Trending-skills dingtalk-workspace-cli
Use DingTalk Workspace CLI (dws) to manage DingTalk contacts, calendar, todos, attendance, approvals, and more from the command line or AI agent workflows.
git clone https://github.com/Aradotso/trending-skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/Aradotso/trending-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/dingtalk-workspace-cli" ~/.claude/skills/aradotso-trending-skills-dingtalk-workspace-cli && rm -rf "$T"
skills/dingtalk-workspace-cli/SKILL.mdDingTalk Workspace CLI (dws)
Skill by ara.so — Daily 2026 Skills collection.
dws is an officially open-sourced cross-platform CLI tool from DingTalk that unifies DingTalk's full product suite into a single binary. It is designed for both human users and AI agent workflows. Every response is structured JSON, and built-in Agent Skills let LLMs use DingTalk out of the box.
Installation
One-liner (recommended)
macOS / Linux:
curl -fsSL https://raw.githubusercontent.com/DingTalk-Real-AI/dingtalk-workspace-cli/main/scripts/install.sh | sh
Windows (PowerShell):
irm https://raw.githubusercontent.com/DingTalk-Real-AI/dingtalk-workspace-cli/main/scripts/install.ps1 | iex
The installer:
- Auto-detects OS and architecture
- Downloads a pre-compiled binary to
~/.local/bin - Installs Agent Skills to
~/.agents/skills/dws
Add to PATH if needed:
export PATH="$HOME/.local/bin:$PATH" # Add to ~/.bashrc or ~/.zshrc to persist
Build from source
git clone https://github.com/DingTalk-Real-AI/dingtalk-workspace-cli.git cd dingtalk-workspace-cli make build ./dws version
Prerequisites & Setup
1. Create a DingTalk app
Go to DingTalk Open Platform, create an enterprise internal app, and note the Client ID (AppKey) and Client Secret (AppSecret).
2. Configure redirect URL
In the app's Security Settings, add
http://127.0.0.1 as a redirect URL.
3. Publish the app
Go to App Release → Version Management and publish the app so it is active.
4. Whitelist (beta)
During the co-creation phase, join the DingTalk DWS group and provide your Client ID and enterprise admin confirmation to be whitelisted.
5. Authenticate
# Via CLI flags dws auth login --client-id $DWS_CLIENT_ID --client-secret $DWS_CLIENT_SECRET # Or set env vars first, then login export DWS_CLIENT_ID=your-app-key export DWS_CLIENT_SECRET=your-app-secret dws auth login
Tokens are stored encrypted with PBKDF2 (600,000 iterations) + AES-256-GCM, keyed to your device MAC address.
Environment Variables
| Variable | Purpose |
|---|---|
| OAuth Client ID (DingTalk AppKey) |
| OAuth Client Secret (DingTalk AppSecret) |
| Override default config directory |
| Custom server registry endpoint |
| Comma-separated domains for bearer token (default: ) |
| Set to to allow HTTP on loopback (dev only) |
Key Commands
Authentication
dws auth login # Authenticate via OAuth device flow dws auth logout # Remove stored credentials dws auth status # Show current auth status
Contacts
# Search users dws contact user search --keyword "Alice" # List departments dws contact department list # Get user details dws contact user get --user-id <userId>
Calendar
# List events dws calendar event list # Create an event dws calendar event create \ --title "Q2 Planning" \ --start "2026-04-01T10:00:00+08:00" \ --end "2026-04-01T11:00:00+08:00" \ --attendees "<userId1>,<userId2>" # Check room availability dws calendar room list
Todo
# List tasks dws todo task list # Create a task dws todo task create \ --title "Prepare quarterly report" \ --executors "<userId>" # Complete a task dws todo task complete --task-id <taskId>
Chat
# List groups dws chat group list # Send a message via webhook dws chat webhook send \ --url $DINGTALK_WEBHOOK_URL \ --content "Deployment succeeded ✅" # Send robot message dws chat robot send \ --group-id <groupId> \ --content "Hello from dws"
Attendance
# Get attendance records dws attendance record list --user-id <userId> --date "2026-03-01" # List shift schedules dws attendance shift list
Approval
# List approval templates dws approval template list # Submit an approval instance dws approval instance create \ --process-code <processCode> \ --form-values '{"key":"value"}' # Query approval instances dws approval instance list --status RUNNING
DING Messages
# Send a DING message dws ding send --receiver-ids "<userId>" --content "Urgent: please review PR" # Recall a DING message dws ding recall --ding-id <dingId>
AI Table (aitable)
# List tables dws aitable table list --space-id <spaceId> # Query records dws aitable record list --table-id <tableId>
Developer Docs
# Search DingTalk open platform docs dws devdoc search --keyword "webhook"
Workbench
# List workbench apps dws workbench app list
Output Formats
All commands support
-f / --format:
# Human-readable table (default) dws contact user search --keyword "Alice" -f table # Structured JSON (for agents and piping) dws contact user search --keyword "Alice" -f json # Raw API response dws contact user search --keyword "Alice" -f raw
Save output to a file:
dws contact user search --keyword "Alice" -f json -o results.json
Dry Run
Preview the MCP tool call without executing it:
dws todo task list --dry-run dws calendar event create --title "Test" --dry-run
Shell Completion
# Bash dws completion bash > /etc/bash_completion.d/dws # Zsh dws completion zsh > "${fpath[1]}/_dws" # Fish dws completion fish > ~/.config/fish/completions/dws.fish
Exit Codes
| Code | Category | Meaning |
|---|---|---|
| 0 | Success | Command completed successfully |
| 1 | API | MCP tool call or upstream API failure |
| 2 | Auth | Authentication or authorization failure |
| 3 | Validation | Bad input flags or schema mismatch |
| 4 | Discovery | Service discovery or cache failure |
| 5 | Internal | Unexpected internal error |
When using
-f json, errors include structured fields: category, reason, hint, actions.
