Borrowing it
Nothing to install: this file belongs to ProjectDXAI/labrat. 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/ProjectDXAI/labrat/main/.agents/skills/labrat-operator/SKILL.mdgit clone --depth 1 https://github.com/ProjectDXAI/labratWrote 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/projectdxai/labrat/labrat-operator)<a href="https://agentmods.dev/skills/projectdxai/labrat/labrat-operator"><img src="https://agentmods.dev/badge/skills/projectdxai/labrat/labrat-operator/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/projectdxai/labrat/labrat-operator"><img src="https://agentmods.dev/badge/skills/projectdxai/labrat/labrat-operator.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 | $0.00042 | $0.00816 |
| Opus 5.5 | $0.00017 | $0.00326 |
| Sonnet 5.5 | $0.00008 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
labrat-operator 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
labrat Operator
Use this skill from a labrat lab root, identified by branches.yaml, evaluation.yaml, runtime.yaml, and scripts/operator_helper.py.
Codex can load this skill implicitly when a task matches the description, or explicitly when the user references $labrat-operator. Keep this skill focused on lab operation; repo release mechanics belong in the root AGENTS.md.
Cold Start
- Run
python scripts/operator_helper.py doctor. - Run
python scripts/operator_helper.py status. - Read
coordination/workspace_map.md. - Read
coordination/prioritized_tasks.md. - Run
python scripts/operator_helper.py next-prompt --runner codex --phase auto.
If you are operating from the repo root, use the equivalent labrat ... --lab-dir <path> commands.
If both repo-root and lab-local AGENTS.md files are loaded, use the lab-local AGENTS.md for runtime operation and the root AGENTS.md for repo maintenance.
Operation Contract
- The runtime is authoritative. Do not hand-score candidates or edit
state/*.json[l]directly. - Do one complete operator loop before returning unless a stop condition fires.
- Reap stale leases, summarize runtime state, synthesize recent evaluations, dispatch work, lease runnable jobs, execute
scripts/run_experiment.py, complete candidates throughscripts/runtime.py, and verify the resulting state. - Use
scripts/evaluator.pyandscripts/runtime.pyfor scoring and promotion. - Write durable conclusions to
coordination/prioritized_tasks.md,logs/checkpoints/,logs/audits/, orlogs/expansions/.
Codex Modes
- Use GPT-5.5 in Codex for design, audit, frame break, profile authoring, release work, and review when it is available in the user's Codex host.
- Use Plan mode before broad workflow, docs, scaffold, or profile changes.
- Use normal execution for routine
doctor,status,next-prompt, dispatch, lease, and complete loops. - Use Codex review after changes to runtime behavior, scaffolding, prompt contracts, or release metadata.
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 · 71 lines · 0 tokens per session scan A adcacaea3994
labrat-operator is a skill published in the GitHub repository ProjectDXAI/labrat (236 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 816 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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