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 yonatangross/orchestkit --skill golden-datasetgit clone --depth 1 https://github.com/yonatangross/orchestkitWrote 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/yonatangross/orchestkit/golden-dataset)<a href="https://agentmods.dev/skills/yonatangross/orchestkit/golden-dataset"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/golden-dataset/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/yonatangross/orchestkit/golden-dataset"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/golden-dataset.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 | $0.00044 | $0.01872 |
| Opus 5.5 | $0.00018 | $0.00749 |
| Sonnet 5.5 | $0.00009 | $0.00374 |
| Haiku 4.5 | $0.00004 | $0.00187 |
Grade A, and why
golden-dataset 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 20d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Golden Dataset
Comprehensive patterns for building, managing, and validating golden datasets for AI/ML evaluation. Each category has individual rule files in rules/ loaded on-demand.
Quick Reference
| Category | Rules | Impact | When to Use |
|---|---|---|---|
| Curation | 2 | HIGH | Content collection, annotation pipelines |
| Management | 2 | HIGH | Versioning, backup/restore |
| Validation | 1 | CRITICAL | Regression testing |
| Add Workflow | 1 | HIGH | 9-phase curation, quality scoring, bias detection, silver-to-gold |
Total: 6 rules across 4 categories. House thresholds and scars: references/ork-delta.md.
Curation
Content collection, multi-agent annotation, and diversity analysis for golden datasets.
| Rule | File | Key Pattern |
|---|---|---|
| Collection | rules/curation-collection.md |
Content type classification, quality thresholds, duplicate prevention |
| Annotation | rules/curation-annotation.md |
Multi-agent pipeline, consensus aggregation, Langfuse tracing |
Difficulty ladder, coverage floors, and duplicate thresholds: references/ork-delta.md.
Management
Versioning, storage, and CI/CD automation for golden datasets.
| Rule | File | Key Pattern |
|---|---|---|
| Versioning | rules/management-versioning.md |
JSON backup format, embedding regeneration, disaster recovery |
| Storage | rules/management-storage.md |
Backup strategies, URL contract, data integrity checks |
CI automation for backups is upstream's job; see "Upstream coverage" below.
Validation
Quality scoring, drift detection, and regression testing for golden datasets.
| Rule | File | Key Pattern |
|---|---|---|
| Regression | rules/validation-regression.md |
Difficulty distribution, pre-commit hooks, full dataset validation |
Schema validation and duplicate detection are upstream's job (see "Upstream coverage"
below); the house thresholds they must enforce live in references/ork-delta.md.
What ships with it
16 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.
- metadata.json 481 B
- references/ork-delta.md 2.8 KB
- references/quality-metrics.md 7.2 KB
- references/storage-patterns.md 6.4 KB
- references/versioning.md 7.2 KB
- rules/_sections.md 1.2 KB
- rules/_template.md 339 B
- rules/curation-add-workflow.md 2.7 KB
- rules/curation-annotation.md 6.6 KB
- rules/curation-collection.md 3.5 KB
- rules/management-storage.md 4.8 KB
- rules/management-versioning.md 4.3 KB
- rules/validation-regression.md 5.8 KB
- scripts/backup-golden-dataset.md 1.7 KB
- scripts/backup-script.py 13 KB runs code
- test-cases.json 4.7 KB
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.
- 20d ago Changed · -1 lines bd8dfb18d823
- 28d ago Changed · -5 lines 87bf58997d76
- 29d ago Changed · -1 lines 1057b49d11e9
- 1mo ago Changed d462ebcb5b4f
- 1mo ago First seen · 217 lines · 44 tokens per session scan A 458d0ca29f4f
golden-dataset is a skill published in the GitHub repository yonatangross/orchestkit (298 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,872 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 2026-09-03.
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