifragility

Source: https://context7.com/minikin/antifragile/llms.txt

Implements the Antifragile trait for individual call options and portfolios to evaluate payoff behavior under price stressors.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis, Verified};

/// A call option with strike price
struct CallOption {
    strike: f64,
}

impl Antifragile for CallOption {
    type Stressor = f64;  // Underlying asset price
    type Payoff = f64;    // Option intrinsic value

    fn payoff(&self, price: f64) -> f64 {
        (price - self.strike).max(0.0)
    }
}

/// A portfolio of options
struct OptionsPortfolio {
    options: Vec<(CallOption, i32)>,  // (option, quantity: positive=long, negative=short)
}

impl Antifragile for OptionsPortfolio {
    type Stressor = f64;
    type Payoff = f64;

    fn payoff(&self, price: f64) -> f64 {
        self.options
            .iter()
            .map(|(opt, qty)| opt.payoff(price) * (*qty as f64))
            .sum()
    }
}

fn main() {
    // Single call option analysis
    let option = CallOption { strike: 100.0 };

    // At-the-money analysis (price = strike)
    let at = 100.0;
    let delta = 10.0;

    println!("Call Option (strike: $100)");
    println!("  Payoff at ${}: ${:.2}", at - delta, option.payoff(at - delta));
    println!("  Payoff at ${}: ${:.2}", at, option.payoff(at));
    println!("  Payoff at ${}: ${:.2}", at + delta, option.payoff(at + delta));
    println!("  Classification: {}", option.classify(at, delta));
    // Output: Antifragile (benefits from volatility)

    // Verify the option is antifragile
    let verified = Verified::check(option, at, delta);
    assert!(verified.is_antifragile());

    // Portfolio analysis: long call spread
    let portfolio = OptionsPortfolio {
        options: vec![
            (CallOption { strike: 100.0 }, 1),   // Long 1 call at 100
            (CallOption { strike: 110.0 }, -1), // Short 1 call at 110
        ],
    };

    println!("\nCall Spread Portfolio");
    println!("  Classification: {}", portfolio.classify(105.0, 5.0));

    // Check if portfolio gains from stress
    if portfolio.gains_from_stress(100.0, 120.0) {
        println!("  Portfolio profits from price increase");
    }
}
```

--------------------------------

### TriadAnalysis::classify Method

Source: https://context7.com/minikin/antifragile/llms.txt

Explains how to use the `classify` method from the `TriadAnalysis` trait to determine if a system is Antifragile, Fragile, or Robust based on its payoff function's convexity.

```APIDOC
## TriadAnalysis::classify

Classifies a system on Taleb's Triad at a specific operating point using the convexity test. Returns `Triad::Antifragile` for convex payoffs, `Triad::Fragile` for concave payoffs, and `Triad::Robust` for linear payoffs.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis};

struct ConvexSystem;  // f(x) = x² - benefits from volatility
impl Antifragile for ConvexSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

struct ConcaveSystem;  // f(x) = √x - harmed by volatility
impl Antifragile for ConcaveSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x.abs().sqrt() }
}

struct LinearSystem;  // f(x) = 2x + 5 - unaffected by volatility
impl Antifragile for LinearSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { 2.0 * x + 5.0 }
}

let convex = ConvexSystem;
let concave = ConcaveSystem;
let linear = LinearSystem;

let at = 10.0;    // Operating point
let delta = 1.0;  // Perturbation size

// Classify each system
assert_eq!(convex.classify(at, delta), Triad::Antifragile);  // Convex -> Antifragile
assert_eq!(concave.classify(at, delta), Triad::Fragile);     // Concave -> Fragile
assert_eq!(linear.classify(at, delta), Triad::Robust);       // Linear -> Robust

// Print classifications with full description
println!("{}", convex.classify(at, delta));   // "Antifragile (benefits from volatility)"
println!("{}", concave.classify(at, delta));  // "Fragile (harmed by volatility)"
println!("{}", linear.classify(at, delta));   // "Robust (unaffected by volatility)"
```
```

