Agentic Engineering
affaan-m/ECC
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .claude/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .claude/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .agents/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .agents/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .cursor/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .cursor/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ruvnet/ruflo.git --path .agents/skills/agent-v3-performance-engineer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .gemini/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .gemini/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .github/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .github/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ruvnet/ruflo agent-v3-performance-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/agent-v3-performance-engineer .opencode/skills/agent-v3-performance-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agent-v3-performance-engineer" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-performance-engineer into .opencode/skills/agent-v3-performance-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-v3-performance-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
agent-v3-performance-engineerAgent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
Agent V3 Performance Engineer is an agent skill from ruvnet/ruflo. Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.
Read from SKILL.md and the folder at commit de590e1. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Agent V3 Performance Engineer loads about 3.2k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 357 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 357 words, ~3,185 tokens.
.claude/skills/agent-v3-performance-engineer/SKILL.md (or your agent's skills folder).name: v3-performance-engineer version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Performance Engineer for achieving aggressive performance targets. Responsible for 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, and comprehensive benchmarking suite. color: yellow metadata: v3_role: "specialist" agent_id: 14 priority: "high" domain: "performance" phase: "optimization" hooks: pre_execution: | echo "⚡ V3 Performance Engineer starting optimization mission..."
echo "🎯 Performance targets:"
echo " • Flash Attention: 2.49x-7.47x speedup"
echo " • AgentDB Search: 150x-12,500x improvement"
echo " • Memory Usage: 50-75% reduction"
echo " • Startup Time: <500ms"
echo " • SONA Learning: <0.05ms adaptation"
# Check performance tools
command -v npm &>$dev$null && echo "📦 npm available for benchmarking"
command -v node &>$dev$null && node --version | xargs echo "🚀 Node.js:"
echo "🔬 Ready to validate aggressive performance targets"post_execution: | echo "⚡ Performance optimization milestone complete"
# Store performance patterns
npx agentic-flow@alpha memory store-pattern \
--session-id "v3-perf-$(date +%s)" \
--task "Performance: $TASK" \
--agent "v3-performance-engineer" \
--performance-targets "2.49x-7.47x" 2>$dev$null || true
⚡ Performance Optimization & Benchmark Validation Specialist
Validate and optimize claude-flow v3 to achieve industry-leading performance improvements through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization.
┌─────────────────────────────────────────┐
│ FLASH ATTENTION │
├─────────────────────────────────────────┤
│ Baseline: Standard attention mechanism │
│ Target: 2.49x - 7.47x speedup │
│ Memory: 50-75% reduction │
│ Method: agentic-flow@alpha integration│
└─────────────────────────────────────────┘┌─────────────────────────────────────────┐
│ SEARCH OPTIMIZATION │
├─────────────────────────────────────────┤
│ Current: O(n) linear search │
│ Target: 150x - 12,500x improvement │
│ Method: AgentDB HNSW indexing │
│ Latency: Sub-100ms for 1M+ entries │
└─────────────────────────────────────────┘┌─────────────────────────────────────────┐
│ SYSTEM PERFORMANCE │
├─────────────────────────────────────────┤
│ Startup: <500ms (cold start) │
│ Memory: 50-75% reduction │
│ SONA: <0.05ms adaptation │
│ Code Size: <5k lines (vs 15k+) │
└─────────────────────────────────────────┘class StartupBenchmarks {
async benchmarkColdStart(): Promise<BenchmarkResult> {
const startTime = performance.now();
// Measure CLI initialization
await this.initializeCLI();
const cliTime = performance.now() - startTime;
// Measure MCP server startup
const mcpStart = performance.now();
await this.initializeMCPServer();
const mcpTime = performance.now() - mcpStart;
// Measure agent spawn latency
const spawnStart = performance.now();
await this.spawnTestAgent();
const spawnTime = performance.now() - spawnStart;
return {
total: performance.now() - startTime,
cli: cliTime,
mcp: mcpTime,
agentSpawn: spawnTime,
target: 500 // ms
};
}
}class MemoryBenchmarks {
async benchmarkVectorSearch(): Promise<SearchBenchmark> {
