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。
Linear command center. Shows current sprint, creates/updates issues, manages priorities, syncs with GSD phases.
インストール方法を見る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:
Secrets: Linear API key required for MCP fallback queries.
$LINEAR_API_KEY env varmcp__doppler__*) — if Doppler MCP server is configureddoppler secrets get LINEAR_API_KEY --plain (if doppler CLI configured in prefs)password_manager_config.query_cmd from ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.jsonPreferences: Read ${CLAUDE_PLUGIN_DATA_DIR}/preferences.json for secrets_manager / doppler config.
| Command | Usage | Output |
|---|---|---|
curl -X POST https://api.linear.app/graphql -H "Authorization: $LINEAR_API_KEY" -H "Content-Type: application/json" -d '{"query":"{ issues(filter: {state: {type: {in: [\"started\",\"unstarted\"]}}}) { nodes { id title state { name } priority assignee { name } } } }"}' | Active issues | JSON |
curl -X POST https://api.linear.app/graphql -H "Authorization: $LINEAR_API_KEY" -H "Content-Type: application/json" -d '{"query":"{ cycles(filter: {isActive: {eq: true}}) { nodes { id number startsAt endsAt } } }"}' | Current cycles | JSON |
Run in parallel:
mcp__linear__list_teams — get all team IDsmcp__linear__list_issues — get issues with cycle filter (use GraphQL fallback for cycle queries if needed)Then fetch issues for the current cycle: mcp__linear__list_issues filtered to current cycle ID.
$ARGUMENTS| Argument | Action |
|---|---|
| (empty), sprint | Show current sprint board |
| backlog | Show unassigned/unscheduled issues |
| create [title] | Create a new issue (prompt for details) |
| update [id] | Update issue by ID |
| sync | Sync GSD phases to Linear issues |
| [issue-id] | Show and edit that specific issue |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LINEAR ► SPRINT [N] — [start] → [end]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
IN PROGRESS
[id] [priority] [title] [assignee] [estimate]
TODO
[id] [priority] [title] [assignee] [estimate]
DONE THIS SPRINT
[id] [title] [completed date]
BLOCKED / CANCELLED
[id] [title] [reason]
──────────────────────────────────────────────────────
Sprint velocity: [done points] / [total points] ([%])
──────────────────────────────────────────────────────
Use batched AskUserQuestion calls (max 4 options each):
AskUserQuestion call 1:
[Create new issue]
[Update issue status]
[Move issue to/from sprint]
[More...]
AskUserQuestion call 2 (only if "More..."):
[View backlog]
[Sync with GSD phases]
Collect from user (or parse from $ARGUMENTS):
Use mcp__linear__create_issue to create. Confirm: Created [id]: [title]
Read all active GSD STATE.md files across projects. For each active phase:
Update Linear issues to match GSD phase completion status.
Use AskUserQuestion after displaying any view to get the next action.
When the user starts working on a Linear issue, use TaskCreate to track it locally. Update with TaskUpdate as the issue progresses. This bridges Linear state with local session state.
When Linear MCP tools hit quota limits or fail, fall back to WebFetch with the Linear GraphQL API:
WebFetch(url: "https://api.linear.app/graphql", method: "POST", headers: {"Authorization": "$LINEAR_API_KEY"}, body: '{"query":"{ issues(filter: {cycle: {id: {eq: \"<id>\"}}}}) { nodes { id title state { name } } } }"}')
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。