Common Patterns
Scripting: find a user then create a todo assigned to them
#!/bin/bash set -euo pipefail # Search for user and extract userId USER_ID=$(dws contact user search --keyword "Alice" -f json | \ jq -r '.data[0].userId') echo "Found user: $USER_ID" # Create a todo assigned to that user dws todo task create \ --title "Review design doc" \ --executors "$USER_ID" \ -f json
Scripting: send a daily standup reminder
#!/bin/bash dws ding send \ --receiver-ids "$TEAM_USER_IDS" \ --content "🕘 Daily standup in 5 minutes — please join!" \ -f json
CI/CD: post build status to a DingTalk group
#!/bin/bash STATUS=${1:-"unknown"} EMOJI=$([[ "$STATUS" == "success" ]] && echo "✅" || echo "❌") dws chat webhook send \ --url "$DINGTALK_WEBHOOK_URL" \ --content "$EMOJI Build #$BUILD_NUMBER $STATUS — $BUILD_URL"
Using dws in a Go project
package main import ( "os/exec" "encoding/json" "fmt" ) type SearchResult struct { Data []struct { UserID string `json:"userId"` Name string `json:"name"` } `json:"data"` } func searchDingTalkUser(keyword string) (*SearchResult, error) { cmd := exec.Command("dws", "contact", "user", "search", "--keyword", keyword, "-f", "json", ) out, err := cmd.Output() if err != nil { return nil, fmt.Errorf("dws error: %w", err) } var result SearchResult if err := json.Unmarshal(out, &result); err != nil { return nil, err } return &result, nil } func main() { result, err := searchDingTalkUser("Alice") if err != nil { panic(err) } for _, u := range result.Data { fmt.Printf("User: %s (%s)\n", u.Name, u.UserID) } }
AI Agent Integration
dws installs Agent Skills automatically. Most AI coding agents (Claude Code, Cursor, Windsurf, etc.) auto-discover skills in .agents/skills/.
Install skills for a specific project
# Install to current working directory (project-scoped) curl -fsSL https://raw.githubusercontent.com/DingTalk-Real-AI/dingtalk-workspace-cli/main/scripts/install-skills.sh | sh
Skills are placed at
./.agents/skills/dws/ — commit these to your repo so all collaborators and agents get DingTalk capabilities automatically.
Typical agent prompts that trigger dws
- "Find the user ID for Alice in DingTalk"
- "Create a todo assigned to Bob for reviewing the PR"
- "List my calendar events for tomorrow"
- "Send a DING message to the team leads"
- "Check attendance records for user X this month"
Architecture Overview
dws uses a discovery-driven pipeline — no product commands are hardcoded:
Market Registry → Discovery → IR (normalized catalog) → CLI (Cobra) → Transport (MCP JSON-RPC) ↓ ↓ mcp.dingtalk.com Disk cache (TTL + stale-fallback for offline use)
- Market — fetches MCP service registry from
mcp.dingtalk.com - Discovery — resolves service capabilities, cached to disk with TTL and stale fallback
- IR — normalizes services into a unified product/tool catalog
- CLI — mounts catalog onto a Cobra command tree, maps flags to MCP input params
- Transport — executes MCP JSON-RPC calls with retry, auth injection, and response size limits
Development
make build # Build binary make test # Run unit tests make lint # Format + lint make package # Build all release artifacts locally (goreleaser snapshot) make release # Build and release via goreleaser
Troubleshooting
dws: command not found
export PATH="$HOME/.local/bin:$PATH"
Auth errors (exit code 2)
- Verify
andDWS_CLIENT_ID
are correctDWS_CLIENT_SECRET - Confirm the app is published and the redirect URL
is configuredhttp://127.0.0.1 - Confirm your enterprise is whitelisted (required during beta)
Discovery failures (exit code 4)
caches service discovery; if the cache is stale, delete it:dwsrm -rf "${DWS_CONFIG_DIR:-$HOME/.config/dws}/cache"- Set
if using a custom registryDWS_SERVERS_URL
API errors (exit code 1)
# Get structured error details dws todo task list -f json # Response includes: category, reason, hint, actions
Inspect raw requests
dws contact user search --keyword "Alice" -f raw
Allow HTTP for local dev/testing
export DWS_ALLOW_HTTP_ENDPOINTS=1