mcpbeat Sign in

Pipeline Health Analyzer Skill for Claude

Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.

5k tokens
context cost
the whole folder, loaded on every use
6
files
instructions only
0
copies elsewhere
how many repositories repackaged it
235
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/OneWave-AI/claude-skills --skill pipeline-health-analyzer

The instruction itself

4 sections, as written by the author

Pipeline Health Analyzer

Identify pipeline risks, predict deal outcomes, and prescribe specific actions to accelerate stalled opportunities.

Contents

  • references/output-template.md - Full report skeleton to populate.
  • references/stage-analysis.md - Per-stage deep-dive patterns (Discovery, Demo, Proposal) and why deals stall.
  • references/forecasting.md - Forecast tables, probability calibration, AI re-scoring, scenario planning.
  • references/email-templates.md - Re-engagement email templates for stalled and dark deals.
  • references/examples.md - Trigger phrases, a worked example request, and best practices.

Workflow

  • Obtain the pipeline data. Request a CSV export with deal name, stage, value, rep, deal age, days in current stage, last activity date, close date, and probability if not provided.
  • Compute pipeline-by-stage metrics: deal count, total value, average deal size, average days in stage, and stage-to-stage conversion rates. Compare each against historical benchmarks.
  • Score each deal across six health dimensions: stage velocity, engagement level, qualification depth, stakeholder coverage, competitive position, and 30-day momentum.
  • Identify stalled and at-risk deals. Flag deals exceeding benchmark time in stage, with no recent activity, with slipped close dates, or with single-threaded contacts.
  • For each critical deal, determine the root cause and prescribe prioritized actions (immediate, this-week, backstop). Pull re-engagement copy from references/email-templates.md.
  • Build the forecast: categorize deals (Commit/Best Case/Pipeline/Upside), calculate weighted and risk-adjusted value, check probability calibration against actual close rates, and re-score outliers. Follow references/forecasting.md.
  • Model best-case, expected, and worst-case scenarios against quota, with a mitigation plan for the downside.
  • Produce strategic recommendations across immediate, short-term, and long-term horizons.
  • Assemble the report using references/output-template.md. Use plain status words (Healthy / At Risk / Critical) and trend words (Up / Flat / Down); never use emoji.

10. Close with the report card, next-review date, and week-over-week KPIs to track.

Operating Principles

  • Run weekly, not monthly; pipeline health degrades fast.
  • Base assessments on activity metrics, not rep intuition.
  • Disqualify dead deals early; a flowing pipeline is healthy.
  • Always end with concrete next steps, not just analysis.
  • Coach with the insights; do not weaponize them against reps.

See references/examples.md for trigger phrases, a worked example, and the full best-practices list.

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take onewave-ai/pipeline-health-analyzer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.