articuler

Dev Tools

AI-powered professional networking assistant. Generate personalized cold emails and outreach playbooks to accelerate career growth and business development.

Install

openclaw skills install 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

Generate cold emails — Write personalized outreach emails tailored to each contact's background
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.

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.

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

{
  "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.

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

{
    "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.

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

{
    "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

{
    "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.

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

{
    "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.

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

{
    "code": 0,
    "msg": "",
    "data": {
        "sessionId": "2c6ae07f-bde7-4f6a-8cc6-944a99d742fd",
        "status": "searching",
        "objective": "Want to be a partner",
        "subject": "",
        "coldEmail": ""
    }
}

Response — Completed

{
    "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

articuler

Install

Articuler Skill

What This Skill Does

Usage

Step 1 — Email Check (Required)

Parameters

Step 2 — Login (Required)

Parameters

Response

Step 3 — Generate Playbook (Async)

Parameters

Response

Step 4 — Poll Playbook Result

Parameters

Polling Logic

Response — In Progress

Response — Completed

Step 5 — Generate Cold Email (Async)

Parameters

Response

Step 6 — Poll Cold Email Result

Parameters

Polling Logic

Response — In Progress

Response — Completed

MCP Server

Intent Detection

Tips

Links

Related skills