Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Revenue and costs tracker. AWS spend via aws ce, credits tracker, project revenue stages. Shows burn rate, runway estimate, credits expiring.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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]")
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
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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.
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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.
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Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
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