Why Extend Your Slack Agent Programmatically?
SlackClaw gives your team a powerful AI agent out of the box — connect your tools via one-click OAuth, ask it questions in Slack, and watch it pull context from GitHub issues, Linear tickets, Notion pages, and more. For many teams, that's enough. But if you've ever thought "I wish the agent would automatically do X when Y happens", or you want to teach it domain-specific skills your company has built internally, the OpenClaw API is where things get genuinely interesting.
OpenClaw, the open-source framework powering SlackClaw, exposes a clean HTTP API that lets you interact with your agent programmatically. You can register custom skills, push context into persistent memory, trigger autonomous workflows from external systems, and listen for agent-generated events — all without touching the Slack interface. This article walks you through the practical mechanics of doing exactly that.
Getting Your API Credentials
Because SlackClaw runs on a dedicated server per team, your API endpoint is unique to your workspace. You're not hitting a shared multi-tenant endpoint — your agent's state, memory, and tool connections live in an isolated environment, which matters for both security and performance.
To grab your credentials, navigate to Settings → Developer Access in your SlackClaw dashboard. You'll find:
- Your team's base API URL (e.g.,
https://your-team.slakclaw.io/api/v1) - An API key tied to your workspace
- A webhook signing secret for verifying inbound event payloads
Store these securely — in an environment variable or a secrets manager like AWS Secrets Manager or HashiCorp Vault. Never commit them to a repository.
Making Your First API Call
The simplest thing you can do is send a task to the agent programmatically. This is identical to typing a message in Slack, except it comes from your code and can be triggered by anything — a CI/CD pipeline, a cron job, a webhook from an external service.
curl -X POST https://your-team.slakclaw.io/api/v1/tasks \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "Summarize all Linear tickets assigned to @sarah that were updated in the last 24 hours and post the summary to #eng-standup",
"context": {
"triggered_by": "morning-digest-cron",
"channel": "eng-standup"
}
}'The agent will pick this up, use its existing Linear and Slack integrations (already connected via OAuth), execute the task autonomously, and return a task ID you can use to poll for status. That last part is important — because OpenClaw is an autonomous agent framework, tasks aren't instantaneous. The agent reasons through steps, calls tools, and may iterate before completing.
Polling for Task Status
curl https://your-team.slakclaw.io/api/v1/tasks/task_abc123 \
-H "Authorization: Bearer YOUR_API_KEY"The response includes a status field (pending, running, completed, failed), a steps array showing every action the agent took, and the final output. The steps array is particularly useful for debugging — you can see exactly which tools were called, what inputs were passed, and what was returned. Learn more about our security features.
Registering Custom Skills
This is where the API starts to earn its keep. A custom skill in OpenClaw is a function you define that the agent can call as a tool — just like it calls GitHub or Jira, except the logic lives in your codebase. You register it once, and from that point forward the agent knows it exists and will use it when relevant. Learn more about our pricing page.
Defining a Skill
A skill has three parts: a name, a description the agent uses to decide when to invoke it, and a schema describing the expected inputs.
curl -X POST https://your-team.slakclaw.io/api/v1/skills \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "get_deployment_status",
"description": "Retrieves the current deployment status for a given service from the internal deploy tracker. Use this when someone asks about whether a service is deployed, what version is running, or if a deployment is in progress.",
"input_schema": {
"type": "object",
"properties": {
"service_name": {
"type": "string",
"description": "The name of the service to check, e.g. payments-api"
},
"environment": {
"type": "string",
"enum": ["production", "staging", "dev"],
"description": "The deployment environment to query"
}
},
"required": ["service_name", "environment"]
},
"endpoint": "https://your-internal-api.example.com/webhooks/openclaw/deployment-status"
}'When the agent decides to use this skill, it will POST a signed request to your endpoint with the extracted parameters. Your endpoint does whatever it needs to — queries a database, calls an internal API, reads from a config file — and returns a JSON response. The agent incorporates that response into its reasoning and continues.