const testQueries = this.generateTestQueries(10000);
// Baseline: Current linear search
const baselineStart = performance.now();
for (const query of testQueries) {
await this.currentMemory.search(query);
}
const baselineTime = performance.now() - baselineStart;
// Target: HNSW search
const hnswStart = performance.now();
for (const query of testQueries) {
await this.agentDBMemory.hnswSearch(query);
}
const hnswTime = performance.now() - hnswStart;
const improvement = baselineTime / hnswTime;
return {
baseline: baselineTime,
hnsw: hnswTime,
improvement,
targetRange: [150, 12500],
achieved: improvement >= 150
};
}
async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {
const baseline = process.memoryUsage();
// Load test data
await this.loadTestDataset();
const withData = process.memoryUsage();
// Test compression
await this.enableMemoryOptimization();
const optimized = process.memoryUsage();
const reduction = (withData.heapUsed - optimized.heapUsed) / withData.heapUsed;
return {
baseline: baseline.heapUsed,
withData: withData.heapUsed,
optimized: optimized.heapUsed,
reductionPercent: reduction * 100,
targetReduction: [50, 75],
achieved: reduction >= 0.5
};
}
}class SwarmBenchmarks {
async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {
// Initialize 15-agent swarm
const agents = await this.spawn15Agents();
// Measure coordination latency
const coordinationStart = performance.now();
await this.coordinateSwarmTask(agents);
const coordinationTime = performance.now() - coordinationStart;
// Measure task decomposition
const decompositionStart = performance.now();
const tasks = await this.decomposeComplexTask();
const decompositionTime = performance.now() - decompositionStart;
// Measure consensus achievement
const consensusStart = performance.now();
await this.achieveSwarmConsensus(agents);
const consensusTime = performance.now() - consensusStart;
return {
coordination: coordinationTime,
decomposition: decompositionTime,
consensus: consensusTime,
agents: agents.length,
efficiency: this.calculateSwarmEfficiency(agents)
};
}
}class AttentionBenchmarks {
async benchmarkFlashAttention(): Promise<AttentionBenchmark> {
const testSequences = this.generateTestSequences([512, 1024, 2048, 4096]);
const results = [];
for (const sequence of testSequences) {
// Baseline attention
const baselineStart = performance.now();
const baselineMemory = process.memoryUsage();
await this.standardAttention(sequence);
const baselineTime = performance.now() - baselineStart;
const baselineMemoryPeak = process.memoryUsage().heapUsed - baselineMemory.heapUsed;
// Flash attention
const flashStart = performance.now();
const flashMemory = process.memoryUsage();
await this.flashAttention(sequence);
const flashTime = performance.now() - flashStart;
const flashMemoryPeak = process.memoryUsage().heapUsed - flashMemory.heapUsed;
results.push({
sequenceLength: sequence.length,
speedup: baselineTime / flashTime,
memoryReduction: (baselineMemoryPeak - flashMemoryPeak) / baselineMemoryPeak,
targetSpeedup: [2.49, 7.47],
targetMemoryReduction: [0.5, 0.75]
});
}
return {
results,
averageSpeedup: results.reduce((sum, r) => sum + r.speedup, 0) / results.length,
averageMemoryReduction: results.reduce((sum, r) => sum + r.memoryReduction, 0) / results.length
};
}
}class SONABenchmarks {
async benchmarkAdaptationTime(): Promise<SONABenchmark> {
const adaptationScenarios = [
'pattern_recognition',
'task_optimization',
'error_correction',
'performance_tuning',
'behavior_adaptation'
];
const results = [];
for (const scenario of adaptationScenarios) {
const adaptationStart = performance.hrtime.bigint();
await this.sona.adapt(scenario);
const adaptationEnd = performance.hrtime.bigint();
const adaptationTimeMs = Number(adaptationEnd - adaptationStart) / 1000000;
results.push({
scenario,
adaptationTime: adaptationTimeMs,
target: 0.05, // ms
achieved: adaptationTimeMs <= 0.05
});
}
return {
scenarios: results,
averageAdaptation: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,
successRate: results.filter(r => r.achieved).length / results.length
};
}
}class PerformanceMonitor {
private metrics = {
flashAttentionSpeedup: new MetricCollector('flash_attention_speedup'),
searchImprovement: new MetricCollector('search_improvement'),
memoryReduction: new MetricCollector('memory_reduction'),
startupTime: new MetricCollector('startup_time'),
sonaAdaptation: new MetricCollector('sona_adaptation')
};
async collectMetrics(): Promise<PerformanceSnapshot> {
return {
timestamp: Date.now(),
flashAttention: await this.metrics.flashAttentionSpeedup.current(),
searchPerformance: await this.metrics.searchImprovement.current(),
memoryUsage: await this.metrics.memoryReduction.current(),
startup: await this.metrics.startupTime.current(),
sona: await this.metrics.sonaAdaptation.current(),
targets: this.getTargetMetrics()
};
}
async generateReport(): Promise<PerformanceReport> {
const snapshot = await this.collectMetrics();
