Install payload
Install payload is mixed and needs spot-checking.
50% (1/2)
Browse the directory
/
Showing 2 resources for "scheduler"
Saved
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Active
Rollout signal scan
2 rollout risk signals in current results
Biggest gaps: metadata review, package integrity. 0 entries have 2+ required gaps.
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Most at-risk entries in this view
Adoption queue
Browse adoption queue · balanced
1/2 visible results are in hold tier and need mitigation before adoption.
ready 0caution 1hold 1
Dagu MCP Server
1 blockers: Metadata review
caution
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
mcp/dagu-mcp-server · trust review · confidence 67%
Hugging Face Diffusers
2 blockers: Metadata review, Install payload
hold
36/100
Request metadata review from maintainers or internal owners.
Add install/config payload for reproducible team rollout.
Collect package checksum or signed artifact information.
tools/hugging-face-diffusers · trust review · confidence 50%
Decision confidence
Decision confidence scan · balanced
1/2 results are low-confidence and need review before adoption.
high 0medium 1low 1
Dagu MCP Server
Address Metadata review, Package integrity before broader rollout.
medium
54/100
Missing: Metadata reviewMissing: Package integrity
mcp/dagu-mcp-server · trust review
Hugging Face Diffusers
Hold adoption until Metadata review, Package integrity are resolved.
low
36/100
Missing: Metadata reviewMissing: Package integrityMissing: Install payload
tools/hugging-face-diffusers · trust review
Freshness distribution
Current results are broadly fresh
Median age 46 days; all 2 scanned entries are within 90 days.
Dagu MCP Serverby dagucloud · submitted by oktofeesh1
Built-in Streamable HTTP MCP server for Dagu that lets AI agents read workflow state, inspect DAG specs and logs, preview or apply workflow changes, and start, enqueue, retry, or stop DAG runs.
Hugging Face Diffusersby Hugging Face · submitted by oktofeesh1
Apache-2.0 library for pretrained diffusion model pipelines, schedulers, adapters, optimization, and training workflows for image, video, and audio generation in PyTorch.