--------------------------------

### Classify Systems using TriadAnalysis

Source: https://context7.com/minikin/antifragile/llms.txt

Use the classify method to determine if a system is Antifragile, Fragile, or Robust based on its response to perturbations at a specific operating point.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis};

struct ConvexSystem;  // f(x) = x² - benefits from volatility
impl Antifragile for ConvexSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

struct ConcaveSystem;  // f(x) = √x - harmed by volatility
impl Antifragile for ConcaveSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x.abs().sqrt() }
}

struct LinearSystem;  // f(x) = 2x + 5 - unaffected by volatility
impl Antifragile for LinearSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { 2.0 * x + 5.0 }
}

let convex = ConvexSystem;
let concave = ConcaveSystem;
let linear = LinearSystem;

let at = 10.0;    // Operating point
let delta = 1.0;  // Perturbation size

// Classify each system
assert_eq!(convex.classify(at, delta), Triad::Antifragile);  // Convex -> Antifragile
assert_eq!(concave.classify(at, delta), Triad::Fragile);     // Concave -> Fragile
assert_eq!(linear.classify(at, delta), Triad::Robust);       // Linear -> Robust

// Print classifications with full description
println!("{}", convex.classify(at, delta));   // "Antifragile (benefits from volatility)"
println!("{}", concave.classify(at, delta));  // "Fragile (harmed by volatility)"
println!("{}", linear.classify(at, delta));   // "Robust (unaffected by volatility)"
```

--------------------------------

### Check for antifragility at an operating point

Source: https://context7.com/minikin/antifragile/llms.txt

Determines if a system exhibits a convex response to stress at a specific point.

```rust
use antifragile::{Antifragile, TriadAnalysis};

struct OptionsPortfolio {
    vega_exposure: f64,  // Long volatility position
}

impl Antifragile for OptionsPortfolio {
    type Stressor = f64;  // Market volatility
    type Payoff = f64;    // Portfolio P&L

    fn payoff(&self, volatility: f64) -> f64 {
        self.vega_exposure * volatility * volatility
    }
}

let portfolio = OptionsPortfolio { vega_exposure: 1.0 };

// Quick check if portfolio benefits from volatility
if portfolio.is_antifragile(0.2, 0.05) {
    println!("Portfolio is antifragile - it benefits from market volatility!");
} else {
    println!("Portfolio does not benefit from volatility");
}

assert!(portfolio.is_antifragile(0.2, 0.05));  // true
```

--------------------------------

### Tear down the environment

Source: https://github.com/minikin/antifragile/blob/main/examples/adaptive-pricing-api/README.md

Removes containers and volumes associated with the project.

```bash
docker compose down -v
```

--------------------------------

### Triad Enum Usage

Source: https://context7.com/minikin/antifragile/llms.txt

Demonstrates creating, comparing, sorting, and converting Triad enum variants. Includes usage of classification predicates, rank, opposite, iteration, string parsing, and conversion to/from u8. Defaults to Robust.

```rust
use antifragile::Triad;

// Create and compare classifications
let classification = Triad::Antifragile;

// Check classification type
assert!(classification.is_antifragile());
assert!(!classification.is_fragile());
assert!(!classification.is_robust());

// Ordering by desirability
assert!(Triad::Fragile < Triad::Robust);
assert!(Triad::Robust < Triad::Antifragile);

// Sort systems by desirability
let mut triads = vec![Triad::Antifragile, Triad::Fragile, Triad::Robust];
triads.sort();
assert_eq!(triads, vec![Triad::Fragile, Triad::Robust, Triad::Antifragile]);

// Get desirability rank (Fragile=0, Robust=1, Antifragile=2)
assert_eq!(Triad::Fragile.rank(), 0);
assert_eq!(Triad::Robust.rank(), 1);
assert_eq!(Triad::Antifragile.rank(), 2);