Verifying Incoming Skill Requests
Your endpoint will receive a request with an X-OpenClaw-Signature header. Always verify it using your webhook signing secret before processing the payload:
import hmac
import hashlib
def verify_signature(payload_body: bytes, signature_header: str, secret: str) -> bool:
expected = hmac.new(
secret.encode(),
payload_body,
hashlib.sha256
).hexdigest()
return hmac.compare_digest(f"sha256={expected}", signature_header)This prevents anyone other than your SlackClaw agent from triggering your internal endpoints.
Working with Persistent Memory
One of SlackClaw's most practically useful features is persistent memory — the agent remembers context across conversations, across days, and across different Slack channels. The API gives you direct read and write access to this memory store, which opens up some powerful patterns.
Writing to Memory
Suppose your onboarding script runs when a new engineer joins. You can push structured context into the agent's memory so it's immediately available:
curl -X POST https://your-team.slakclaw.io/api/v1/memory \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"key": "team_member:alice@example.com",
"value": {
"name": "Alice Chen",
"role": "Senior Backend Engineer",
"team": "Payments",
"github_handle": "alicechen-dev",
"linear_team": "PAY",
"joined": "2024-11-01"
},
"tags": ["team_member", "engineering", "payments"]
}'From this point on, when someone in Slack asks "Who owns the payments service?" or "Can you assign this Linear ticket to the payments team lead?", the agent already has the context it needs without having to look anything up.
Reading from Memory
You can also query memory programmatically — useful for building dashboards, audit tools, or syncing agent context back into external systems: For related insights, see OpenClaw for Slack Teams: The Complete 2026 Guide.
curl "https://your-team.slakclaw.io/api/v1/memory?tags=team_member&limit=50" \
-H "Authorization: Bearer YOUR_API_KEY"Triggering Workflows from External Events
Some of the most valuable automations are event-driven. Here are a few patterns teams commonly implement:
- GitHub webhook → agent task: When a pull request is marked "ready for review", trigger the agent to find the right reviewer based on code ownership, assign them in GitHub, and post a summary in the relevant Slack channel.
- PagerDuty alert → agent investigation: On a new incident, automatically instruct the agent to query recent deployments, check error rates in your observability tool, and draft an initial incident summary in your
#incidentschannel. - Jira status change → Notion update: When an Epic moves to "Done" in Jira, have the agent update the corresponding Notion project page with a completion summary and notify stakeholders.
- Scheduled digest: A cron job fires every Monday morning, triggering the agent to compile a weekly summary from Linear, GitHub, and Gmail into a single Slack message for leadership.
Because SlackClaw connects to 800+ tools via one-click OAuth, you don't need to write integration code for most of these data sources — the agent already knows how to talk to them. Your external trigger just needs to tell the agent what to do; it figures out how.
A Note on Credits and Cost Management
SlackClaw uses credit-based pricing with no per-seat fees, which means programmatic API usage draws from the same credit pool as your Slack interactions. Autonomous tasks that involve many tool calls naturally consume more credits than simple lookups — a task that queries GitHub, Linear, and Notion before posting a formatted Slack message will cost more than a task that answers a single question.
Two practical tips here: First, use the dry_run parameter when testing new task prompts — it simulates the agent's execution plan and estimates credit usage without actually running. Second, tag your programmatic tasks with a triggered_by context field (as shown in the first example) so your usage dashboard can break down consumption by automation type, helping you identify which workflows are most credit-intensive.
Where to Go From Here
The patterns covered here — custom skills, memory management, event-driven task triggering — represent the practical core of what teams use the OpenClaw API for. But the surface area is larger: you can also subscribe to agent event streams, manage tool connection scopes programmatically, and build multi-step workflow chains where one agent task's output becomes another's input. For related insights, see Set Up OpenClaw in Slack in Under 5 Minutes.
The full API reference is available in your SlackClaw dashboard under Developer Docs, and the OpenClaw GitHub repository has examples ranging from simple cron-triggered digests to more complex event-driven pipeline integrations. Start with one workflow that has a clear, measurable value — a morning standup digest, an automated incident triage, a PR review assignment — and build from there. Once your team sees the agent handling that reliably, the appetite for more tends to grow quickly.