agent-capabilities
CommandMatrix of agent capabilities and their specializations.
Coding challenges, achievements, and leaderboards
> /plugin marketplace add ruvnet/agentic-flow> /plugin install agentic-flow@agentic-flow
name: flow-nexus-challenges description: Coding challenges, achievements, and leaderboards
Complete coding challenges to earn rUv credits and climb the leaderboard.
mcp__flow-nexus__challenges_list({
difficulty: "intermediate", // beginner, advanced, expert
category: "algorithms",
status: "active",
limit: 20
})mcp__flow-nexus__challenge_get({
challenge_id: "two-sum-problem"
})mcp__flow-nexus__challenge_submit({
challenge_id: "challenge_id",
user_id: "your_id",
solution_code: `
function solution(nums, target) {
const map = new Map();
for (let i = 0; i < nums.length; i++) {
const complement = target - nums[i];
if (map.has(complement)) {
return [map.get(complement), i];
}
map.set(nums[i], i);
}
return [];
}
`,
language: "javascript",
execution_time: 45 // milliseconds
})mcp__flow-nexus__app_store_complete_challenge({
challenge_id: "challenge_id",
user_id: "your_id",
submission_data: {
passed_tests: 10,
total_tests: 10,
execution_time: 45
}
})// Global leaderboard
mcp__flow-nexus__leaderboard_get({
type: "global", // weekly, monthly, challenge
limit: 10
})
// Challenge-specific leaderboard
mcp__flow-nexus__leaderboard_get({
type: "challenge",
challenge_id: "specific_challenge",
limit: 25
})mcp__flow-nexus__achievements_list({
user_id: "your_id",
category: "speed_demon" // Categories vary
})// Check balance
mcp__flow-nexus__ruv_balance({ user_id: "your_id" })
// View history
mcp__flow-nexus__ruv_history({
user_id: "your_id",
limit: 20
})
// Earn credits (automatic on completion)
mcp__flow-nexus__app_store_earn_ruv({
user_id: "your_id",
amount: 100,
reason: "Completed expert challenge",
source: "challenge"
})1. Start with beginner challenges 2. Review other solutions after completing 3. Optimize for both correctness and speed 4. Complete daily challenges for bonus credits 5. Unlock achievements for extra rewards
Production-ready AI agent orchestration with 66 self-learning agents, 213 MCP tools, and autonomous multi-agent swarms.
Matrix of agent capabilities and their specializations.
Complete guide to all 54 available agent types in Claude Flow.
Reviewed all command files in `.claude/commands/analysis/` directory to ensure proper usage of: - `mcp__claude-flow__*` tools…
Analyze performance bottlenecks in swarm operations and suggest optimizations.
Implement tasks incrementally — build, test, verify, commit. Add "auto" to run the whole plan in one approved pass.