// Get opposite classification
assert_eq!(Triad::Antifragile.opposite(), Triad::Fragile);
assert_eq!(Triad::Fragile.opposite(), Triad::Antifragile);
assert_eq!(Triad::Robust.opposite(), Triad::Robust);  // Robust is self-opposite

// Iterate over all variants
for triad in Triad::iter() {
    println!("{}: rank {}", triad, triad.rank());
}

// Convert to/from strings (case insensitive parsing)
let s: &str = Triad::Antifragile.into();  // "antifragile"
let parsed: Triad = "robust".parse().unwrap();
let upper: Triad = "FRAGILE".parse().unwrap();

// Convert to/from u8
let n: u8 = Triad::Antifragile.into();  // 2
let from_n = Triad::try_from(1u8).unwrap();  // Triad::Robust

// Default is Robust (neutral)
assert_eq!(Triad::default(), Triad::Robust);

```

--------------------------------

### Classify systems with numerical tolerance

Source: https://context7.com/minikin/antifragile/llms.txt

Uses epsilon-based comparison to classify systems where floating-point precision might otherwise cause false results.

```rust
use antifragile::{Antifragile, Triad, TriadAnalysis};

struct NearlyLinear;
impl Antifragile for NearlyLinear {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff {
        2.0 * x + 1e-10 * x * x  // Almost linear with tiny convexity
    }
}

let system = NearlyLinear;

// Exact classification detects the tiny convexity
assert_eq!(system.classify(10.0, 1.0), Triad::Antifragile);

// With tolerance, it's effectively Robust
assert_eq!(system.classify_with_tolerance(10.0, 1.0, 1e-6), Triad::Robust);

// Different tolerance levels for different precision needs
let tight = system.classify_with_tolerance(10.0, 1.0, 1e-15);  // Antifragile
let loose = system.classify_with_tolerance(10.0, 1.0, 1.0);    // Robust
println!("Tight tolerance: {:?}, Loose tolerance: {:?}", tight, loose);
```

--------------------------------

### Push Git Tag

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Push the newly created Git tag to the remote repository. This makes the release tag visible to others and is part of the automated release process.

```bash
git push origin v0.x.y
```

--------------------------------

### Conventional Commit Types

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Lists the standard types used in conventional commits, such as 'feat' for new features, 'fix' for bug fixes, and 'docs' for documentation changes.

```markdown
- feat: New feature
- fix: Bug fix
- docs: Documentation changes
- test: Adding or updating tests
- refactor: Code refactoring
- perf: Performance improvements
- chore: Maintenance tasks
```

--------------------------------

### Implement the Antifragile Trait

Source: https://context7.com/minikin/antifragile/llms.txt

Define custom systems by implementing the Antifragile trait, specifying stressor and payoff types along with the payoff function.

```rust
use antifragile::Antifragile;

// Define a financial call option with convex payoff
struct CallOption {
    strike: f64,
}

impl Antifragile for CallOption {
    type Stressor = f64;  // Underlying price
    type Payoff = f64;    // Option value

    fn payoff(&self, price: f64) -> f64 {
        (price - self.strike).max(0.0)
    }
}

// Define an insurance portfolio with concave payoff
struct InsurancePortfolio {
    premium_collected: f64,
    max_liability: f64,
}

impl Antifragile for InsurancePortfolio {
    type Stressor = f64;  // Claim rate
    type Payoff = f64;    // Profit

    fn payoff(&self, claim_rate: f64) -> f64 {
        self.premium_collected - (claim_rate * self.max_liability).min(self.max_liability)
    }
}

let option = CallOption { strike: 100.0 };
let portfolio = InsurancePortfolio {
    premium_collected: 1000.0,
    max_liability: 10000.0,
};

// Access payoff at different stress levels
println!("Option payoff at price 110: {}", option.payoff(110.0));  // 10.0
println!("Option payoff at price 90: {}", option.payoff(90.0));    // 0.0
println!("Portfolio profit at 5% claims: {}", portfolio.payoff(0.05)); // 500.0
```

--------------------------------

### Verify gains from increased stress

Source: https://context7.com/minikin/antifragile/llms.txt

Tests if a system's payoff improves when moving from a low stress level to a high stress level.

