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 ArieGoldkin/claude-forge --skill golden-datasetgit clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote 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/ariegoldkin/claude-forge/golden-dataset)<a href="https://agentmods.dev/skills/ariegoldkin/claude-forge/golden-dataset"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/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/ariegoldkin/claude-forge/golden-dataset"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/golden-dataset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.01262 |
| Opus 5 | $0.00013 | $0.00631 |
| Sonnet 5 | $0.00005 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
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 10d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Golden Dataset
Curate, manage, and validate high-quality test datasets for AI/ML systems
Overview
A golden dataset is a curated collection of high-quality examples used for regression testing, retrieval evaluation, model benchmarking, and reproducibility. This skill covers the full lifecycle: curating new entries with quality analysis, managing backup/restore operations, and validating data integrity.
Example Golden Dataset Metrics
| Metric | Value |
|---|---|
| Documents | 98 completed |
| Chunks | 415 embedded segments |
| Test queries | 203 with expected results |
| Pass rate | 91.6% retrieval quality |
Purpose: Test hybrid search (vector + BM25 + RRF), validate metadata boosting, detect retrieval regressions, benchmark embedding models.
Curation
Quality criteria, workflows, and multi-agent analysis patterns for evaluating and adding documents to the golden dataset.
Key areas:
- Content type classification -- article, tutorial, research paper, documentation, video transcript, code repository
- Difficulty stratification -- trivial, easy, medium, hard, adversarial (based on semantic complexity)
- Quality dimensions -- accuracy (0.25), coherence (0.20), depth (0.25), relevance (0.30)
- Multi-agent pipeline -- parallel evaluation with Quality Evaluator, Difficulty Classifier, Domain Tagger, Query Generator
- Duplicate prevention -- URL check + semantic similarity > 80% threshold
Detailed patterns: references/curation.md Multi-agent pipeline: references/multi-agent-pipeline.md
Management
Backup, restore, and lifecycle operations for protecting golden dataset integrity.
Key areas:
- Data integrity contracts -- real canonical URLs required, no placeholders
- JSON backup strategy -- version-controlled, human-readable, portable; embeddings excluded and regenerated on restore
- Restore process -- load JSON, validate structure, create analyses/chunks, regenerate embeddings, verify integrity
- CLI usage --
poetry run python scripts/backup_golden_dataset.py backup|verify|restore - CI/CD automation -- GitLab CI pipeline integration for scheduled backups
What ships with it
12 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.
- checklists/backup-restore-checklist.md 14 KB
- references/backup-restore.md 14 KB
- references/ci-cd-automation.md 2.2 KB
- references/curation.md 5.9 KB
- references/disaster-recovery.md 2.4 KB
- references/duplicate-detection.md 2.8 KB
- references/management.md 6.5 KB
- references/multi-agent-pipeline.md 13 KB
- references/validation-contracts.md 12 KB
- references/validation-rules.md 4.4 KB
- references/validation-workflows.md 7.4 KB
- references/validation.md 3.2 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.
- 10d ago First seen · 123 lines · 25 tokens per session scan A 0f23bdb365c6
golden-dataset is a skill published in the GitHub repository ArieGoldkin/claude-forge (6 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,262 once invoked, about $0.0001 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 2026-08-31.
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