manzanas: Skill for Claude Code

.agents/skills/run-evals/SKILL.md

run-evals is a skill for Claude Code, Codex from BariBariGood/manzanas. It costs 43 tokens per session (721 once invoked), scanned A, original, Apache-2.0.

A benchmark runner for manzanasd scenarios, which are repeatable sequences used to test a simulator service. It repeats each scenario and reports success, failures, consistency, and timing.

In plain words
What is it for?
Use it to run YAML test scenarios, produce human- and machine-readable reports, save screenshots, measure action latency, and detect inconsistent results in continuous integration.
Why use it?
It shows whether daemon changes work end to end and whether the same test behaves differently across runs.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is BariBariGood/manzanas's own configuration. It tells Claude Code and Codex how to work on manzanas itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything manzanas configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is go build -o bin/manzanas-eval ./eval/cmd/manzanas-eval.

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/BariBariGood/manzanas/main/.agents/skills/run-evals/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/BariBariGood/manzanas

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for run-evals

README.md
[![agentmods](https://agentmods.dev/badge/skills/baribarigood/manzanas/run-evals/github.svg)](https://agentmods.dev/skills/baribarigood/manzanas/run-evals)
Your own site
<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.

agentmods 80×15 button for run-evals

Your own site · 80×15
<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>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 721 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.00721
Opus 5 $0.00022 $0.00360
Sonnet 5 $0.00009 $0.00144
Haiku 4.5 $0.00004 $0.00072

Measured 9d ago against content hash 98b07aab9f83, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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 9d 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.

.agents/skills/run-evals/SKILL.md · 75 lines

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: erase and dispatch launch_app/observe. Against a --mock daemon the acquire fails with 501 not_implemented: reset is not implemented in this build — that is expected, not a harness bug. For Linux/CI coverage of the harness itself use go 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 overloaded retries 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}

Read the full file on GitHub · 75 lines

Changes

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.

  1. 9d ago First seen · 75 lines · 43 tokens per session scan A 98b07aab9f83

Subscribe to this mod's changes

run-evals is a skill published in the GitHub repository BariBariGood/manzanas (20 stars, last pushed 8d ago), licensed Apache-2.0. It adds 43 tokens to every session and 721 once invoked, about $0.0002 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 2026-08-30.

Related

Other skills, from other repositories

ios-simulator-interaction

Interaction with the iOS simulator using iosef, a CLI optimized for agent usage. Use when building or testing changes on the iOS Simulator — viewing the screen, tapping buttons, reading accessibility trees, finding elements by selector, asserting UI state, scripting multi-step test flows, installing and launching…

riwsky/iosef · 100 tokens

testa

Autonomously E2E-test iOS apps in the Simulator — read the screen (accessibility tree OR on-device OCR), tap/type/swipe/drag-drop/pinch/rotate, manage apps, and assert results. Use when asked to test, QA, drive, or reproduce a flow in an iOS app/simulator (React Native, Expo, native SwiftUI, or any app).…

valewnrt/testa · 103 tokens

argent-test-ui-flow

Autonomously test an app UI (iOS or Android) by running interact-screenshot-verify loops using argent MCP tools. Use when testing UI flows, verifying login works, testing navigation, running end-to-end UI test scenarios, manual QA steps, visible UI changes, or visual behavior.

software-mansion/argent · 64 tokens

baguette

Drive iOS simulators programmatically via the baguette CLI — taps, swipes, multi-finger gestures, hardware buttons (Home / Lock / Volume / Action / Power), ASCII keyboard text, and frame capture, all without opening Xcode. Use when: (1) an agent needs to drive a booted iOS simulator from a script — tap a coordinate…

tddworks/baguette · 249 tokens

mobile-automation

Control Android and iOS devices, emulators and simulators — launch apps, tap, swipe, type, take screenshots, read the accessibility tree. Use when a task involves a mobile device or app, mobile UI testing, or reproducing a bug on a phone.

mobile-next/mobile-mcp · 58 tokens

verify-ui-change-in-cloud

Verifies user-facing Warp client changes by spawning a cloud agent with computer use to test Warp. Use only when the user explicitly requested computer-use verification or accepted an offer to run it, and ONLY in non-sandboxed environments and local environments. Triggers a cloud agent that runs the test-warp-ui skill.

warpdotdev/warp · 69 tokens