Install payload
Install payload is broadly covered in current results.
75% (3/4)
Browse the directory
/
Showing 4 resources for "packaging"
Saved
/ search · [ ] view density · esc clear input
Active
Source-backed filter active — add entries to compare trust side by side.
Trust snapshot
4 results in this view
Claimed
0%(0/4)
3 trust signals differ in this sample: Review status, Source provenance, Submitter
Signals differ on Review status, Source provenance, Submitter — add entries to compare before you install.
Rollout signal scan
2 rollout risk signals in current results
Biggest gaps: metadata review, package integrity. 0 entries have 2+ required gaps.
good
Adoption queue
Browse adoption queue · balanced
1/4 visible results are in hold tier and need mitigation before adoption.
ready 0caution 3hold 1
Packrift MCP Server
No required blockers for this preset.
caution
64/100
Collect package checksum or signed artifact information.
mcp/packrift-mcp-server · trust review · confidence 83%
Agent Skills ZIP Packaging QA Capability Pack Skill
1 blockers: Metadata review
caution
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
skills/agent-skills-zip-packaging-qa-capability-pack · trust review · confidence 67%
LeanCTX 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/leanctx-mcp-server · trust review · confidence 67%
BentoML
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/bentoml · trust review · confidence 50%
Decision confidence
Decision confidence scan · balanced
1/4 results are low-confidence and need review before adoption.
high 0medium 3low 1
Packrift MCP Server
Address Package integrity before broader rollout.
medium
68/100
Missing: Package integrity
mcp/packrift-mcp-server · trust review
Agent Skills ZIP Packaging QA Capability Pack Skill
Address Metadata review, Package integrity before broader rollout.
medium
54/100
Missing: Metadata reviewMissing: Package integrity
skills/agent-skills-zip-packaging-qa-capability-pack · trust review
LeanCTX MCP Server
Address Metadata review, Package integrity before broader rollout.
medium
54/100
Missing: Metadata reviewMissing: Package integrity
mcp/leanctx-mcp-server · trust review
BentoML
Hold adoption until Metadata review, Package integrity are resolved.
low
36/100
Missing: Metadata reviewMissing: Package integrityMissing: Install payload
tools/bentoml · trust review
Freshness distribution
Current results are broadly fresh
Median age 53 days; all 4 scanned entries are within 90 days.
Theme distribution
Results center on packaging
50% of this view shares the top theme. Leading themes: packaging, ai-agents, capability-pack.
- packaging50% (2)
- ai-agents25% (1)
- capability-pack25% (1)
- catalog25% (1)
- code-search25% (1)
- context-engineering25% (1)
- deployment25% (1)
- developer-tools25% (1)
18 distinct themes across 4 scanned
Packrift MCP Server exposes Packrift's packaging-supplies catalog through a remote MCP endpoint. It lets AI agents search packaging products, retrieve pricing and inventory context, and create cart URLs for ecommerce packaging workflows.
Expert Agent Skills ZIP packaging QA capability pack applying documented skills directory layout, SKILL.md frontmatter checks, archive validation, and install smoke tests from official skills and plugin packaging documentation.
LeanCTX MCP Serverby Yves Gude · submitted by oktofeesh1
Local-first context-engineering MCP server and CLI that gives Claude token-efficient file reads, shell-output compression, code search, graph queries, persistent session memory, context packaging, verification tools, and dashboard-style token accounting through a single Rust binary.
BentoMLby BentoML · submitted by oktofeesh1
Apache-2.0 Python framework for building, packaging, serving, containerizing, and deploying AI model inference APIs and multi-model serving systems.