articuler

# Articuler Skill Craft the perfect cold email and build a structured outreach playbook — all driven by your specific professional goal and LinkedIn profile. ## What This Skill Does 1. **Generate cold emails** — Write personalized outreach emails tailored to each contact's background 2. **Build a playbook** — Create a structured outreach strategy with timing, talking points, and follow-up cadence ## Usage > **Note:** Steps 1 and 2 are required before calling any business API. Complete them in order to obtain a `token`. --- ### Step 1 — Email Check (Required) Send a verification code to the user's email address. ```bash curl --location --request POST 'https://api.articuler.ai/user/send-check' \ --header 'Content-Type: application/json' \ --data '{ "email": "your@email.com", "type": 1 }' ``` #### Parameters | Field | Type | Description | | ------- | ------- | --------------------------------------------- | | `email` | string | The user's email address | | `type` | integer | Verification type, use `1` for standard check | The verification code will be sent to the user's inbox. Ask the user to provide the `captcha` code before proceeding to Step 2. --- ### Step 2 — Login (Required) Log in with the user's email, captcha code, and LinkedIn profile URL. Returns a `token` required for all subsequent API calls. ```bash curl --location --request POST 'https://api.articuler.ai/user/artclaw/login' \ --header 'Content-Type: application/json' \ --data '{ "email": "your@email.com", "captcha": "xxxxxx", "linkedin_url": "https://www.linkedin.com/in/your-profile/" }' ``` #### Parameters | Field | Type | Description | | -------------- | ------ | ---------------------------------------------------------------- | | `email` | string | The user's email address | | `captcha` | string | The verification code received in the user's inbox (from Step 1) | | `linkedin_url` | string | The user's own LinkedIn profile URL | #### Response ```json { "token": "xxxxxx" } ``` Save the `token` — it is required for all subsequent API calls. --- ### Step 3 — Generate Playbook (Async) Submit a playbook generation job for the current user and a target contact. This is an async API — it returns a `sessionId` immediately. Use Step 4 to poll for the result. ```bash curl --location --request POST 'https://api.articuler.ai/user/artclaw/playbook' \ --header 'Content-Type: application/json' \ --data '{ "token": "xxx", "target_linkedin_url": "https://www.linkedin.com/in/your-target-profile/", "objective": "Want to be a partner" }' ``` #### Parameters | Field | Type | Description | | --------------------- | ------ | ------------------------------------------------------- | | `token` | string | Auth token obtained from Step 2 | | `target_linkedin_url` | string | The target contact's LinkedIn profile URL | | `objective` | string | The goal of this outreach (e.g. "Want to be a partner") | #### Response ```json { "code": 0, "msg": "", "data": { "sessionId": "31ee76d4-ab7e-4d5f-8196-f4507ef779b9", "status": "searching" } } ``` Save the `sessionId` — it is used to poll for the result in Step 4. --- ### Step 4 — Poll Playbook Result Poll this endpoint repeatedly until `status` is `"done"`. When done, read the playbook content from the response. ```bash curl --location --request GET 'https://api.articuler.ai/user/artclaw/playbook/info?sess_id=xxx' \ --header 'Content-Type: application/json' ``` #### Parameters | Field | Type | Description | | --------- | ------ | ------------------------------------ | | `sess_id` | string | The `sessionId` returned from Step 3 | #### Polling Logic - Poll every **3 seconds** - Stop polling when `data.status === "done"` - If `status` remains `"searching"` after **300 seconds**, inform the user that generation is taking longer than expected and ask whether to keep waiting #### Response — In Progress ```json { "code": 0, "msg": "", "data": { "sessionId": "31ee76d4-ab7e-4d5f-8196-f4507ef779b9", "status": "searching", "objective": "Want to be a partner", "playbook": { "subtopics": null }, "doOrNot": { "do": null, "doNot": null } } } ``` #### Response — Completed ```json { "code": 0, "msg": "", "data": { "sessionId": "31ee76d4-ab7e-4d5f-8196-f4507ef779b9", "status": "done", "objective": "Want to be a partner", "playbook": { "subtopics": [ { "subtopicName": "Absence of Personal Information for Mason Brown", "insights": [ { "insight": { "title": "No Verified Personal Background or Professional Activities Available for Mason Brown", "content": "Despite extensive search, no confirmed profiles...", "referenceLinks": [] } } ] }, { "subtopicName": "Monroe Street Partners' Strategic Differentiators", "insights": [ { "insight": { "title": "Non-Traditional Private Equity Model Focused on Long-Term Value", "content": "Monroe Street Partners (MSP), founded in ...", "referenceLinks": [ "https://www.monroestreet-partners.com/aboutmsp", "https://static1.squarespace.com/static/6218510..." ] } } ] } ] }, "doOrNot": { "do": [ { "title": "Discuss Monroe Street Partners' Unique PE Model and Financing Needs", "contentList": [ "Leverage intelligence about MSP being ..." ] }, { "title": "Explore Synergies Between Mason's DCM Experience and MSP's Lower Middle-Market Focus", "contentList": [ "Mason's background in Debt Capital Markets at PNC..." ] }, { "title": "Address Current Private Credit Market Trends Relevant to MSP's Strategy", "contentList": [ "The industry context shows private credit doubling..." ] } ], "doNot": [ { "title": "Avoid Pushing a Generic \"Off-the-Shelf\" Lending Solution", "contentList": [ "Monroe Street Partners explicitly differentiates ..." ] }, { "title": "Refrain from Unsubstantiated Assumptions about Mason's Booth Studies or Long-Term Career Plans", "contentList": [ "While Mason is attending Booth, his specific degree..." ] }, { "title": "Do Not Undervalue Mason's Role as a Summer Associate", "contentList": [ "Although a Private Equity Summer Associate role is ..." ] } ] } } } ``` Present the `playbook.subtopics`, `doOrNot.do`, and `doOrNot.doNot` to the user in a readable format. --- ### Step 5 — Generate Cold Email (Async) Submit a cold email generation job. Same async pattern as Steps 3–4: returns a `sessionId` immediately, use Step 6 to poll for the result. ```bash curl --location --request POST 'https://api.articuler.ai/user/artclaw/coldemail' \ --header 'Content-Type: application/json' \ --data '{ "token": "xxx", "target_linkedin_url": "https://www.linkedin.com/in/your-target-profile/", "objective": "Want to be a partner" }' ``` #### Parameters | Field | Type | Description | | --------------------- | ------ | --------------------------------------------------------------------- | | `token` | string | Auth token obtained from Step 2 | | `target_linkedin_url` | string | The target contact's LinkedIn profile URL | | `objective` | string | The goal of this cold email (e.g. "Explore investment opportunities") | #### Response ```json { "code": 0, "msg": "", "data": { "sessionId": "2c6ae07f-bde7-4f6a-8cc6-944a99d742fd", "status": "searching" } } ``` Save the `sessionId` — it is used to poll for the result in Step 6. --- ### Step 6 — Poll Cold Email Result Poll this endpoint repeatedly until `status` is `"done"`. When done, read the email content from the response. ```bash curl --location --request GET 'https://api.articuler.ai/user/artclaw/coldemail?sess_id=xxx' \ --header 'Content-Type: application/json' ``` #### Parameters | Field | Type | Description | | --------- | ------ | ------------------------------------ | | `sess_id` | string | The `sessionId` returned from Step 5 | #### Polling Logic - Poll every **3 seconds** - Stop polling when `data.status === "done"` - If `status` remains `"searching"` after **300 seconds**, inform the user that generation is taking longer than expected and ask whether to keep waiting #### Response — In Progress ```json { "code": 0, "msg": "", "data": { "sessionId": "2c6ae07f-bde7-4f6a-8cc6-944a99d742fd", "status": "searching", "objective": "Want to be a partner", "subject": "", "coldEmail": "" } } ``` #### Response — Completed ```json { "code": 0, "msg": "", "data": { "sessionId": "2c6ae07f-bde7-4f6a-8cc6-944a99d742fd", "status": "done", "objective": "Want to be a partner", "subject": "Exploring a potential partnership", "coldEmail": "Hi [Name],\n\nI came across your work on..." } } ``` Always show the `subject` and `coldEmail` to the user for review before sending. Never auto-send. --- ## MCP Server Articuler provides an MCP server as an alternative to the REST API above. **Endpoint:** `https://www.articuler.ai/mcp` | Tool | Description | | --------------------- | ------------------------------------------------------------------------------------ | | `generate_cold_email` | Generate a personalized cold email given two LinkedIn profiles and an objective | | `generate_playbook` | Generate a multi-step outreach playbook given two LinkedIn profiles and an objective | --- ## Intent Detection Detect the user's networking intent from their objective to set the right tone and structure: | Intent | Keywords | | ------------- | -------------------------- | | `fundraising` | 融资、投资人、VC、pre-seed | | `hiring` | 招聘、合伙人、CTO、团队 | | `partnership` | 合作、BD、渠道、资源置换 | | `research` | 了解行业、调研、趋势 | | `sales` | 客户、企业采购、demo | --- ## Tips - Complete Steps 1 and 2 before calling any business API - For cold emails, always show the draft to the user for review — never auto-send - Playbooks work best when both LinkedIn profiles are detailed and up to date - The more specific the `objective`, the more targeted the output ## Links - **Articuler:** https://www.articuler.ai - **ClawhHub Skills Hub:** https://clawhub.ai/skills