shalin-rahman/skills/multi-agent-orchestration
skills/multi-agent-orchestration/SKILL.md
# Skill: Multi-Agent Orchestration # Usage: Use when coordinating multiple specialized LLM agents to solve complex, multi-step problems. ## 🎭 Agent Personas & Specialization - Give each agent a narrow, deep specialization rather than making one "God Agent". - Distinguish between **Planners** (Strategy, breaking down tasks), **Executors** (Coding, Writing), and **Evaluators** (QA, Security, Auditing). - Ensure explicit hand-offs between agents. ## 🖇️ Orchestration Patterns - **Sequential Rou
$ install --global
skillsauthnpx skillsauth add shalin-rahman/Synapticity skills/multi-agent-orchestrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
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9
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Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
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CrowdStrikeAdvanced threat intelligence
50%
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OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: Apr 23, 2026, 12:50 AM330.1s1 file scanned
SKILL.md
Skill: Multi-Agent Orchestration
Usage: Use when coordinating multiple specialized LLM agents to solve complex, multi-step problems.
🎭 Agent Personas & Specialization
- Give each agent a narrow, deep specialization rather than making one "God Agent".
- Distinguish between Planners (Strategy, breaking down tasks), Executors (Coding, Writing), and Evaluators (QA, Security, Auditing).
- Ensure explicit hand-offs between agents.
🖇️ Orchestration Patterns
- Sequential Routing (Waterfall): Agent A finishes -> Passes output to Agent B -> Passes to Agent C. Good for well-defined pipelines (e.g., Spec -> Code -> Test).
- Hierarchical/Managerial: A Manager agent receives the abstract goal, delegates specific tasks to Worker agents, reviews their work, and synthesizes the final output.
- Consensus/Debate: Multiple agents (with different system prompts or models) tackle the same problem, and an Arbitrator agent determines the best solution or merges them.
🛣️ State & Context Management (The "Blackboard")
- Implement a shared "Context Blackboard" or State Object.
- Agents should not just pass raw text strings to each other. They should read from and write to a structured state engine (e.g., updating a specific field in a JSON object).
- Maintain an "Audit Trail" so later agents (and human operators) can see why an earlier agent made a decision.
🛑 Control Flow & Halting
- The Critic Loop: Always pair a generation agent with a critic agent. The generator produces, the critic evaluates, and gives feedback.
- Implement explicit halting conditions. The manager agent must have a tool specifically named
FinishTask(final_answer)to signal the workflow is complete and escape the ReAct loop. - Guard against "Agent Death Spirals" where two agents get stuck arguing or repeating the same error. Implement hard iteration limits.
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