LoopX is a provider-neutral control plane that gives long-running AI-agent work persistent state, decisions, governance, recovery, and handoffs across different agent harnesses. It is for managing and reviewing durable work performed by Codex, Claude Code, Cursor, dsh, or custom agent systems.
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 huangruiteng/loopx --skill loopx-benchmarkgit clone --depth 1 https://github.com/huangruiteng/loopxWrote 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/huangruiteng/loopx/loopx-benchmark)<a href="https://agentmods.dev/skills/huangruiteng/loopx/loopx-benchmark"><img src="https://agentmods.dev/badge/skills/huangruiteng/loopx/loopx-benchmark/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/huangruiteng/loopx/loopx-benchmark"><img src="https://agentmods.dev/badge/skills/huangruiteng/loopx/loopx-benchmark.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 | $0.00069 | $0.02501 |
| Opus 5.5 | $0.00028 | $0.01000 |
| Sonnet 5.5 | $0.00014 | $0.00500 |
| Haiku 4.5 | $0.00007 | $0.00250 |
Grade A, and why
loopx-benchmark 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LoopX Benchmark Workflow
Use this skill for a LoopX-managed benchmark experiment. The builtin
benchmark-toolkit capability owns provider-neutral experiment state and
integrity boundaries. This packaged skill is its task-triggered Agent playbook.
The capability is catalog-ready without a per-Goal enable switch. Installing this skill does not grant runner, shell, network, credential, private-evidence, or Goal mutation authority. Respect the selected todo's required capabilities, any external provider binding, host permissions, and user gates.
Capability surface
loopx capability show benchmark-toolkit --format json— catalog entry with usage hints, role boundaries, and the post-run case-insight template.loopx benchmark --help— subcommands (experiment-board-show, experiment-board-upsert, source-revision-fence, integrity-qualification, classify-artifacts).
Share a study through the public-safe contract
When another benchmark developer needs portable study data, use the capability's typed study flow rather than sharing a runner-specific ledger or raw evidence:
- Validate
benchmark_study_manifest_v0withbenchmark study-validate. - Wrap one allowlisted manifest, experiment-board row, redacted insight, or runtime
observation with
benchmark upload-envelope. - Run
benchmark upload-localwithout--executefirst, then explicitly execute against a caller-selected local JSONL store. - Verify the record/digest/revision binding with
benchmark upload-readback. - Derive the campaign/arm/case/run packet with
benchmark study-dashboard; pass a compact four-arm contract only when the study preregistered that design.
For case_insight_projection, first upload the same run's active terminal
experiment-board row with insight.status=complete. The case, run, and outcome
must match; the run row remains the only arm, score, countability, integrity, and
treatment-fidelity authority. Reduce private post-run evidence to bounded prose
and public-safe handles or digests before building the envelope.
What ships with it
2 files 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.
- 1mo ago Changed · +16 lines 45459c4ad206
- 1mo ago Changed · +25 lines c177663c1431
- 1mo ago Changed · +23 lines bb98396c3254
- 1mo ago First seen · 149 lines · 69 tokens per session scan A a158afdbad3f
loopx-benchmark is a skill published in the GitHub repository huangruiteng/loopx (6,233 stars, last pushed 5d ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,501 once invoked, about $0.0003 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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