```rust
use antifragile::{Antifragile, TriadAnalysis};

struct LearningSystem {
    learning_rate: f64,
}

impl Antifragile for LearningSystem {
    type Stressor = f64;  // Training iterations
    type Payoff = f64;    // Model accuracy

    fn payoff(&self, iterations: f64) -> f64 {
        // Accuracy improves with more training (logarithmic improvement)
        1.0 - 1.0 / (1.0 + self.learning_rate * iterations)
    }
}

let model = LearningSystem { learning_rate: 0.1 };

// Does more training improve accuracy?
assert!(model.gains_from_stress(100.0, 1000.0));  // true - more training helps

// Check at different levels
let low_stress = 10.0;
let high_stress = 100.0;
if model.gains_from_stress(low_stress, high_stress) {
    println!("Model improves with more training iterations");
}
```

--------------------------------

### Implement Antifragile Trait

Source: https://github.com/minikin/antifragile/blob/main/README.md

Define the payoff function for a custom system to determine its response to stress.

```rust
use antifragile::Antifragile;

struct MySystem;

impl Antifragile for MySystem {
    type Stressor = f64;  // The type of stress applied
    type Payoff = f64;    // The outcome produced

    fn payoff(&self, stressor: Self::Stressor) -> Self::Payoff {
        // Define your system's response to stress
        stressor * stressor
    }
}
```

--------------------------------

### Conventional Commits Format

Source: https://github.com/minikin/antifragile/blob/main/CONTRIBUTING.md

Specifies the conventional commits format for commit messages, including type, scope, subject, body, and footer. This standardizes commit history for better understanding and automation.

```markdown
type(scope): subject

body

footer
```

--------------------------------

### Assess system stability across stress levels

Source: https://context7.com/minikin/antifragile/llms.txt

Checks if the payoff variation between two stress levels remains within a specified threshold.

```rust
use antifragile::{Antifragile, TriadAnalysis};

struct ConstantSystem;
impl Antifragile for ConstantSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, _: Self::Stressor) -> Self::Payoff { 10.0 }
}

struct VolatileSystem;
impl Antifragile for VolatileSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

let constant = ConstantSystem;
let volatile = VolatileSystem;

// Constant system is perfectly stable
assert!(constant.is_stable(1.0, 100.0, 0.001));

// Volatile system is not stable (payoff varies greatly)
assert!(!volatile.is_stable(1.0, 10.0, 1.0));  // |1 - 100| = 99 > 1

// Use for risk assessment
let threshold = 50.0;
if volatile.is_stable(5.0, 10.0, threshold) {
    println!("Acceptable volatility range");
} else {
    println!("Warning: High volatility detected");  // This prints
}
```

--------------------------------

### TriadAnalysis::classify_with_tolerance

Source: https://context7.com/minikin/antifragile/llms.txt

Classifies a system by considering numerical tolerance for floating-point payoffs. Values within a specified epsilon are treated as equal, which is useful for systems with near-exact or imprecise payoffs.

```APIDOC
## TriadAnalysis::classify_with_tolerance

### Description
Classifies a system with numerical tolerance for floating-point payoffs where exact equality is rare due to precision issues. Values within epsilon of each other are treated as equal.

### Method
This is a method call on a type implementing the `Antifragile` trait.

### Endpoint
N/A (Rust method call)

### Parameters
- **`self`**: The system instance implementing `Antifragile`.
- **`operating_point`** (f64) - The specific stress level at which to classify.
- **`delta`** (f64) - The change in stress to observe the payoff difference.
- **`epsilon`** (f64) - The tolerance for considering two floating-point values as equal.

