Ralph Orchestrator is a framework that repeatedly runs AI-agent tasks until they finish or reach an iteration limit. Developers use it to coordinate autonomous coding work through command-line, web-dashboard, and MCP-server interfaces, with state managed per workspace. The catalogue entries provide agent skills, agents, instructions, and plugins for operating Ralph.
Borrowing it
Nothing to install: this file belongs to mikeyobrien/ralph-orchestrator. 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/mikeyobrien/ralph-orchestrator/main/.claude/skills/evaluate-presets/SKILL.mdgit clone --depth 1 https://github.com/mikeyobrien/ralph-orchestratorWrote 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/mikeyobrien/ralph-orchestrator/evaluate-presets)<a href="https://agentmods.dev/skills/mikeyobrien/ralph-orchestrator/evaluate-presets"><img src="https://agentmods.dev/badge/skills/mikeyobrien/ralph-orchestrator/evaluate-presets/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/mikeyobrien/ralph-orchestrator/evaluate-presets"><img src="https://agentmods.dev/badge/skills/mikeyobrien/ralph-orchestrator/evaluate-presets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 211 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00028 | $0.01839 |
| Opus 5 | $0.00014 | $0.00920 |
| Sonnet 5 | $0.00006 | $0.00368 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
evaluate-presets 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.
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate Presets
Overview
Systematically test all hat collection presets using shell scripts. Direct CLI invocation—no meta-orchestration complexity.
When to Use
- Testing preset configurations after changes
- Auditing the preset library for quality
- Validating new presets work correctly
- After modifying hat routing logic
Quick Start
Evaluate a single preset:
./tools/evaluate-preset.sh tdd-red-green claude
Evaluate all presets:
./tools/evaluate-all-presets.sh claude
Arguments:
- First arg: preset name (without
.ymlextension) - Second arg: backend (
claudeorkiro, defaults toclaude)
Bash Tool Configuration
IMPORTANT: When invoking these scripts via the Bash tool, use these settings:
- Single preset evaluation: Use
timeout: 600000(10 minutes max) andrun_in_background: true - All presets evaluation: Use
timeout: 600000(10 minutes max) andrun_in_background: true
Since preset evaluations can run for hours (especially the full suite), always run in background mode and use the TaskOutput tool to check progress periodically.
Example invocation pattern:
Bash tool with:
command: "./tools/evaluate-preset.sh tdd-red-green claude"
timeout: 600000
run_in_background: true
After launching, use TaskOutput with block: false to check status without waiting for completion.
What the Scripts Do
evaluate-preset.sh
- Loads test task from
tools/preset-test-tasks.yml(ifyqavailable) - Creates merged config with evaluation settings
- Runs Ralph with
--record-sessionfor metrics capture - Captures output logs, exit codes, and timing
- Extracts metrics: iterations, hats activated, events published
Output structure:
.eval/
├── logs/<preset>/<timestamp>/
│ ├── output.log # Full stdout/stderr
│ ├── session.jsonl # Recorded session
│ ├── metrics.json # Extracted metrics
│ ├── environment.json # Runtime environment
│ └── merged-config.yml # Config used
└── logs/<preset>/latest -> <timestamp>
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.
- 9d ago First seen · 242 lines · 28 tokens per session scan A b64f18850a8b
evaluate-presets is a skill published in the GitHub repository mikeyobrien/ralph-orchestrator (3,132 stars, last pushed 7d ago), licensed MIT. It adds 28 tokens to every session and 1,839 once invoked, about $0.0001 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.
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