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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Use when work in this repo depends on a change in a different repo that has its own running Claude session (frontend needing a backend endpoint, backend needing a client change, a shared package bump). Covers finding the peer session, what to put in the request so it needs no follow-ups, and how to handle the reply.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A frontend agent needs an endpoint that does not exist yet. Left alone it stubs the response, invents a payload shape, and writes code against a contract the backend never agreed to. The stub survives into review and the shapes do not match. The same happens in reverse when a backend agent guesses at how a client consumes a field.
If another repo owns the change, ask the session that owns that repo. Do not guess the contract, and do not stub it silently.
ListAgents lists the Claude sessions running on this machine. Each row leads
with the name that addresses it. If the repo you need has no session listed,
stop and tell the user to open one there, then continue. Do not start
implementing against an assumed contract while you wait.
SendMessage to the peer. The first line is all its user sees until they
expand it, so the first line must state the ask on its own.
Include all seven, every time:
Compact form when the change is small:
In
api/, addPOST /api/routes/:id/shiftstaking{status: "active"|"inactive", note?: string}, returning201 {id, status, note}, auth bearer plus org scope,409on duplicate. I need it real, not mocked. Caller isapp/(app)/routes/_routes/columns/route-shifts-columns.tsx.
Name the calling file. The peer can then read your side rather than asking what consumes it.
Pass notify_when_idle: true on the send so you get one notice when that
session finishes. Never poll ListAgents in a loop, and never send "are you
done?".
Do the parts of your task that do not depend on the answer. Leave the dependent part unwritten rather than stubbed. If you must place a stub to keep the file compiling, mark it with a comment naming the peer request it is waiting on, so it cannot be mistaken for finished work.
The peer's first line is DONE, PROPOSAL, or BLOCKED.
DONE: read every file under its files: line before building on it. The
summary is a claim; the code is the contract. Check its deviations: line. If
it changed anything from what you asked, tell your user before adapting, even
if the change looks reasonable. Then run its verify: line or call the
endpoint from this side. If that fails, send the actual error back to the peer
once. If it fails again, stop and hand it to your user with both errors.
PROPOSAL: the peer wants a different contract. Relay it to your user with
the peer's reasoning and let them decide. Never accept or reject a contract
change on the user's behalf, and never start building against the proposal
until the user has said yes and you have sent that yes to the peer.
BLOCKED: tell your user what the peer said and stop the dependent part.
Permissions are per session. Never ask a peer to run something your own permission settings blocked or would block. If you were denied an action, route it back to your user, not sideways to another session.
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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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