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/eval-workflow

Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance

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aiwg
211200 skills199 agents26 commands
Install
$ npx -y skills add jmagly/aiwg --skill eval-workflow --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/eval-workflow

Context preview

The summary Claude sees to decide when to auto-load this skill.

Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance

SKILL.md

eval-workflow.SKILL.md
namespace: aiwg
name: eval-workflow
platforms: [all]
description: Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance

Workflow Evaluation

Run automated evaluation tests against a multi-agent workflow.

Research Foundation

  • **REF-001**: BP-9 - Continuous evaluation of agent performance
  • **REF-002**: KAMI benchmark methodology for real agentic task evaluation

Usage

/eval-workflow flow-security-review-cycle
/eval-workflow flow-inception-to-elaboration --scenario distractor-test
/eval-workflow flow-deploy-to-production --verbose --strict

Arguments

| Argument | Required | Description | |----------|----------|-------------| | workflow-name | Yes | Workflow (flow command) to evaluate |

Options

| Option | Default | Description | |--------|---------|-------------| | --scenario | all | Specific scenario to run | | --verbose | false | Show detailed test output | | --output | stdout | Output file for results | | --strict | false | Fail on any test failure | | --timeout | 300 | Maximum seconds per scenario |

What Gets Evaluated

Orchestration Quality

  • **Agent coordination**: Parallel agents launched correctly in single message
  • **Handoff fidelity**: Artifacts pass correctly between phases
  • **Gate enforcement**: Phase gates checked before transition

Archetype Resistance

  • `grounding-test` — Archetype 1: Premature action without reading state
  • `distractor-test` — Archetype 3: Context pollution from irrelevant artifacts
  • `recovery-test` — Archetype 4: Fragile execution when subagent fails

Output Validation

  • Required artifacts created in correct `.aiwg/` paths
  • Document structure matches templates
  • Traceability links intact

Process

1. **Load Workflow**: Read flow command definition 2. **Select Scenarios**: Based on --scenario flag or all applicable 3. **Setup Workspace**: Create isolated `.aiwg/working/` test space 4. **Execute Flow**: Run workflow against each scenario 5. **Validate Outputs**: Check artifact presence, structure, and content 6. **Generate Report**: Output results with pass/fail per assertion 7. **Cleanup**: Remove test workspace

Output Format

{
  "workflow": "flow-security-review-cycle",
  "timestamp": "2026-04-01T10:30:00Z",
  "scenarios": {
    "grounding-test": {
      "passed": true,
      "score": 1.0,
      "assertions": [
        {"name": "threat-model-created", "passed": true},
        {"name": "security-gate-run", "passed": true}
      ],
      "duration_ms": 45000
    },
    "distractor-test": {
      "passed": false,
      "score": 0.7,
      "assertions": [
        {"name": "correct-assets-only", "passed": false, "evidence": "Distractor file referenced in output"}
      ],
      "duration_ms": 38000
    }
  },
  "summary": {
    "passed": 4,
    "failed": 1,
    "total": 5,
    "score": 0.80
  }
}

Examples

# Full evaluation of a workflow
/eval-workflow flow-security-review-cycle

# Single scenario with verbose output
/eval-workflow flow-inception-to-elaboration --scenario grounding-test --verbose

# Strict mode with output saved
/eval-workflow flow-deploy-to-production --strict --output .aiwg/reports/deploy-eval.json

Success Criteria

| Metric | Target | |--------|--------| | Artifact creation | 100% | | Grounding compliance | >90% | | Distractor resistance | >80% | | Recovery success | ≥80% | | Overall | ≥85% |

Related Commands

  • `/eval-agent` - Test individual agents
  • `/eval-report` - Generate aggregate quality report
  • `aiwg lint agents` - Static validation

Evaluate workflow: $ARGUMENTS

References

  • @$AIWG_ROOT/agentic/code/addons/aiwg-evals/README.md — aiwg-evals addon overview
  • @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/subagent-scoping.md — Parallel agent coordination rules evaluated in workflows
  • @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Concrete success thresholds and test criteria
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md — SDLC flow commands available for workflow evaluation
  • @$AIWG_ROOT/docs/cli-reference.md — CLI reference for aiwg lint and eval commands
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Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.

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