### Request Example
```rust
use antifragile::{Antifragile, Triad, TriadAnalysis};

struct NearlyLinear;
impl Antifragile for NearlyLinear {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff {
        2.0 * x + 1e-10 * x * x  // Almost linear with tiny convexity
    }
}

let system = NearlyLinear;

// Exact classification detects the tiny convexity
assert_eq!(system.classify(10.0, 1.0), Triad::Antifragile);

// With tolerance, it's effectively Robust
assert_eq!(system.classify_with_tolerance(10.0, 1.0, 1e-6), Triad::Robust);

// Different tolerance levels for different precision needs
let tight = system.classify_with_tolerance(10.0, 1.0, 1e-15);  // Antifragile
let loose = system.classify_with_tolerance(10.0, 1.0, 1.0);    // Robust
println!("Tight tolerance: {:?}, Loose tolerance: {:?}", tight, loose);
```

### Response
#### Success Response (Triad)
- **`Triad`** (enum) - The classification of the system (e.g., `Antifragile`, `Robust`, `Perverse`).

#### Response Example
```rust
// Example output from the code snippet:
Tight tolerance: Antifragile, Loose tolerance: Robust
```
```

--------------------------------

### TriadAnalysis::is_stable

Source: https://context7.com/minikin/antifragile/llms.txt

Assesses the stability of a system's payoff across different stress levels by comparing payoff differences to a threshold.

```APIDOC
## TriadAnalysis::is_stable

### Description
Checks if the payoff is stable across stress levels by comparing the absolute difference to a threshold. Returns true if the system's output doesn't vary significantly with stress changes.

### Method
This is a method call on a type implementing the `Antifragile` trait.

### Endpoint
N/A (Rust method call)

### Parameters
- **`self`**: The system instance implementing `Antifragile`.
- **`low_stress`** (f64) - The lower level of stress.
- **`high_stress`** (f64) - The higher level of stress.
- **`threshold`** (f64) - The maximum acceptable absolute difference in payoff between `low_stress` and `high_stress`.

### Request Example
```rust
use antifragile::{Antifragile, TriadAnalysis};

struct ConstantSystem;
impl Antifragile for ConstantSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, _: Self::Stressor) -> Self::Payoff { 10.0 }
}

struct VolatileSystem;
impl Antifragile for VolatileSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, x: Self::Stressor) -> Self::Payoff { x * x }
}

let constant = ConstantSystem;
let volatile = VolatileSystem;

// Constant system is perfectly stable
assert!(constant.is_stable(1.0, 100.0, 0.001));

// Volatile system is not stable (payoff varies greatly)
assert!(!volatile.is_stable(1.0, 10.0, 1.0));  // |1 - 100| = 99 > 1

// Use for risk assessment
let threshold = 50.0;
if volatile.is_stable(5.0, 10.0, threshold) {
    println!("Acceptable volatility range");
} else {
    println!("Warning: High volatility detected");  // This prints
}
```

### Response
#### Success Response (Boolean)
- **`true`** (bool) - If the absolute difference between payoffs at `low_stress` and `high_stress` is less than or equal to the `threshold`.
- **`false`** (bool) - Otherwise.

#### Response Example
```rust
// Example output from the code snippet:
// Warning: High volatility detected
```
```

--------------------------------

### Classify with Triad Enum

Source: https://github.com/minikin/antifragile/blob/main/README.md

Use the Triad enum to represent and manipulate system classifications.

