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Eval Workflow

Community
jmagly
eval-workflow

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

Overview

Publisherjmagly
Repositoryaiwg
Skill nameeval-workflow
Stars
225
Forks
34
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jmagly on GitHub. Read the source before you install it.

Installation

Install the Eval Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-evals/skills/eval-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/aiwg-evals/skills/eval-workflow .claude/skills/eval-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eval Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eval Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eval Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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

Arguments

ArgumentRequiredDescription
workflow-nameYesWorkflow (flow command) to evaluate

Options

OptionDefaultDescription
--scenarioallSpecific scenario to run
--verbosefalseShow detailed test output
--outputstdoutOutput file for results
--strictfalseFail on any test failure
--timeout300Maximum 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

json
{
  "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

bash
# 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

MetricTarget
Artifact creation100%
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

Frequently asked questions

What does the Eval Workflow AI skill do?

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

Why use Eval Workflow on TypingMind?

Because you install it once and use it with any model. Eval Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eval Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-evals/skills/eval-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Eval Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eval Workflow?

As many as you like. As long as a model supports skills, you can use Eval Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eval Workflow AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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