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。
Production incidents dashboard. Reads ECS health, Sentry errors, CI failures. Offers to dispatch fix agents for active fires.
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:
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).
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。