Automate BambooHR tasks via Rube MCP (Composio): employees, time-off, benefits, dependents, employee updates. Always search tools first for current schemas.
Install with Tessl CLI
npx tessl i github:Lingjie-chen/MT5 --skill bamboohr-automation60
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Incremental employee sync workflow
RUBE_SEARCH_TOOLS first
62%
0%
RUBE_MANAGE_CONNECTIONS setup
0%
0%
EMPLOYEE_GET_CHANGED used
100%
100%
ISO 8601 timestamp format
100%
100%
GET_EMPLOYEE per changed ID
100%
100%
fields parameter specified
100%
100%
last_sync_timestamp persisted
70%
100%
Defensive response parsing
62%
25%
Status field filtering
50%
37%
GET_ALL_EMPLOYEES efficiency note
50%
62%
Numeric employee IDs
0%
87%
Without context: $0.6189 · 3m 4s · 28 turns · 34 in / 10,306 out tokens
With context: $0.6695 · 3m 1s · 24 turns · 30 in / 10,348 out tokens
Time-off request and approval workflow
RUBE_SEARCH_TOOLS first
57%
0%
GET_META_TIME_OFF_TYPES prerequisite
83%
100%
Time-off type ID is numeric
75%
100%
GET_TIME_OFF_BALANCES check
0%
100%
YYYY-MM-DD date format
100%
100%
Separate approval step
100%
100%
Employee ID resolution
88%
100%
requestId used for approval
100%
100%
RUBE_MANAGE_CONNECTIONS setup
0%
100%
Permissions note
0%
100%
Defensive parsing
100%
100%
Without context: $0.8000 · 2m 55s · 36 turns · 2,689 in / 10,492 out tokens
With context: $0.6047 · 2m 26s · 24 turns · 29 in / 8,885 out tokens
Bulk employee profile update
RUBE_SEARCH_TOOLS first
57%
0%
RUBE_MANAGE_CONNECTIONS setup
0%
100%
GET_ALL_EMPLOYEES for name resolution
80%
100%
Numeric employee IDs
25%
100%
GET_EMPLOYEE before UPDATE
0%
100%
Exact field names
100%
100%
Only specified fields updated
100%
100%
Active employee filter
0%
0%
Rate limiting / throttling
25%
0%
Defensive response parsing
87%
100%
fields parameter in GET_EMPLOYEE
0%
100%
Update report produced
100%
100%
Without context: $1.7411 · 4m 45s · 58 turns · 64 in / 15,533 out tokens
With context: $1.0352 · 3m 57s · 37 turns · 455 in / 14,102 out tokens
Table of Contents
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