return {
summary: this.generateSummary(snapshot),
achievements: this.checkAchievements(snapshot),
recommendations: this.generateRecommendations(snapshot),
trends: this.analyzeTrends(),
nextActions: this.suggestOptimizations()
};
}
}class PerformanceRegression {
async detectRegressions(): Promise<RegressionReport> {
const current = await this.runFullBenchmarkSuite();
const baseline = await this.getBaselineMetrics();
const regressions = [];
// Check each performance metric
for (const [metric, currentValue] of Object.entries(current)) {
const baselineValue = baseline[metric];
const change = (currentValue - baselineValue) / baselineValue;
if (change < -0.05) { // 5% regression threshold
regressions.push({
metric,
baseline: baselineValue,
current: currentValue,
regressionPercent: change * 100
});
}
}
return {
hasRegressions: regressions.length > 0,
regressions,
recommendations: this.generateRegressionFixes(regressions)
};
}
}⚡ Mission: Validate and achieve industry-leading performance improvements that make claude-flow v3 the fastest and most efficient agent orchestration platform.
© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/agent-v3-performance-engineer of ruvnet/ruflo.
Open the folder on GitHubat commit de590e1
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.
Agent V3 Performance Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Agent V3 Performance Engineer this skillruvnet/ruflo | 74k | 2 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Agentic Engineeringaffaan-m/ECC | 274k | — | ~986 | Automated safety check: Pass | MIT | |
| Prompt Engineeringdavila7/claude-code-templates | 32k | 7 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Chaos Engineeringalirezarezvani/claude-skills | 28k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Context Engineering Collectionmuratcankoylan/Agent-Skills-for-Context-Engineering | 18k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Prompt EngineerRightNow-AI/openfang | 18k | — | ~829 | Automated safety check: Pass | Apache-2.0 |
affaan-m/ECC
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
davila7/claude-code-templates
Expert guide on prompt engineering patterns, best practices, and optimization techniques.
alirezarezvani/claude-skills
A skill your agent uses when planning, running, or learning from chaos engineering experiments.
muratcankoylan/Agent-Skills-for-Context-Engineering
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems.
RightNow-AI/openfang
Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM optimization
wshobson/agents
Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation.
ruvnet/ruflo
Stores, searches, and retrieves successful patterns with HNSW-indexed semantic search so agents can reuse past solutions instead of relearning them.
ruvnet/ruflo
Runs claude-flow CLI security scans for input validation, path traversal, SQL injection, XSS, hardcoded secrets and known CVEs, and writes an audit report.
ruvnet/ruflo
Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.
ruvnet/ruflo
Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.
ruvnet/ruflo
Sets up and drives Ruflo, an npm-installed orchestration layer for multi-agent swarms, persistent memory, routing, hooks and its MCP tool catalog.
ruvnet/ruflo
Reference for spawning, listing, monitoring and stopping agents with claude-flow commands, with agent type families, routing codes and coordination tips.
Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer. Agent V3 Performance Engineer is an agent skill from ruvnet/ruflo.
Run `npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a claude-code`. Or copy the skill folder (.agents/skills/agent-v3-performance-engineer in ruvnet/ruflo) into .claude/skills/agent-v3-performance-engineer in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a codex`. Or copy the skill folder (.agents/skills/agent-v3-performance-engineer in ruvnet/ruflo) into .agents/skills/agent-v3-performance-engineer in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-v3-performance-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-v3-performance-engineer, .gemini/skills/agent-v3-performance-engineer, .github/skills/agent-v3-performance-engineer and .opencode/skills/agent-v3-performance-engineer in your project.
SKILL.md names no scripts, command-line tools or credentials: Agent V3 Performance Engineer is instructions for the agent only. Our summary lists: Node.js.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Agent V3 Performance Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Agent V3 Performance Engineer: Agentic Engineering (affaan-m/ECC, 274k stars), Prompt Engineering (davila7/claude-code-templates, 32k stars), Chaos Engineering (alirezarezvani/claude-skills, 28k stars) and Context Engineering Collection (muratcankoylan/Agent-Skills-for-Context-Engineering, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.
Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.