Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jmagly/aiwg --skill eval-workflowgit clone --depth 1 https://github.com/jmagly/aiwgWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmagly/aiwg/eval-workflow)<a href="https://agentmods.dev/skills/jmagly/aiwg/eval-workflow"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/eval-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jmagly/aiwg/eval-workflow"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/eval-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01038 |
| Opus 5.5 | $0.00009 | $0.00415 |
| Sonnet 5.5 | $0.00004 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
eval-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 statedistractor-test— Archetype 3: Context pollution from irrelevant artifactsrecovery-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
- Load Workflow: Read flow command definition
- Select Scenarios: Based on --scenario flag or all applicable
- Setup Workspace: Create isolated
.aiwg/working/test space - Execute Flow: Run workflow against each scenario
- Validate Outputs: Check artifact presence, structure, and content
- Generate Report: Output results with pass/fail per assertion
- Cleanup: Remove test workspace
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 143 lines · 22 tokens per session scan A 728eba1ece77
eval-workflow is a skill published in the GitHub repository jmagly/aiwg (225 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 1,038 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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