TIG Innovator Skill

Autonomous algorithm optimization agent for The Innovation Game (TIG) protocol. This skill enables AI agents to earn cryptocurrency by optimizing algorithms and submitting improvements to TIG's decentralized marketplace.

Overview

TIG is a protocol that incentivizes algorithmic innovation. Innovators submit algorithm optimizations that compete for adoption by benchmarkers. If your algorithm is adopted by 25%+ of benchmarkers, you earn a share of block rewards.

How this skill makes money:

• Download existing TIG algorithms (free)

• Analyze code with LLM → suggest Rust optimizations

• Test locally to verify improvement (free)

• Submit only when competitive (costs 10 TIG ≈ $1)

• Earn recurring rewards if adopted

Ecosystem Integration

This skill can be wrapped as a paid Lucid Agent service -- offering algorithm optimization-as-a-service via x402 micropayments. Use lucid-agents-sdk to expose TIG optimization as paid entrypoints (e.g., analyze , optimize , benchmark ).

Quick Start

1. Set up environment

export ANTHROPIC_API_KEY="your-key" export TIG_NETWORK="testnet" # Start with testnet

2. List algorithms for a challenge

tig-innovator list vector_search

3. Analyze an algorithm for optimization opportunities

tig-innovator analyze <algorithm-id>

4. Generate optimized version

tig-innovator optimize <algorithm-id>

5. Test the optimization locally

tig-innovator test <algorithm-id> --optimized ./optimized.rs

6. Submit if improvement is significant (>5%)

tig-innovator submit <algorithm-id> --code ./optimized.rs

Commands

tig-innovator list <challenge>

Lists all algorithms for a challenge with performance metrics.

Arguments:

• challenge : One of satisfiability , vehicle_routing , knapsack , vector_search , hypergraph , neural_network

Options:

• --network <mainnet|testnet> : Network to query (default: mainnet)

• --limit <n> : Max algorithms to show (default: 20)

• --sort <adoption|score> : Sort order (default: adoption)

Example:

tig-innovator list vector_search --limit 10 --sort adoption

tig-innovator analyze <algorithm-id>

Downloads and analyzes an algorithm's source code for optimization opportunities.

Arguments:

• algorithm-id : The algorithm ID from the list command

Options:

• --detailed : Show detailed analysis with code snippets

Output:

• Optimization opportunities ranked by impact

• Specific code locations

• Suggested improvements

• Confidence scores

Example:

tig-innovator analyze c003_a042_0001

tig-innovator optimize <algorithm-id>

Generates an optimized version of the algorithm using LLM analysis.

Arguments:

• algorithm-id : The algorithm ID to optimize

Options:

• --output <path> : Output path for optimized code (default: ./optimized_.rs)

• --iterations <n> : Number of optimization iterations (default: 3)

• --focus <type> : Focus on specific optimization type: loop , memory , simd , cache , all

Example:

tig-innovator optimize c003_a042_0001 --output ./my_optimization.rs --iterations 5

tig-innovator test <algorithm-id>

Runs local benchmarks comparing original vs optimized algorithm.

Arguments:

• algorithm-id : The baseline algorithm ID

Options:

• --optimized <path> : Path to optimized code file

• --samples <n> : Number of test samples (default: 100)

Requirements:

• Docker must be running (uses TIG dev containers)

Example:

tig-innovator test c003_a042_0001 --optimized ./my_optimization.rs

tig-innovator submit <algorithm-id>

Submits optimized algorithm to TIG network.

Arguments:

• algorithm-id : Reference to the algorithm being improved

Options:

• --code <path> : Path to optimized code file

• --network <mainnet|testnet> : Target network (default: from env)

• --dry-run : Validate without submitting

Requirements (mainnet only):

• TIG_WALLET_ADDRESS environment variable

• TIG_PRIVATE_KEY environment variable

• Sufficient TIG balance (10 TIG per submission)

Example:

Testnet (free)

tig-innovator submit c003_a042_0001 --code ./optimized.rs --network testnet

Mainnet (costs 10 TIG)

tig-innovator submit c003_a042_0001 --code ./optimized.rs --network mainnet

tig-innovator status

Shows status of your submissions and earnings.

