agent-automation-smart-agent

Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent

1.07x

Quality

Does it follow best practices?

Impact

99%

1.07x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-automation-smart-agent/SKILL.md

Evaluation results

98%

14%

Agent Team Composition for a Secure Payment Microservice

Capability matching and team composition

Criteria

Without context

With context

Task requirements analysis

100%

Capability analysis step

50%

100%

Agent selection from capability analysis

75%

100%

Architecture/design agent present

100%

Implementation agent present

100%

Security review agent present

100%

Testing agent present

100%

Mesh or peer topology chosen

80%

Decision log present

100%

Reasoning documented per decision

100%

Conservative scope

100%

Task dependency awareness

100%

Without context: $0.2467 · 1m 47s · 11 turns · 15 in / 5,023 out tokens

With context: $0.4590 · 2m 14s · 21 turns · 27 in / 6,427 out tokens

100%

CI/CD Agent Pool Scaling Simulator

Workload-based scaling and resource optimization

Criteria

Without context

With context

Scale-up on high load

100%

Scale-down after surge

100%

Utilisation monitoring

100%

Work distribution across agents

100%

Agent lifecycle management

100%

Scaling event log

100%

Cost vs. performance trade-off

100%

Just-in-time spawning rationale

80%

100%

No external dependencies

100%

Predictive or burst awareness

50%

100%

Without context: $0.3956 · 2m 11s · 13 turns · 20 in / 8,454 out tokens

With context: $0.5712 · 2m 54s · 22 turns · 27 in / 9,102 out tokens

100%

Resilience Plan for a Multi-Agent Document Processing System

Failure recovery and adaptive coordination

Criteria

Without context

With context

Failure detection signals

100%

Automatic reinforcement

100%

Work reassignment

100%

Strategy adjustment described

100%

Graceful degradation mode

100%

Adaptive learning from failures

100%

Capability gap identification

100%

Manual override mechanism

100%

Override without full restart

100%

Iterative improvement loop

100%

Appropriate detail level

100%

Without context: $0.1661 · 1m 10s · 10 turns · 17 in / 3,172 out tokens

With context: $0.3220 · 1m 42s · 19 turns · 57 in / 4,753 out tokens

Repository: ruvnet/claude-flow
Commit: b2618f9

Evaluated: 17 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Agent Team Composition for a Secure Payment Microservice CI/CD Agent Pool Scaling Simulator Resilience Plan for a Multi-Agent Document Processing System

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