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。
Token-efficient morning briefing. Pre-gathers all data via shell scripts, then presents a unified business dashboard with prioritized actions.
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
owner — use in the greeting header ("Good morning, [owner]")timezone — display all timestamps in this timezonedefault_channels — which channels to include in unread summaryDaemon health: Read ${CLAUDE_PLUGIN_DATA_DIR}/daemon-health.json
action_needed is not null → surface it before the briefingwacli-sync status before including WhatsApp unread counts~/.wacli/.health for live auth statusWhatsApp pre-check: Only include WhatsApp data if ~/.wacli/.health shows status=connected.
Health file — check ~/.wacli/.health BEFORE any wacli command:
status=connected → proceedstatus=needs_auth or status=needs_reauth → prompt user for QR scan| Command | Usage | Output |
|---|---|---|
wacli doctor --json | Check auth/connected/lock/FTS | {data: {authenticated, connected, lock_held, fts_enabled}} |
wacli chats list --json | All chats | {data: [{JID, Name, Kind, LastMessageTS}]} |
| Command | Usage | Output |
|---|---|---|
gog calendar events primary --today --json | Today's calendar events | Calendar events |
gog gmail search -j --results-only --no-input --max 30 "in:inbox" | Search inbox | JSON array of threads |
If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when gathering briefing data in parallel. This enables:
Team setup (only when flag is enabled):
TeamCreate("go-team")
Agent(team_name="go-team", name="infra-scanner", prompt="Check ECS health, Vercel status, and CI failures across all clusters")
Agent(team_name="go-team", name="inbox-scanner", prompt="Scan unread messages across WhatsApp, Email, Slack, Telegram, Notion")
Agent(team_name="go-team", name="pr-scanner", prompt="Find open PRs needing action — reviews, CI fixes, merge-ready")
Agent(team_name="go-team", name="sprint-scanner", prompt="Check Linear sprint progress and GSD phase state across projects")
If the flag is NOT set, use standard fire-and-forget subagents.
All data below was collected by shell scripts in <10 seconds:
${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || echo '{"clusters":[],"error":"infra check failed"}'
${CLAUDE_PLUGIN_ROOT}/bin/ops-git 2>/dev/null || echo '[]'
${CLAUDE_PLUGIN_ROOT}/bin/ops-prs 2>/dev/null || echo '[]'
${CLAUDE_PLUGIN_ROOT}/bin/ops-ci 2>/dev/null || echo '[]'
${CLAUDE_PLUGIN_ROOT}/bin/ops-unread 2>/dev/null || echo '{}'
for d in $(jq -r '.projects[] | select(.gsd == true) | .paths[]' "${CLAUDE_PLUGIN_ROOT}/scripts/registry.json" 2>/dev/null); do
expanded="${d/#\~/$HOME}"
if [ -f "$expanded/.planning/STATE.md" ]; then
alias=$(basename "$expanded")
phase=$(grep -m1 'current_phase' "$expanded/.planning/STATE.md" 2>/dev/null | head -1 || echo "unknown")
progress=$(grep -m1 'progress' "$expanded/.planning/STATE.md" 2>/dev/null | head -1 || echo "unknown")
echo "$alias: $phase | $progress"
fi
done
${CLAUDE_PLUGIN_ROOT}/bin/ops-external 2>/dev/null || echo '[]'
gog calendar events primary --today --json 2>/dev/null | head -20 || echo "calendar unavailable"
Analyze ALL the pre-gathered data above and present it as a morning briefing. Follow the ops-briefing output style.
Format:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
OPS ► MORNING BRIEFING — [DATE]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
FIRES (fix now)
[table of production issues, CI failures, broken deploys]
PRs NEEDING ACTION
[table: repo, PR#, title, status, action needed]
PORTFOLIO DASHBOARD
[table: project, phase, branch, uncommitted, CI, next action]
EXTERNAL PROJECTS
[table: alias, source, status, details — from ops-external data]
MARKETING
Health: [N]/100 ([Healthy/Warning/Critical]) | Blended ROAS: [X]x | Top channel: [channel]
Meta: $[X] spent (7d) [X]x ROAS | Google: $[X] spent (7d) [X]x ROAS | Email: [N] subs
[If health < 70: "⚠ Run /ops:marketing optimize for recommendations"]
[If no marketing configured: "(marketing not configured — /ops:marketing setup)"]
UNREAD
[WhatsApp: N, Email: N, Slack: check MCP, Notion: N items, Calendar: N events today]
TODAY'S PRIORITIES (ranked by revenue impact + urgency)
1. [action] — [project] — [why]
2. ...
3. ...
──────────────────────────────────────────────────────
Marketing section data source: Read from ops-marketing-dash pre-gathered output (see Pre-gathered data section). If marketing data is present in the dash output, compute the health score inline (see ops-marketing SKILL.md health score formula). If ops-marketing-dash is not configured or returns empty marketing data, show (marketing not configured — /ops:marketing setup).
Priority ranking: fires > degraded infra > CI failures > unread comms > ready-to-merge PRs > revenue-generating GSD work > stale projects.
If $ARGUMENTS contains a project alias, focus the briefing on that project only.
After the briefing, use batched AskUserQuestion calls (max 4 options each) for the "What's next?" prompt. Show the top 3 priority actions + [More...] in the first call, then remaining actions + [/ops-yolo — let me run your business today] in the second call. Route to the appropriate ops skill or project.
For Slack counts: if the pre-gathered data shows "count": -1, use mcp__claude_ai_Slack__slack_search_public_and_private with query in:channel (NOT is:unread — scan full recent activity) to get actual message counts. Do this as a parallel tool call while analyzing other data.
For Notion counts: if NOTION_MCP_ENABLED=true and pre-gathered data shows Notion as available, use mcp__claude_ai_Notion__notion-search with query: "" sorted by last_edited_time descending to surface recently active pages. Then call mcp__claude_ai_Notion__notion-get-comments on the top results to find comments needing response. Note: Notion search does not support date range filters — sort by recency and limit to the first 10-20 results instead.
After presenting the briefing, create a TaskCreate for each recommended priority action. As the user works through them (or delegates via skill routing), update with TaskUpdate. This gives continuity across the session.
After the first briefing, offer to schedule recurring briefings via AskUserQuestion:
[Schedule daily at 9am] [Schedule weekday mornings] [No schedule]
Use CronCreate to set up the schedule. Show existing schedules with CronList.
When gog calendar fails, use WebFetch with the Google Calendar API as fallback:
WebFetch(url: "https://www.googleapis.com/calendar/v3/calendars/primary/events?timeMin=<today>T00:00:00Z&timeMax=<today>T23:59:59Z")
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。