Borrowing it
Nothing to install: this file belongs to BariBariGood/manzanas. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/BariBariGood/manzanas/main/.agents/skills/run-evals/SKILL.mdgit clone --depth 1 https://github.com/BariBariGood/manzanasWrote 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/baribarigood/manzanas/run-evals)<a href="https://agentmods.dev/skills/baribarigood/manzanas/run-evals"><img src="https://agentmods.dev/badge/skills/baribarigood/manzanas/run-evals/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/baribarigood/manzanas/run-evals"><img src="https://agentmods.dev/badge/skills/baribarigood/manzanas/run-evals.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 | $0.00043 | $0.00721 |
| Opus 5.5 | $0.00017 | $0.00288 |
| Sonnet 5.5 | $0.00009 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
run-evals 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the eval harness
eval/ is a pure protocol client: it runs YAML scenarios against any daemon
N times (fresh lease + reset per run) and reports pass/fail, determinism
rate, flaky steps, and per-step latency percentiles.
Build and run
go build -o bin/manzanas-eval ./eval/cmd/manzanas-eval
bin/manzanas-eval \
--daemon http://<mac-host>:7433 \
--runs 3 \
--out eval-out \
eval/scenarios/*.yaml
Outputs: eval-out/report.md (human), report.json (machine),
<scenario>-<name>-run<N>.png screenshot artifacts. Exit code is non-zero on
any failing run or cross-run tree-hash drift, so it drops straight into CI.
Requirements & caveats (verified)
- The shipped scenarios need a real Mac daemon: they use
reset: eraseand dispatchlaunch_app/observe. Against a--mockdaemon the acquire fails with501 not_implemented: reset is not implemented in this build— that is expected, not a harness bug. For Linux/CI coverage of the harness itself usego test ./eval/...(httptest fake daemon). - Each run acquires a fresh lease with the scenario's reset policy, so eval
runs erase their target on release — point evals at a host/label where that
is acceptable, and prefer a device-specific label (e.g.
iphone-air) so the reset can't select another agent's simulator. - The runner renews the lease during long runs and deletes any snapshots the run created at teardown.
- Boots pass the daemon's safety gates; on a loaded host expect
503 overloadedretries to stretch run times.
Writing a scenario
YAML (strict parsing — unknown fields rejected; step names unique):
name: my-check
lease:
labels: [iphone-air] # device-specific label on shared hosts
reset: erase # none | erase | snapshot:<name>
ttl_seconds: 600
default_timeout: 120s
steps:
- op: boot
timeout: 300s
- op: action
kind: launch_app
payload: {bundle_id: com.apple.Preferences, terminate_running: true}
- op: assert
assert:
element_exists: {label: General}
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 · 75 lines · 43 tokens per session scan A 98b07aab9f83
run-evals is a skill published in the GitHub repository BariBariGood/manzanas (21 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 721 once invoked, about $0.0002 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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