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Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent
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Based on SOC occupation classification
| name | agent-automation-smart-agent |
| description | Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent |
name: smart-agent color: "orange" type: automation description: Intelligent agent coordination and dynamic spawning specialist capabilities:
This agent implements intelligent, automated agent management by analyzing task requirements and dynamically spawning the most appropriate agents with optimal capabilities.
Task Requirements → Capability Analysis → Agent Selection
↓ ↓ ↓
Complexity Required Skills Best Match
Assessment Identification Algorithm
Task: "Build REST API with authentication"
Automated Response:
- Spawn: API Designer (architect)
- Spawn: Backend Developer (coder)
- Spawn: Security Specialist (reviewer)
- Spawn: Test Engineer (tester)
- Configure: Mesh topology for collaboration
Detected: High parallel test load
Automated Response:
- Scale: Testing agents from 2 to 6
- Distribute: Test suites across agents
- Monitor: Resource utilization
- Adjust: Scale down when complete
Required: Database optimization
Automated Response:
- Search: Agents with SQL expertise
- Match: Performance tuning capability
- Spawn: DB Optimization Specialist
- Assign: Specific optimization tasks
"I need to refactor the payment system for better performance" Automatically spawns: Architect, Refactoring Specialist, Performance Analyst, Test Engineer
"Process these 1000 data files" Automatically scales processing agents based on workload
"Debug this WebSocket connection issue" Finds and spawns agents with networking and real-time communication expertise
Input: Task description
Model: Multi-label classifier
Output: Required capabilities
Input: Agent profile + Task features
Model: Regression model
Output: Expected performance score
Input: Historical patterns
Model: Time series analysis
Output: Resource predictions
Execute a natural-language browser intent via page-agent (browser_act) when the target is easier to describe than to select — degrades gracefully when page-agent or an OpenAI-compatible LLM provider isn't configured
Run `@metaharness/darwin evolve <repo>` to mutate a harness's seven policy surfaces (planner/contextBuilder/reviewer/retryPolicy/toolPolicy/memoryPolicy/scorePolicy), sandbox-score each variant, and promote only measured wins. The model is frozen; the harness evolves. Closes the loop ADR-150 opens (score+genome describe; evolve changes). Degrades gracefully when @metaharness/darwin is absent (ADR-150 + ADR-153 architectural constraints).
Run a GEPA learning cycle via `metaharness learn` (upstream ADR-235, metaharness@0.3.0) — optimizes a harness genome against a SWE-bench-style slice manifest. $0 dry-run by default; `--run` is the explicit spend opt-in. Requires a metaharness repo checkout (`--repo` or $METAHARNESS_REPO) — without one it reports `checkout-required` with clone instructions. Degrades gracefully when metaharness is absent.
Static security scan of a harness's declared MCP surface via `harness mcp-scan <path>`. Reads `.mcp/servers.json` + `.harness/claims.json`. Pure-read, no dispatch. Exits 1 on findings at or above `--fail-on` severity.
5-dimension harness readiness scorecard from `metaharness score <path>`. Returns harnessFit / compileConfidence / taskCoverage / toolSafety / memoryUsefulness + estCostPerRunUsd + scaffoldReady. Pure-read; subprocess invocation; degrades gracefully when MetaHarness is absent (ADR-150 architectural constraint).
Enterprise-review-grade threat model from `harness threat-model <path>`. Categorizes MCP-surface threats; emits `worst: 'clean'|'low'|'medium'|'high'` + per-threat findings. Pure-read.