Update CRM pipeline. Use when: changing deal stages, values, notes, tracking velocity, or managing deal progression.
npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill pipeline-update
Update deal and opportunity records in the CRM pipeline — move deals between stages, update values, add notes, and track pipeline velocity. Provides a clear before-and-after view of every change and calculates the downstream impact on pipeline metrics, giving both marketing and sales teams visibility into deal progression, forecast accuracy, and revenue pacing against targets. Designed for both individual deal updates and batch stage transitions, with built-in validation to prevent invalid stage skips and mandatory field gaps.
Use this command for pipeline changes that affect deal stage, value, or forecast. For creating new deals from leads, use /digital-marketing-pro:lead-import to bring leads into the CRM first, then use this command to manage their pipeline progression.
For bulk pipeline reporting without individual updates, use /digital-marketing-pro:executive-dashboard instead.
yes (or an equivalent explicit approval). ANY other input — ambiguous, implied, partial, or absent approval — cancels the run.python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action create-approval --data '{"risk_level":"<tier>","summary":"..."}' before executing, then python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action mark-executed --id {approval_id} after the platform confirms success.The user must provide (or will be prompted for):
~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.10. Calculate pipeline velocity impact: Compute how this update affects pipeline velocity metrics — stage conversion rate, average deal cycle time for this stage, weighted pipeline value, forecast accuracy vs. target, and comparison to historical averages for similar deal sizes and stages.
11. Process win/loss data (if closing): If the deal is being closed won or lost, record the win/loss reason, competitor data, and deciding factors. For closed-lost deals, determine if the contact should enter a win-back nurture sequence. For closed-won deals, trigger any post-sale workflows (customer onboarding, case study candidate flagging, referral request scheduling).
12. Generate pipeline health assessment: After the update, assess overall pipeline health — pipeline coverage ratio vs. quota, average deal age by stage, deals at risk of slipping (past expected close date or stalled beyond historical average), and stage-by-stage bottleneck identification.
13. Log the update: Record the complete update — timestamp, deal ID, before state, after state, user who initiated, velocity impact, follow-up tasks created, win/loss data, and notifications sent — to ~/.claude-marketing/brands/{slug}/logs/pipeline-update-log.json.
A structured pipeline update report containing:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take indranilbanerjee/pipeline-update from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.