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Subagent Workflow Patterns To Boost Output Quality
Deploy 6 battle-tested multi-agent orchestration patterns to eliminate agent laziness and boost output quality.
$9
Subagent Workflow Patterns To Boost Output Quality
Example session with this skill installed
Run a tournament between three different pricing models for my new AI dev tool, then have a skeptic agent find flaws in the winner.
- Read your context and instructions
- Compiled the subagent workflow patterns
🏆 Tournament Winner: Tiered usage-based model ($0.10/req).
⚖️ Adversarial Review: "Scalability risk detected for high-volume users."
✅ Revision: Added a $499 enterprise cap to address scaling concerns.
Finalizing the strategic brief...
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About this skill
Orchestrate Multi-Agent Workflows Like a Team Lead
Stop struggling with "agentic laziness" and goal drift in long conversations. This skill transforms your AI agent into a sophisticated orchestrator capable of managing teams of specialized subagents through six battle-tested architectural patterns.
The 6 Patterns — With Real Examples
🔀 Fan-Out & Synthesize
Parallelize massive tasks across multiple subagents. Instead of researching one competitor at a time, spawn 3 agents simultaneously — each analyzes a different competitor — then merge their findings into one competitive landscape report. Perfect for code reviews (20+ files in parallel), market research, and migrations.
🛡️ Adversarial Verification
Solve self-bias by spawning a "skeptic" agent to find flaws before delivery. Your agent writes code or content → a harsh reviewer tears it apart against a rubric → only passes when the critic is satisfied. No more "looks good to me" when it isn't. Perfect for security audits, fact-checking, and quality-critical deliverables.
🏆 Tournament
Generate multiple competing solutions and use a pairwise judge to pick the objective winner. Need to choose a pricing model? 3 agents each argue for a different approach → judge compares A vs B → winner faces C → champion emerges. Better than picking the first idea that comes to mind.
🧠 Generate-and-Filter
Produce dozens of ideas across parallel streams, then distill them into the top 1%. Brainstorming a store name? 3 agents generate 10 names each from different angles → a filter agent scores, ranks, and deduplicates → you get the top 5 winners. Quantity into quality.
🔀 Classify-and-Act
Intelligently route complex requests to the right specialist subagent. Incoming bug report? A classifier quickly determines if it's frontend, backend, or infrastructure — then delegates to the appropriate fixer. No more wasting time with the wrong specialist.
🔄 Loop-Until-Done
Ensure 100% completion for exhaustive tasks. Need to fix all SEO tags across your store? Spawn an agent to find and fix missing meta descriptions → scan again → fix more → repeat until zero findings remain. No more "I fixed most of them."
What's Included in This Skill
- SKILL.md — Full configuration file with all 6 patterns and domain mappings
- 6 Template Files — One ready-to-use prompt template per pattern (fan-out, adversarial, tournament, classify, generate-filter, loop)
- Domain Quick-Reference — Which patterns to reach for in software development, content creation, business strategy, learning & research, creative design, and ecommerce
- Sample Inputs & Outputs — Real worked examples showing exactly what each pattern produces
Why Use This Skill
Individual agents often lose focus or settle for "good enough." By using these orchestration patterns, you enforce a rigorous process that mimics high-performing human teams — parallel workers, quality reviewers, judges, and loopers. No external dependencies or complex infrastructure required.
Before vs After
- 😞 Before: Agent fixes 20/50 bugs and stops → ✅ After: Loop-Until-Done ensures all 50 are fixed and verified
- 😞 Before: Agent says "looks good" to its own code → ✅ After: Adversarial agent finds real flaws every time
- 😞 Before: You manually split work across chat tabs → ✅ After: Fan-out spawns parallel agents — one merged result
- 😞 Before: Agent picks the first solution it thought of → ✅ After: Tournament pits 3 approaches, best one wins
Supported Tools
Optimized for Claude Code, Cursor, Aider, Windsurf, Codex CLI, Gemini CLI, GitHub Copilot, and any agent supporting the SKILL.md standard or MCP-based subagent delegation.
- Token usage is higher than single-agent execution — use patterns selectively for high-value tasks. Not recommended for simple one-shot questions.
- Subagents have no memory of past conversations — all context must be passed explicitly in each prompt.
- Fan-out is typically limited to 3-5 parallel subagents depending on your platform's capabilities.
- Verifier agents are self-reporting — for critical outputs, spot-check the final result manually.
- Pattern effectiveness depends on your AI agent's built-in subagent support. Works best with Claude Code, Cursor, Codex CLI, and similar agents that support task delegation.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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Permissions
Terminal/Shell access is needed so the agent can spawn and coordinate subagents for parallel task execution, file operations, and verification workflows.