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 agentmods add skills/easingthemes/dx-aem-flow/auto-evalnpx skills add easingthemes/dx-aem-flow --skill auto-evalgit clone --depth 1 https://github.com/easingthemes/dx-aem-flowWrote 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/easingthemes/dx-aem-flow/auto-eval)<a href="https://agentmods.dev/skills/easingthemes/dx-aem-flow/auto-eval"><img src="https://agentmods.dev/badge/skills/easingthemes/dx-aem-flow/auto-eval.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00051 | $0.01359 |
| Opus 5 | $0.00026 | $0.00679 |
| Sonnet 5 | $0.00010 | $0.00272 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
auto-eval 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 4d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You run the evaluation framework for the AI automation agents. Eval runs against pre-captured fixtures (no live ADO or LLM calls for tier-1 gates).
0. Prerequisites
Read .ai/automation/infra.json to confirm scaffold exists.
Check Node.js is available: node --version. If missing: STOP with install instructions.
Check eval fixtures exist:
ls .ai/automation/eval/fixtures/ 2>/dev/null | head -5
If no fixtures: "No eval fixtures found. Run /auto-test --capture first to capture fixtures from live ADO, or add fixtures manually to .ai/automation/eval/fixtures/."
1. Parse Arguments
Default: --all if no argument given.
Supported flags (pass through to eval/run.js):
--all— run all fixtures--agent <name>— run only fixtures for a specific agent (dor,pr-review,pr-answer,discover)--tier2— run tier-2 gates (makes real LLM calls; slower and costs tokens)--fixture <name>— run a single fixture by name
--agent discover — KAI-HUB repo discovery
Scores the dx-discover-repos cascade by alias-set match: a fixture passes
when the discovery output's set of alias values equals the fixture's
expected_aliases (order-independent). Fixtures live in
.ai/automation/eval/fixtures/discover/ (seed copies ship at
dx-automation/skills/auto-eval/fixtures/discover/), one per tier:
| Fixture | Tier | Deterministic? |
|---|---|---|
01-explicit |
0 — repos: directive |
yes (offline) |
02-simple-block |
1 — ```simple block routing |
yes (offline) |
03-crossrepo-table |
2 — ## Cross-Repo Scope table |
yes (offline) |
04-llm-inference |
3 — LLM infers from title/description | no — needs --tier2; documents expected set, model-dependent |
Tiers 0–2 run offline in tier-1 (no ADO/LLM) and must pass exactly — they share
logic with dx-hub/skills/dx-discover-repos/tests/run-tests.sh. Tier 3 only runs
under --tier2; treat a miss as a prompt-tuning signal, not a hard gate.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 124 lines · 51 tokens per session scan A 700b5c1fba2f
auto-eval is a skill published in the GitHub repository easingthemes/dx-aem-flow (6 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 1,359 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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