Results for “pipeline-crm”

Full pipeline from signal detection to scored lead in CRM. Chains signal-scanner, lead-scorer, and crm-writer into one flow.

Tracks leads, manages pipelines, automates outreach, and handles follow-ups to help close deals.

Build sales playbooks, define pipeline stages, and optimize conversion rates with structured processes and lead scoring.

Design and optimize revenue operations, including lead lifecycle rules, scoring, routing, handoffs, and CRM process automation for marketing, sales, and customer success alignment.

Designs and improves revenue operations, including lead lifecycle rules, scoring, routing, handoffs, and CRM process automation for marketing, sales, and customer success alignment.

Revenue operations analyst specializing in pipeline health diagnostics, deal velocity analysis, forecast accuracy, and data-driven sales coaching. Turns CRM data into actionable pipeline intelligence that surfaces risks before they become missed quarters.

Weekly pipeline health analysis with velocity metrics, stage distribution, forecasting, and actionable recommendations

Monday morning full pipeline review with metrics, channel performance, and forecast. Reads all CRM tabs, calculates deltas vs previous week, and generates actionable recommendations.

Qualify, prioritize, and advance sales prospects into the next best action. Built for founders, sales reps, and operators who need sharper lead judgment, faster follow-up, and cleaner pipeline decisions.

Manages career workflows: resume review and scoring, job evaluation with apply recommendations, rubric development, content extraction from conversations, and pipeline quality audits.

Operate the Teams meeting summary pipeline via Hermes CLI — summarize meetings, inspect pipeline status, replay jobs, manage Microsoft Graph subscriptions.

Configurable pipeline orchestrator for sequencing stages

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.

Designs and implements CI/CD pipelines with automated testing, build automation, deployment, and rollback strategies.

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Weekly pipeline metrics, forecast, and trend dashboard in structured markdown

Designs and implements CI/CD pipelines, automating build, test, and deployment processes with security scanning and deployment strategies.

Expert-level guidance for GitLab CI/CD pipeline configuration, development, optimization, debugging, and best practices.

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments. Use when the user requests ci cd or provides relevant inputs for this workflow.

Designs and implements CI/CD pipelines, infrastructure automation, and workflow optimization to reduce manual tasks.

Detect stack and generate CI/CD pipeline configs. Usage: /pipeline <detect|generate> [options]

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.

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Every visual change enters the pipeline at one of four stages, and the stage

Design or repair a CI/CD pipeline with well-ordered parallel stages, lockfile-keyed caching, quality gates, and build-once artifact promotion.

Configure container-native CI/CD pipelines with Drone, including YAML pipeline definitions, plugins, secrets, and multi-machine builds.

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

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.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

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.