mcpbeat Sign in

Ops Revenue Skill for Claude

Revenue and costs tracker. AWS spend via aws ce, credits tracker, project revenue stages. Shows burn rate, runway estimate, credits expiring.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
3248
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/davepoon/buildwithclaude --skill ops-revenue

What it tells the agent to use

found in the instruction text
Write writes files
WebFetch fetches pages from the network

The instruction itself

17 sections, as written by the author

OPS ► REVENUE & COSTS

Runtime Context

Before executing, load available context:

  • Preferences: Read ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json
  • timezone — display all timestamps correctly
  • Daemon health: Read ${CLAUDE_PLUGIN_DATA_DIR}/daemon-health.json
  • If action_needed is not null → surface it before the cost report
  • Secrets: AWS Cost Explorer requires credentials.

Secret Resolution

  • AWS: check $AWS_PROFILE / $AWS_ACCESS_KEY_IDdoppler secrets get AWS_ACCESS_KEY_ID --plain → vault query cmd from prefs
  • If no credentials available, report "AWS costs unavailable — credentials not configured" and show only the revenue pipeline from registry

CLI/API Reference

aws CLI (Cost Explorer)

| Command | Usage | Output |

|---------|-------|--------|

| aws ce get-cost-and-usage --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --granularity MONTHLY --metrics "UnblendedCost" --group-by "Type=DIMENSION,Key=SERVICE" --output json | Cost by service | Cost JSON |

| aws ce get-cost-and-usage --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --granularity MONTHLY --metrics "UnblendedCost" --output json | Total cost | Cost JSON |

| aws ce get-cost-forecast --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --metric "UNBLENDED_COST" --granularity MONTHLY --output json | End-of-month forecast | Forecast JSON |

| aws ce list-savings-plans-purchase-recommendation --output json | Savings plan recommendations | JSON |


Phase 1 — Gather financial data in parallel

AWS costs (current month)

aws ce get-cost-and-usage \
  --time-period "Start=$(date +%Y-%m-01),End=$(date +%Y-%m-%d)" \
  --granularity MONTHLY \
  --metrics "UnblendedCost" \
  --group-by "Type=DIMENSION,Key=SERVICE" \
  --output json 2>/dev/null

AWS costs (last 3 months trend)

aws ce get-cost-and-usage \
  --time-period "Start=$(date -v-3m +%Y-%m-01 2>/dev/null || date -d '3 months ago' +%Y-%m-01),End=$(date +%Y-%m-%d)" \
  --granularity MONTHLY \
  --metrics "UnblendedCost" \
  --output json 2>/dev/null

AWS credits remaining

aws ce list-savings-plans-purchase-recommendation --output json 2>/dev/null || echo '{}'
aws ce get-credits --output json 2>/dev/null || echo "credits API unavailable"

AWS cost forecast (end of month)

aws ce get-cost-forecast \
  --time-period "Start=$(date +%Y-%m-%d),End=$(date +%Y-%m-28)" \
  --metric "UNBLENDED_COST" \
  --granularity MONTHLY \
  --output json 2>/dev/null

Project registry (revenue stage)

cat "${CLAUDE_PLUGIN_ROOT}/scripts/registry.json" 2>/dev/null | jq '[.projects[] | {alias, name, stage: (.revenue_stage // .revenue.stage // "pre-revenue"), mrr: (.mrr // 0), source: (.source // "git"), type: (.type // "repo")}]'

External project revenue (Shopify, custom SaaS)

${CLAUDE_PLUGIN_ROOT}/bin/ops-external 2>/dev/null || echo '[]'

For Shopify projects showing status: "healthy", pull GMV via Shopify Admin API:

# For each Shopify project in registry with valid credentials:
STORE_URL="[from project.shopify.store_url]"
TOKEN="[from env var named in project.shopify.credential_key]"
curl -s -H "X-Shopify-Access-Token: $TOKEN" \
  "https://$STORE_URL/admin/api/2024-10/orders.json?status=any&created_at_min=$(date -v-30d +%Y-%m-%dT00:00:00Z 2>/dev/null)&limit=250" 2>/dev/null

Include Shopify GMV in the revenue pipeline table with source=shopify.


Phase 2 — Render dashboard

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 OPS ► REVENUE & COSTS — [month]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

AWS SPEND
 This month to date:  $[X]
 Forecast (EOM):      $[X]
 Last month:          $[X]
 MoM change:          [+/-X%]

 Top services:
 [service]  $[X]  ([%] of total)
 [service]  $[X]
 ...

CREDITS
 AWS credits remaining:  $[X]
 Expires:                [date]
 Burn rate at current:   [N months remaining]

REVENUE PIPELINE
 PROJECT        SOURCE     STAGE           MRR/GMV    STATUS
 ──────────────────────────────────────────────────────────────
 [project]      git        [stage]         $[X]       [status]
 [project]      shopify    [stage]         $[X] GMV   [status]
 [project]      custom     [stage]         $[X]       [status]
 ...
 ──────────────────────────────────────────────────────────────
 TOTAL MRR                                 $[X]
 TOTAL SHOPIFY GMV (30d)                   $[X]

RUNWAY ESTIMATE
 Monthly burn (AWS):  $[X]
 Total MRR:           $[X]
 Net burn:            $[X/month]
 Credits cover:       [N months]
 Cash runway:         [depends on external data]

──────────────────────────────────────────────────────

Use batched AskUserQuestion calls (max 4 options each):

AskUserQuestion call 1:

  [Drill into AWS costs by service]
  [Show cost anomalies (spike detection)]
  [Export cost report]
  [More...]

AskUserQuestion call 2 (only if "More..."):

  [Update project revenue stage]
  [Set budget alert]

Route by $ARGUMENTS

| Argument | Action |

| -------- | ---------------------------- |

| costs | Show only AWS cost breakdown |

| credits | Show only credits and expiry |

| revenue | Show only revenue pipeline |

| runway | Calculate and show runway |

| (empty) | Show full dashboard |

Use AskUserQuestion after the dashboard for next action.


Native tool usage

WebFetch — billing API fallback

When aws ce commands fail or return incomplete data, use WebFetch to query the AWS Cost Explorer API directly. Also useful for fetching Stripe/billing provider data if configured.

Write — export reports

When user selects "Export cost report" (option c), use Write to save the report as a dated file:

Write(file_path: "/tmp/ops-revenue-[date].md", content: "[formatted report]")

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 davepoon/ops-revenue 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.