Experiential is an open-source model gateway and router, meaning a service that gives agents one API for hosted, user-provided, local, and custom language models. It is for teams that need to choose models, control access and spending, and route production requests according to quality, speed, or cost. The catalogue entries provide agent skills and instructions for operating or configuring the gateway.
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 experientiallabs/experiential --skill improve-judgegit clone --depth 1 https://github.com/experientiallabs/experientialWrote 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/experientiallabs/experiential/improve-judge)<a href="https://agentmods.dev/skills/experientiallabs/experiential/improve-judge"><img src="https://agentmods.dev/badge/skills/experientiallabs/experiential/improve-judge/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/experientiallabs/experiential/improve-judge"><img src="https://agentmods.dev/badge/skills/experientiallabs/experiential/improve-judge.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 | $0.00031 | $0.00829 |
| Opus 5 | $0.00015 | $0.00415 |
| Sonnet 5 | $0.00006 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
improve-judge 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve the Judge
Use the common-owned judging contracts and persisted local review state. Every change starts from a reviewed disagreement in persisted rollout evidence and ends with a regression that would catch the same failure. Never tune a judge from intuition or from unverified caller data.
1. Resolve the canonical evidence
- Start from one local project and its completed rollout, judgment, rubric, label, lineage, and calibration artifacts.
- Use
exp.common.judging.Judgeas the scoring boundary. A judge receives recursively verified artifact IDs throughjudge_persistedand returns a structuredJudgment. - Use
RubricReview,HumanScoreReview, andJudgeCalibrationServiceto inspect judgments, record human score corrections, refresh calibration reports, and explicitly approve calibration. CLI and Platform workflows must call these same services rather than create a second artifact path. - Keep raw review and run output under the local project root or
/tmp. Do not commit customer evidence or operator-local outputs.
For a composed router workflow, read the judgments persisted by exp.compose_router. The workflow
injects its approved review supplier, setup supplier, simulator factory, judge, model catalog, and
finite budgets. Do not create a second scoring or evaluation path around that composition seam.
2. Classify disagreements
Sample about 20 reviewed cells across the score range and compare every dimension judgment with the active human score. Classify each actionable miss:
- A false positive scores materially above the human label.
- A false negative scores materially below the human label.
- A protocol failure lacks a valid structured
Judgmentand is infrastructure evidence, not a low score. - A lineage or provenance mismatch is an invalid input and must fail before calibration.
Recheck controls after every change. A new miss on an established control is a regression even if the target disagreement improves.
3. Locate the owning layer
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 · 81 lines · 31 tokens per session scan A 449eb0001dd9
improve-judge is a skill published in the GitHub repository experientiallabs/experiential (2,836 stars, last pushed yesterday), licensed Apache-2.0. It adds 31 tokens to every session and 829 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.
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