maf-prs-job
maf-prs-job is an MLOps & model serving skill for AI agents, published by microsoft in the promptflow repository on GitHub. Skillspace rates maf-prs-job C (77/100) and it passed the safety scan; a judge model has also reviewed it.
Convert an existing Prompt Flow Parallel Run Step (PRS) pipeline submission into an Azure ML PRS pipeline that runs a Microsoft Agent Framework (MAF) workflow. Wraps the MAF workflow into a PRS init()/run() entry script, generates the parallel component YAML and conda environment, and rewrites the pipeline submission script. Replaces what `load_component(flow.dag.yaml)` did automatically for Prompt Flow — produces the hand-built equivalent so that downstream pipeline code (`flow_node = flow_component(...)`, `flow_node.outputs.flow_outputs`, `flow_node.outputs.debug_info`, `flow_node.mini_batch_size`, scheduler, batch endpoint) stays unchanged. WHEN: convert promptflow PRS to MAF PRS, migrate PRS pipeline to agent framework, wrap MAF workflow as parallel component, bulk run MAF workflow, run agent framework as parallel run step, batch run MAF workflow on AML, submit MAF workflow as pipeline component, replace flow.dag.yaml with MAF workflow in pipeline, load_component equivalent for MAF…
- Grade
- C
- Quality
- 77 / 100
- Safety
- Passed
- GitHub stars
- 11,186
Placed #6 of 6 in the judged category final for MLOps & model serving.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 82 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Converts between public Prompt Flow, Azure ML and MAF products
Judge review
Placed #6 of 6 in the judged category final for MLOps & model serving.
- Trigger
78 78 - Instructions
85 85 - Specificity
90 90 - Safety
75 75 - Disclosure
82 82 - Maintenance
85 85
Anticipates how Prompt-Flow users would phrase the request in their old mental model and preserves port names 1:1, but the jargon-dense scope needs prior context to parse.
Not checked yet
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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