```rust
use antifragile::Triad;

let classification = Triad::Antifragile;

// Check classification
assert!(classification.is_antifragile());

// Ordering: Fragile < Robust < Antifragile
assert!(Triad::Fragile < Triad::Robust);
assert!(Triad::Robust < Triad::Antifragile);

// Convert to/from strings
let s: &str = classification.into();  // "antifragile"
let parsed: Triad = "robust".parse().unwrap();

// Convert to/from u8 (Fragile=0, Robust=1, Antifragile=2)
let n: u8 = classification.into();  // 2
let from_n = Triad::try_from(1u8).unwrap();  // Triad::Robust
```

--------------------------------

### TriadAnalysis::is_antifragile

Source: https://context7.com/minikin/antifragile/llms.txt

A convenience method to determine if a system exhibits antifragile behavior, meaning its payoff increases with stress (convex response).

```APIDOC
## TriadAnalysis::is_antifragile

### Description
Convenience method to check if a system is antifragile at a given operating point. Returns true only if the system has a convex response to stress.

### Method
This is a method call on a type implementing the `Antifragile` trait.

### Endpoint
N/A (Rust method call)

### Parameters
- **`self`**: The system instance implementing `Antifragile`.
- **`operating_point`** (f64) - The specific stress level at which to evaluate the payoff.
- **`delta`** (f64) - The change in stress to observe the payoff difference.

### Request Example
```rust
use antifragile::{Antifragile, TriadAnalysis};

struct OptionsPortfolio { vega_exposure: f64 }
impl Antifragile for OptionsPortfolio {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, volatility: f64) -> f64 {
        self.vega_exposure * volatility * volatility
    }
}

let portfolio = OptionsPortfolio { vega_exposure: 1.0 };

// Quick check if portfolio benefits from volatility
if portfolio.is_antifragile(0.2, 0.05) {
    println!("Portfolio is antifragile - it benefits from market volatility!");
} else {
    println!("Portfolio does not benefit from volatility");
}

assert!(portfolio.is_antifragile(0.2, 0.05));  // true
```

### Response
#### Success Response (Boolean)
- **`true`** (bool) - If the system's payoff increases with a small increase in stress (convex response).
- **`false`** (bool) - Otherwise.

#### Response Example
```rust
// Example output from the code snippet:
// Portfolio is antifragile - it benefits from market volatility!
```
```

--------------------------------

### TriadAnalysis::gains_from_stress

Source: https://context7.com/minikin/antifragile/llms.txt

Determines if a system's payoff improves with increased stress, useful for systems that learn or improve with exposure.

```APIDOC
## TriadAnalysis::gains_from_stress

### Description
Practical test to determine if higher stress leads to better payoff. Returns true if payoff(high_stress) > payoff(low_stress). Useful for learning systems where payoff improves with exposure.

### Method
This is a method call on a type implementing the `Antifragile` trait.

### Endpoint
N/A (Rust method call)

### Parameters
- **`self`**: The system instance implementing `Antifragile`.
- **`low_stress`** (f64) - The lower level of stress.
- **`high_stress`** (f64) - The higher level of stress.

### Request Example
```rust
use antifragile::{Antifragile, TriadAnalysis};

struct LearningSystem { learning_rate: f64 }
impl Antifragile for LearningSystem {
    type Stressor = f64;
    type Payoff = f64;
    fn payoff(&self, iterations: f64) -> f64 {
        // Accuracy improves with more training (logarithmic improvement)
        1.0 - 1.0 / (1.0 + self.learning_rate * iterations)
    }
}

let model = LearningSystem { learning_rate: 0.1 };

// Does more training improve accuracy?
assert!(model.gains_from_stress(100.0, 1000.0));  // true - more training helps

// Check at different levels
let low_stress = 10.0;
let high_stress = 100.0;
if model.gains_from_stress(low_stress, high_stress) {
    println!("Model improves with more training iterations");
}
```

### Response
#### Success Response (Boolean)
- **`true`** (bool) - If the payoff at `high_stress` is greater than the payoff at `low_stress`.
- **`false`** (bool) - Otherwise.

#### Response Example
```rust
// Example output from the code snippet:
// Model improves with more training iterations
```
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.