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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Production incidents dashboard. Reads ECS health, Sentry errors, CI failures. Offers to dispatch fix agents for active fires.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Before executing, load available context:
Daemon health: Read ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/daemon-health.json
infra-monitor service status — if not running, pre-gathered infra data may be staleaction_needed is not null → surface it immediately as a potential fireSecrets: AWS credentials are required for ECS/CloudWatch queries.
$AWS_ACCESS_KEY_ID / $AWS_PROFILE env varsdoppler secrets get AWS_ACCESS_KEY_ID --plain (if doppler configured in prefs)password_manager_config.query_cmd from preferences$SENTRY_AUTH_TOKEN → Doppler SENTRY_AUTH_TOKEN → vaultPreferences: Read ${CLAUDE_PLUGIN_DATA_DIR}/preferences.json for secrets_manager config to know which vault to query.
| Command | Usage | Output |
|---|---|---|
aws ecs list-services --cluster <name> --query 'serviceArns' | ECS services | ARN list |
aws ecs describe-services --cluster <name> --services <arn> --query 'services[0].{status:status,running:runningCount,desired:desiredCount}' | Service health | JSON |
aws logs tail /ecs/<service> --since 1h --format short | ECS logs | Log lines (use with Monitor for live) |
| Command | Usage | Output |
|---|---|---|
gh run list --limit 20 --json status,conclusion,name,headBranch,createdAt | Recent CI runs | JSON array |
gh run view <id> --repo <repo> --log-failed | Failed CI logs | Log output |
| Command | Usage | Output |
|---|---|---|
sentry-cli issues list --project <slug> --status unresolved | Unresolved issues | Issue list |
curl -H "Authorization: Bearer $SENTRY_AUTH_TOKEN" "https://sentry.io/api/0/projects/<org>/<proj>/issues/?query=is:unresolved" | API fallback when MCP unavailable | JSON array |
If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when dispatching multiple fix agents simultaneously. This enables:
Team setup (only when flag is enabled, dispatch phase):
TeamCreate("fire-fixers")
Agent(team_name="fire-fixers", name="fix-[service]", ...)
If the flag is NOT set, use standard parallel subagents.
${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || echo '{"clusters":[],"error":"infra check failed"}'
${CLAUDE_PLUGIN_ROOT}/bin/ops-ci 2>/dev/null || echo '[]'
${CLAUDE_PLUGIN_ROOT}/bin/ops-external 2>/dev/null || echo '[]'
Analyze the pre-gathered data — including external projects. Then run parallel checks:
gh run list --limit 20 --json status,conclusion,name,headBranch,createdAt 2>/dev/nullauth_expired as HIGH (credential rotation needed), unreachable/degraded as MEDIUM, not_configured as LOW.Classify each issue by severity:
| Severity | Criteria |
|---|---|
| CRITICAL | Service down, DB unreachable, auth broken |
| HIGH | Elevated error rate, deploy stuck, CI main broken |
| MEDIUM | Non-critical service degraded, flaky tests |
| LOW | Warning-level, non-urgent |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
OPS ► FIRES DASHBOARD — [timestamp]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
CRITICAL
[service] — [issue] — [since]
HIGH
[service] — [issue] — [since]
MEDIUM
[service] — [issue] — [since]
ECS HEALTH
[cluster] [service] [desired/running] [status]
CI STATUS
[repo] [branch] [workflow] [status] [last run]
SENTRY (top errors, 24h)
[error] [count] [first seen] [project]
EXTERNAL PROJECTS
[alias] [source] [status] [details — e.g. auth_expired, unreachable]
──────────────────────────────────────────────────────
Use batched AskUserQuestion calls (max 4 options each). Only show relevant actions (e.g., skip dispatch options if no issues found):
AskUserQuestion call 1:
[Dispatch fix agent for [top critical issue]]
[Dispatch fix agent for [second issue]]
[View logs for [service]]
[More...]
AskUserQuestion call 2 (only if "More..."):
[Open Sentry dashboard]
[Open GitHub Actions]
[All clear — nothing to do]
If no fires: show "ALL SYSTEMS OPERATIONAL" with last-checked timestamps.
When user selects to fix an issue, use AskUserQuestion to confirm the scope before dispatching:
Dispatch fix agent for: [issue title]
Severity: [CRITICAL/HIGH/MEDIUM]
Repo: [repo]
Error: [brief description]
The agent will:
- Investigate root cause in [repo]
- Create feature branch with fix
- Open PR for review
[Dispatch agent] [Show me the logs first] [Skip — I'll fix manually]
On confirmation, spawn an Agent with:
Use the agents/infra-monitor.md agent definition for infra issues.
If $ARGUMENTS contains a project alias, filter to that project's services only.
Use Monitor to stream ECS task logs or GitHub Actions runs when investigating fires:
Monitor(command: "aws logs tail /ecs/<service> --follow --since 5m")
Use TaskCreate for each active fire. Update with TaskUpdate as fires are investigated/fixed/escalated.
When diagnosing fires, use WebFetch to check AWS status page (https://health.aws.amazon.com/health/status), Vercel status, or third-party API status pages.
Use WebSearch to find if the error pattern matches a known AWS/infrastructure issue (e.g., "ECS task stopped CannotPullContainerError" → known ECR throttling).
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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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