Claude custom projects in the automation workflow?

What are Claude Projects, and why can't automation tools call them directly?

Claude Projects, available inside Claude.ai, bundle three things into a single UI experience: a persistent system prompt applied to every conversation in the project, shared knowledge files you upload once and reference across sessions, and conversation scoping that keeps project chats separate from your general Claude history. ^[1] That's the full feature set — there is no additional model capability underneath.

Automation platforms like Make.com have no native "Call Project" module because Anthropic has not exposed Projects as a discrete API endpoint. The feature was designed as a user-experience wrapper for the Claude.ai interface, not as a programmable resource. This question came up in the r/ClaudeAI community, and the confusion is understandable — Projects feel like a persistent AI configuration, so it's natural to want to reference them in code.

How do you replicate Claude Projects inside Make.com or n8n?

The mental model shift that unlocks everything: instead of thinking "I need to call a Project," think "I need to recreate what a Project provides on every API call." The Claude Messages API lets you do exactly this across any HTTP-capable automation tool. ^[2]

Step 1 — Move your Project instructions to a system prompt

In the Claude.ai UI, you give a Project a set of instructions. In API calls you send those same instructions as the system parameter in your request body. A Make.com HTTP module calling the Anthropic API uses this structure:

{
  "model": "claude-opus-4-5",
  "max_tokens": 1024,
  "system": "You are a Google Ads specialist. Analyze campaign performance and suggest budget adjustments. Output recommendations as structured JSON. Never recommend increasing spend on campaigns with ROAS below 2.0.",
  "messages": [
    {
      "role": "user",
      "content": "{{Campaign performance data from previous step}}"
    }
  ]
}

Store this system prompt in a Make.com Data Store record, a Google Sheet cell, or a Notion property — not hardcoded in the HTTP module body. Retrieve it dynamically on each run and map it into the system field.

Step 2 — Inject document context via the messages array

Projects let you upload files that Claude references across conversations. In the API, you pass that same content inside the messages array — either as text in the user turn or using Claude's document support for PDFs and other file types. ^[2]

A practical approach for most marketing automation workflows:

1. Store reference documents in Google Drive, Notion, or a database.
2. Add a retrieval step in your workflow to fetch only the content relevant to the current task.
3. Inject that content into the system prompt or as an initial user message before your main task prompt.

Step 3 — Handle conversation memory with external storage

Projects in the Claude UI retain a thread of conversation history scoped to that project. The API is stateless — it remembers nothing between calls unless you supply previous messages explicitly. ^[3] For automation workflows, this is an advantage once you architect it correctly.

In production I cap conversation threads at 15–20 turns before summarizing and starting a compressed context. Beyond that, you're paying for tokens with diminishing returns on output quality — and older messages carry less influence on Claude's response regardless of context window size.

How do you configure the HTTP module in Make.com step by step?

Make.com's generic HTTP module handles Anthropic API calls cleanly even without a native Claude connector.

1. Method: POST
2. URL: https://api.anthropic.com/v1/messages
3. Headers:
   • x-api-key: your Anthropic API key (stored in Make.com secrets, never in the module body)
   • anthropic-version: 2023-06-01
   • content-type: application/json
4. Body type: Raw / JSON
5. Body: your JSON payload with model, max_tokens, system, and messages

To replicate a "Project," add a Data Store lookup module before the HTTP call that retrieves your stored system prompt and any relevant document snippets, then map those values into the HTTP body using Make.com's variable syntax.

Multi-turn conversation pattern

If your workflow requires back-and-forth with Claude rather than single-shot calls, follow this pattern — similar to what multi-step Claude workflows look like in practice:

1. Look up the existing conversation array for this session from your Data Store.
2. Append the new user message to that array.
3. Send the full array as the messages parameter in your API call.
4. Parse Claude's response and append the assistant turn to the array.
5. Save the updated array back to the Data Store.
6. Use a router to check whether the conversation is complete or needs another turn.

When should you use Claude Projects versus the API in automation?

Projects and the API serve different trigger patterns — the right choice depends on how a task is initiated, not which tool is "better."

What do real marketing automation workflows look like using this pattern?

These examples use the Project-replication pattern above and represent the kinds of workflows I build on top of Claude for advertising and marketing teams. You can see a fully built version of this approach in Buddy, the AI Google Ads agent.

Google Ads performance alerts

A scheduled Make.com scenario runs every morning. It pulls the previous day's campaign data from the Google Ads API ^[4], injects a system prompt stored in a Data Store (the same instructions I'd put in a Claude Project), and asks Claude to flag any campaigns with ROAS below 1.5x or CPA above target thresholds. Claude's analysis posts to a Slack channel automatically — no one needs to open a dashboard.

Ad copy generation at scale

When a new product is added to a Shopify store, a webhook triggers a Make.com workflow. The workflow fetches the product description, retrieves the brand's tone-of-voice guide from Google Drive, and asks Claude to generate responsive search ad headline and description variants. Output writes directly to a Google Sheet for human review before upload. The brand guide is the "Project knowledge" — it just lives in Drive instead of the Claude UI.

Monthly reporting narrative

An n8n workflow aggregates performance data across channels at month-end, structures it as JSON, and sends it to Claude with a system prompt that instructs it to write a plain-English executive summary using the client's specific reporting template injected as context. The output goes into a Google Doc. What previously took 90 minutes of human time now takes under 3 minutes (QA and send).

What to do next

1. Extract your Project instructions. Open your existing Claude Project, copy the system prompt verbatim, and paste it into a Make.com Data Store record, a Google Sheet cell, or a Notion property. This is your portable "Project config."
2. Set up and test the HTTP module. Point Make.com's HTTP module at https://api.anthropic.com/v1/messages with your API key in headers. Test with a hardcoded system prompt first to confirm the connection before adding dynamic retrieval.
3. Map your document context. List every file uploaded to your Claude Project. Decide where each lives in your automation stack (Drive, Airtable, Data Store) and build a retrieval step that fetches relevant content before each Claude API call.
4. Add conversation memory only if you need it. Most automation workflows are single-shot (one input → one Claude response → output to another tool). Only add conversation array management if your use case genuinely requires multi-turn dialogue in an automated context.
5. Version your system prompts from day one. Before going to production, set up a simple log tracking prompt versions. You'll use it the first time an update breaks something that was working — and it will happen.

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