Options:

• --network <mainnet|testnet> : Network to query

Workflow Recommendations

For Best Results

Start with vector_search or neural_network - These challenges have the most opportunity for micro-optimizations that LLMs excel at (loop unrolling, SIMD, cache optimization).

Always test before submitting - The test command runs real benchmarks. Only submit if you see >5% improvement.

Use testnet first - Validate your workflow on testnet before spending TIG on mainnet.

Focus on adopted algorithms - Algorithms with high adoption have proven benchmarker interest. Improving them increases your chances of adoption.

Iterate multiple times - Run optimize with --iterations 5 or more. Each pass finds new opportunities.

Optimization Types

Type Description Best For

loop

Loop unrolling, vectorization All challenges

memory

Memory access patterns, allocation Large data sets

simd

SIMD intrinsics Numeric computation

cache

Cache-friendly data layout Matrix operations

branch

Branch prediction hints Conditional logic

Example Full Workflow

1. Find a good target

tig-innovator list vector_search --sort adoption --limit 5

2. Analyze top algorithm

tig-innovator analyze c003_a042_0001 --detailed

3. Generate optimization (multiple iterations)

tig-innovator optimize c003_a042_0001
--output ./vs_optimized.rs
--iterations 5
--focus all

4. Test locally

tig-innovator test c003_a042_0001 --optimized ./vs_optimized.rs

Output shows:

Baseline score: 1000

Optimized score: 1120

Improvement: +12%

Recommendation: SUBMIT ✓

5. Submit to testnet first

tig-innovator submit c003_a042_0001
--code ./vs_optimized.rs
--network testnet

6. If testnet succeeds, submit to mainnet

tig-innovator submit c003_a042_0001
--code ./vs_optimized.rs
--network mainnet

7. Monitor adoption

tig-innovator status --network mainnet

Environment Variables

Variable Required Description

ANTHROPIC_API_KEY

Yes API key for Claude (code analysis)

TIG_NETWORK

No Default network: mainnet or testnet

TIG_WALLET_ADDRESS

Mainnet Your TIG wallet address

TIG_PRIVATE_KEY

Mainnet Wallet private key for submissions

TIG_API_URL

No Custom API URL (default: api.tig.foundation)

Challenges Reference

Challenge Description Optimization Potential

vector_search

Nearest neighbor search HIGH - SIMD, cache

neural_network

NN training optimization HIGH - Matrix ops

knapsack

Quadratic knapsack problem MEDIUM - Loop opt

satisfiability

Boolean SAT solving MEDIUM - Branch pred

vehicle_routing

VRPTW optimization MEDIUM - Memory

hypergraph

Graph partitioning LOW - Algorithm-heavy

Troubleshooting

"Docker not found"

The test command requires Docker. Install Docker and ensure it's running.

"Compilation failed"

The optimized code must compile as valid Rust. Check:

• All imports are valid (no external crates allowed)

• Function signature matches solve_challenge

• Code is deterministic

"Score lower than baseline"

Not all optimizations improve performance. Try:

• Different --focus types

• More --iterations

• Different algorithm as baseline

"Insufficient TIG balance"

Mainnet submissions require 10 TIG. Get TIG from:

• Exchanges (if listed)

• TIG faucet (testnet only)

• Delegation rewards

Architecture

tig-innovator/ ├── src/ │ ├── cli.ts # CLI entrypoint │ ├── tig/ │ │ ├── api.ts # TIG API client │ │ ├── algorithms.ts # Algorithm fetching │ │ └── submission.ts # Submission logic │ ├── optimizer/ │ │ ├── analyzer.ts # LLM code analysis │ │ ├── generator.ts # Optimization generation │ │ └── validator.ts # Code validation │ └── benchmark/ │ ├── runner.ts # Docker benchmark runner │ └── comparator.ts # Score comparison └── docker/ └── tig-dev.dockerfile # TIG dev environment

License

MIT - Built for the Daydreams AI ecosystem.