Packs

2 packs

Results for “mvp-planning”

9 skills
projectious-work
ml-pipeline
ML pipeline design — data versioning, experiment tracking, deployment patterns, drift monitoring. Use when building an ML pipeline from data to deployment, setting up MLOps tooling (DVC, MLflow, model registry), choosing deployment patterns (shadow, canary, A/B), or designing monitoring for drift and degradation.
0 · bundle
github
devops-rollout-plan
Generate comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application changes.
36.2k
alirezarezvani
vpe-advisor
Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.
20.4k · bundle
sakamoto-family-smile
mle-workflow
Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
0
jeffallan
ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
dangquangse
team-pm
Plans sprints, breaks user stories into tasks, and estimates story points from BA and TechLead artifacts, producing sprint plan, task breakdown, and story point files.
19 · bundle
huuanh20
team-pm
Reads BA and TechLead artifacts to produce a sprint plan, task breakdown, and story point estimates, creating one tracked todo per sprint task for live pipeline tracking.
1 · bundle
seb1n
ml-pipeline-creation
Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
159
brycewang-stanford
g1
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit
1k