Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Revenue and costs tracker. AWS spend via aws ce, credits tracker, project revenue stages. Shows burn rate, runway estimate, credits expiring.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Before executing, load available context:
Preferences: Read ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json
timezone — display all timestamps correctlyDaemon health: Read ${CLAUDE_PLUGIN_DATA_DIR}/daemon-health.json
action_needed is not null → surface it before the cost reportSecrets: AWS Cost Explorer requires credentials.
$AWS_PROFILE / $AWS_ACCESS_KEY_ID → doppler secrets get AWS_ACCESS_KEY_ID --plain → vault query cmd from prefs| Command | Usage | Output |
|---|---|---|
aws ce get-cost-and-usage --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --granularity MONTHLY --metrics "UnblendedCost" --group-by "Type=DIMENSION,Key=SERVICE" --output json | Cost by service | Cost JSON |
aws ce get-cost-and-usage --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --granularity MONTHLY --metrics "UnblendedCost" --output json | Total cost | Cost JSON |
aws ce get-cost-forecast --time-period Start=<YYYY-MM-DD>,End=<YYYY-MM-DD> --metric "UNBLENDED_COST" --granularity MONTHLY --output json | End-of-month forecast | Forecast JSON |
aws ce list-savings-plans-purchase-recommendation --output json | Savings plan recommendations | JSON |
aws ce get-cost-and-usage \
--time-period "Start=$(date +%Y-%m-01),End=$(date +%Y-%m-%d)" \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--group-by "Type=DIMENSION,Key=SERVICE" \
--output json 2>/dev/null
aws ce get-cost-and-usage \
--time-period "Start=$(date -v-3m +%Y-%m-01 2>/dev/null || date -d '3 months ago' +%Y-%m-01),End=$(date +%Y-%m-%d)" \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--output json 2>/dev/null
aws ce list-savings-plans-purchase-recommendation --output json 2>/dev/null || echo '{}'
aws ce get-credits --output json 2>/dev/null || echo "credits API unavailable"
aws ce get-cost-forecast \
--time-period "Start=$(date +%Y-%m-%d),End=$(date +%Y-%m-28)" \
--metric "UNBLENDED_COST" \
--granularity MONTHLY \
--output json 2>/dev/null
cat "${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")}]'
${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.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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]
$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.
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.
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]")
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
日本語の概要は準備中です。原文の説明を表示しています。
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
日本語の概要は準備中です。原文の説明を表示しています。
Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
日本語の概要は準備中です。原文の説明を表示しています。
Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.
日本語の概要は準備中です。原文の説明を表示しています。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。