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.
git clone --depth 1 https://github.com/twells89/sigma-migration-skillsWrote 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/plugins/twells89/sigma-migration-skills/gooddata-to-sigma)<a href="https://agentmods.dev/plugins/twells89/sigma-migration-skills/gooddata-to-sigma"><img src="https://agentmods.dev/badge/plugins/twells89/sigma-migration-skills/gooddata-to-sigma/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/plugins/twells89/sigma-migration-skills/gooddata-to-sigma"><img src="https://agentmods.dev/badge/plugins/twells89/sigma-migration-skills/gooddata-to-sigma.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
gooddata-to-sigma 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 today.
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.
What it actually says
{
"$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json",
"name": "gooddata-to-sigma",
"displayName": "GoodData \u2192 Sigma",
"version": "1.2.25",
"description": "Migrate GoodData Cloud / GoodData.CN workspaces to Sigma \u2014 the declarative workspace layout (LDM + analytics model) \u2192 Sigma data model + workbook. Exports the full workspace via the declarative REST API (logicalModel + analyticsModel), maps datasets/attributes/facts/references to a Sigma data model, translates MAQL metrics to Sigma formulas, maps insights (visualizationObjects) to workbook charts/KPIs/pivots and analytical dashboards to workbook pages + layout, ports user data filters (RLS) to Sigma user attributes, and verifies parity against the same warehouse. Honors the sibling converters' 'flag, never fake' contract: MAQL constructs with no clean Sigma equivalent, GoodData-compute-only metrics, and unsupported widgets are surfaced as loud flags rather than silently mis-converted. Includes a read-only estate assessment. Companion to the sigma-migration-skills converters.",
"author": {
"name": "Thomas Wells"
},
"license": "MIT",
"keywords": [
"gooddata",
"gooddata-cloud",
"gooddata-cn",
"maql",
"ldm",
"sigma",
"migration",
"bi",
"converter",
"assessment"
],
"skills": "./skills/"
}
What it installs
The manifest is a name and a version. 2 skills travel with it, and installing the plugin installs all of them — 393 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- today Changed 2744c6b89738
- 5d ago Changed 45adb0746f4e
- 9d ago First seen · 25 lines scan A 822dd39b21c1
gooddata-to-sigma is a plugin published in the GitHub repository twells89/sigma-migration-skills (14 stars, last pushed today), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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.
Other plugins, from other repositories
clip-aware-embeddings
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic…
tao-skill-bank
NVIDIA TAO skill bank with generated capability discovery for model training, data processing, application workflows, AutoML, and platform execution.
classifier
Text classification CLI using Bayesian, LSI, KNN, Logistic Regression, and TF-IDF algorithms.
databricks
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.
torch-air marketplace
Torch Accelerator Integration Readiness — evaluate how well a hardware accelerator integrates with PyTorch.
vmware-privateai
VMware Private AI (NVIDIA): GPU inventory, vGPU profiles, utilization, model serving — 17 tools.