GOD is a control room for observing and directing societies of language-model agents running in simulated worlds. It lets researchers inspect replays, question individual agents, alter future events, reset simulations, and export experiments for reuse. The catalogue entries are skills and agents for operating and investigating these simulations.
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 skills add XiaoLuoLYG/GOD --skill skvm-jitgit clone --depth 1 https://github.com/XiaoLuoLYG/GODWrote 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/xiaoluolyg/god/skvm-jit)<a href="https://agentmods.dev/skills/xiaoluolyg/god/skvm-jit"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/skvm-jit/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/xiaoluolyg/god/skvm-jit"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/skvm-jit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Supply Chain · line 134 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00111 | $0.02778 |
| Opus 5 | $0.00056 | $0.01389 |
| Sonnet 5 | $0.00022 | $0.00556 |
| Haiku 4.5 | $0.00011 | $0.00278 |
Grade C, and why
skvm-jit scanned grade C with 2 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 8d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
- If `skvm` is not on PATH, report it to the user and stop — do not install anything. If `skvm jit-optimize` fails with "opencode not found", tell the user to re-run the skvm installer (`curl -fsSL https://skillvm.ai/ins Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- If `skvm` is not on PATH, report it to the user and stop — do not install anything. If `skvm jit-optimize` fails with "opencode not found", tell the user to re-run the skvm installer (`curl -fsSL https://skillvm.ai/ins Copies of this mod
1 near-identical copy found in the catalogue:
- skvm-jit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkVM JIT Optimize Trigger
When you finish a task that was driven by a skill, and the skill's own instructions appear to have caused problems, collect a short structured record of what happened and invoke skvm jit-optimize. The optimizer reads that evidence, proposes improvements to the skill's files, and stores them as a proposal you can inspect with skvm proposals show <id>.
This skill is not a human review workflow. Its job is to turn task evidence into a concrete optimization run.
This works for any skill the host harness can load — it does not need to have been produced by SkVM's compiler. jit-optimize only needs the skill folder on disk; it does not require SkVM-specific artifacts.
When to trigger
Run this skill only when both of the following hold:
- The task you just completed was driven by a skill that the harness loaded from disk (not an ad-hoc user instruction).
- At least one of the following is true:
- the task failed or ended partial, and a clearer skill would plausibly have avoided it
- you observed a concrete problem in the skill's instructions: ambiguity, a missing step, an incorrect claim, a confusing ordering, or an unnecessary detour the skill forced you into
Do not run this skill when:
- the task succeeded and the skill read cleanly — silent passes are fine, the optimizer does not need "nothing to report" submissions
- the failure was purely user-side (typo in the prompt, missing credentials, network failure) and no change to the skill would have helped
- the task did not use a skill at all, or used only a trivial one-shot instruction
- you are running inside
skvm benchor any other SkVM-orchestrated flow — bench owns its own optimization loop, do not double-submit
Step 1: Locate the skill directory
The skill directory contains a SKILL.md file. You need the absolute path to pass as --skill=<dir> in Step 3.
Each agent harness installs skills in well-known locations. Read adapter-skill-paths.md (sibling file in this skill's directory) and look up the section matching the harness you are currently running inside — it lists the search order for Claude Code, opencode, openclaw, hermes, jiuwenclaw, and bare-agent. Probe the listed paths in order and pick the first one that contains a SKILL.md for the skill name you are looking for.
What ships with it
1 file 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.
- 8d ago First seen · 143 lines · 111 tokens per session scan C 529840416ede
skvm-jit is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,107 stars, last pushed 16d ago), licensed Apache-2.0. It adds 111 tokens to every session and 2,778 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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