القائمة
Use when orchestrate multi-skill workflows by routing tasks to the right agents and coordinating cross-platform operations.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/oyi77/1ai-skills --skill berkahkarya-orchestratorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Android and mobile application security testing — emulators, rooting, traffic interception, dynamic instrumentation. Use when testing mobile apps for vulnerabilities, reversing APKs, or bypassing security controls on Android.
Self-reflection + Self-criticism + Auto-learning from corrections + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when working with self improving.
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities. Use when working with conducting full scope red team engagement.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | berkahkarya-orchestrator |
| description | Use when orchestrate multi-skill workflows by routing tasks to the right agents and coordinating cross-platform operations. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["ai-agent","berkahkarya","infrastructure","memory","orchestrator","self-improvement","workflow"] |
| version | 1.0.0 |
Trigger phrases:
"berkahkarya orchestrator"
"Orchestrate multi-skill workflows by routing tasks to the right agents and coordinating cross-platform operations"
When the task falls within this skill's domain expertise
When automated execution saves time over manual work
When the skill's tools and integrations are available
The Berkahkarya Orchestrator is a foundational core infrastructure skill that provides system-wide coordination capabilities for the agent ecosystem. It operates as the central dispatch hub that receives incoming requests, decomposes them into executable subtasks, routes each subtask to the most capable skill agent, and reconciles the results into a coherent final response. Unlike simple linear pipelines, the orchestrator supports parallel execution branches, dependency-aware sequencing, and intelligent fallback strategies when primary agents fail.
The orchestrator maintains a dynamic skill registry that tracks every available agent's capabilities, current load, and historical performance. When a request arrives, the orchestrator queries this registry to match each subtask's domain requirements against registered agent capabilities, selecting the optimal primary candidate and a fallback chain. This routing intelligence improves over time through a feedback loop that captures success rates, latency, and dispatch accuracy for every executed workflow.
Cross-platform coordination is a core capability — the orchestrator manages operations across Telegram, WhatsApp, browser automation, REST APIs, file systems, and custom integrations. It handles platform-specific concerns such as credential lifecycle, rate limiting, message formatting, and connection state without exposing these details to downstream skill agents. Each agent receives a normalized context payload regardless of the originating platform.
The orchestrator's dependency graph engine enables complex multi-step workflows with branches, joins, and conditional paths. It detects circular dependencies before execution begins, enforces ordering constraints for dependent subtasks, and parallelizes independent branches automatically. This graph-based approach ensures that complex cross-platform operations execute in the correct sequence while maximizing throughput through concurrent execution of independent work.
| Rationalization | Reality |
|---|---|
| "I will add monitoring later" | Without monitoring, you cannot detect failures. Add it from day one. |
| "One model is enough" | Different tasks need different models. Route intelligently. |
| "Premature optimization" | Infrastructure decisions are hard to change later. Design for scale early. |
| "A single orchestrator config works for all tasks" | Different task types need different dispatch strategies, timeouts, and fallback chains. Profile per category. |
| "Manual routing is fine at this scale" | Manual routing doesn't scale beyond 3-4 agents. Automate dispatch before coordination overhead dominates. |
| "If one skill fails, retry the same skill" | Repeated retries without fallback waste time. Route to a different agent or degrade gracefully after first failure. |
# Example: Model routing
ROUTES = {
"code": ["claude-sonnet-4-20250514", "gpt-4o"],
"vision": ["gemini-2.5-pro", "gpt-4o"],
"fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}
def route_request(task: str, prompt: str):
models = ROUTES.get(task, ROUTES["fast"])
for model in models:
try:
return call_model(model, prompt)
except Exception:
continue
raise RuntimeError("All models failed")
const skillRegistry = {
code: ["claude-sonnet-4", "gpt-4o"],
vision: ["gemini-2.5-pro", "gpt-4o"],
fast: ["gemini-2.5-flash", "gpt-4o-mini"],
};
async function dispatch(taskType, prompt) {
const models = skillRegistry[taskType] ?? skillRegistry.fast;
for (const model of models) {
try {
return await callModel(model, prompt);
} catch (err) {
console.warn(`Model ${model} failed:`, err.message);
}
}
throw new Error(`All models failed for task=${taskType}`);
}
| Problem | Solution |
|---|---|
| Agent dispatch times out or hangs | Set per-agent timeout in dispatch config; implement circuit-breaker that degrades to fallback agents after 2 consecutive failures |
| State corruption across workflow steps | Use immutable state snapshots with explicit checkpointing between steps; validate state schema at each transition |
| Dependency deadlock between parallel tasks | Run dependency graph cycle detection before dispatch; flag circular dependencies and escalate instead of attempting execution |
| Conflicting outputs from multiple skill agents | Register conflict-resolution precedence rules per task category; default to "first confirmed correct" with consensus threshold |
| Platform credential expiration mid-workflow | Implement credential refresh hooks per platform; cache tokens with expiry-aware lookup and proactive renewal before dispatch |
| Skill registry returns stale/unavailable agents | Add health-check probe before dispatch; cache registry responses with TTL and fallback to last-known-good on probe failure |
| Approach | Timeframe | Description |
|---|---|---|
| Workflow-as-a-Service consulting | 1-3 months | Offer custom multi-agent workflow design for enterprises; build orchestration blueprints that integrate their existing tools with AI agents |
| SaaS workflow orchestration platform | 6-12 months | Build a visual workflow builder around the orchestrator engine — drag-and-drop agent routing, state management, and performance dashboards for teams |
| Agent coordination audit | 1-2 months | Review existing agent deployments for routing inefficiencies, dependency bottlenecks, and failure-prone dispatch patterns; deliver optimization roadmap |
| Enterprise SLA monitoring add-on | 3-6 months | Wrap the orchestrator with SLA dashboards, incident alerts, and compliance audit trails; sell as premium tier to mid-market customers |
| Custom integration bundle | 2-4 weeks per integration | Build and maintain platform-specific connectors (Telegram, WhatsApp, Slack, Discord, email) as reusable orchestration plugins |