Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added new core modules: `src/config.ts` for validated configuration and `src/lib/safe-fetch.ts` for safe network requests. - Introduced basic tests for provider gateway and safe fetch logic (`tests/gateway-provider.test.ts`, `tests/safe-fetch.test.ts`). - Updated documentation to clarify environment variable requirements and outbound data handling.
What This Skill Does
Agent Manager is an orchestration kernel that receives plans from external AI systems, then validates, schedules, and executes the tools within those plans. It enforces token budgets, tracks per-task costs, and stores replayable telemetry for every run.
It separates plan authoring from execution infrastructure, so any external planner can submit plans without implementing its own scheduling, budget enforcement, or streaming telemetry.
When to use it
- Running multi-step AI agent workflows with enforced cost budgets
- Streaming real-time task execution events to a monitoring dashboard
- Replaying a failed run to debug the exact tool call sequence
- Injecting deterministic result overrides into a live task mid-run
- Auditing per-task LLM costs across multiple tenants or workloads
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: execute a two-task plan where task A fetches data and task B summarizes it
- 1Call GET /v1/capabilities to confirm available providers
- 2Submit plan via POST /v1/plan to validate structure and get recommendations
- 3Start the run with POST /v1/run and receive a run ID
- 4Stream progress with GET /v1/run/:id/stream, reading task_start and tool_call_end events
- 5Retrieve final results and cost breakdown from GET /v1/run/:id
Completed run record with per-task cost attribution and a replayable event log