beam-ai-team/beam-retry-tasks
skills/beam/beam-tools/beam-retry-tasks/SKILL.md
Retry failed Beam.ai tasks. Load when user says "retry tasks", "rerun failed tasks", "retry beam tasks", "resubmit tasks", or needs to re-execute failed agent tasks.
data-ai
Updated Jul 8, 2026
$ install --global
skillsauthnpx skillsauth add beam-ai-team/beam-next-skills beam-retry-tasksInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- beam-retry-tasks
- type:
- skill
- version:
- 1.0
- description:
- Retry failed Beam.ai tasks. Load when user says "retry tasks", "rerun
- author:
- Jack Li
- category:
- integrations
- platform:
- Beam AI
- updated:
- 2025-12-15
- visibility:
- team
- - approval:
- beam_task_retry
Beam Retry Tasks
Retry failed or stopped Beam.ai tasks using the retry API.
When to Use
- Retry tasks that failed due to transient errors (API timeouts, 400/500 errors)
- Re-execute tasks after fixing underlying issues
- Batch retry multiple failed tasks from an agent
Safety Contract
Before retrying any Beam task, show the workspace, agent ID or task IDs, retry count/limit, status filter, dry-run result when available, and expected side effects. Require explicit user approval in the current turn. Read-only listing, analysis, and dry runs do not require approval.
Prerequisites
.env file at project root:
# Beam.ai - BID instance (default)
BEAM_API_KEY=<bid-api-key>
BEAM_WORKSPACE_ID=<bid-workspace-id>
# Beam.ai - Production instance
BEAM_API_KEY_PROD=<prod-api-key>
BEAM_WORKSPACE_ID_PROD=<prod-workspace-id>
Dependencies: pip install requests python-dotenv
Quick Start
# Retry a single task
python 03-skills/beam-retry-tasks/scripts/retry_tasks.py --task-id <task_id>
# Retry all FAILED tasks from an agent (last 1 day)
python 03-skills/beam-retry-tasks/scripts/retry_tasks.py --agent <agent_id>
# Retry from JSON file (output from debug_issue_tasks.py)
python 03-skills/beam-retry-tasks/scripts/retry_tasks.py --file /tmp/failed_tasks.json
# Dry run (preview without executing)
python 03-skills/beam-retry-tasks/scripts/retry_tasks.py --agent <agent_id> --dry-run
Workspaces
| Workspace | API Endpoint | Default |
|-----------|--------------|---------|
| bid | api.bid.beamstudio.ai | Yes |
| prod | api.beamstudio.ai | No |
Retryable Statuses
By default, retries these statuses:
FAILED- Task execution failedERROR- Processing errorSTOPPED- Condition failedTIMEOUT- Execution timeout
Use --status to customize:
python retry_tasks.py --agent abc123 --status FAILED --status STOPPED
CLI Reference
| Flag | Description | Default |
|------|-------------|---------|
| --task-id, -t | Single task ID to retry | - |
| --agent, -a | Agent ID - retry all issue tasks | - |
| --file, -f | JSON file with task IDs | - |
| --status, -s | Status to include (repeatable) | FAILED, ERROR, STOPPED, TIMEOUT |
| --days, -d | Look back period (1,3,7,14,30) | 1 |
| --limit, -l | Max tasks to retry | 100 |
| --workspace, -w | Workspace: bid or prod | bid |
| --dry-run | Preview without executing | false |
| --delay | Delay between retries (seconds) | 0.2 |
| --output, -o | Save results to JSON file | - |
Input Formats
Single Task
python retry_tasks.py --task-id abc123-def456-...
From Agent
python retry_tasks.py --agent 455269b0-4d8d-4071-a8a0-6b07450462aa --days 7
From JSON File
Supports multiple formats:
Array of task objects:
[
{"task_id": "abc123", "custom_id": "INS-001"},
{"task_id": "def456", "custom_id": "INS-002"}
]
Array of strings:
["abc123", "def456", "ghi789"]
Example Output
Workspace: bid
API Base: https://api.bid.beamstudio.ai
Fetching FAILED, ERROR, STOPPED, TIMEOUT tasks from agent 455269b0...
Look back: 1 day(s)
Found 55 tasks to retry
============================================================
Retrying 55 tasks...
Started: 2025-12-15T09:22:31
[ 1/55] OK INS-29980 (0351632e...)
[ 2/55] OK INS-30137 (289bbb78...)
[ 3/55] OK INS-30138 (e5b5aa28...)
...
[ 55/55] OK INS-30298 (678327b2...)
============================================================
=== SUMMARY ===
Total tasks: 55
Successfully retried: 55
Failed to retry: 0
Integration with Debug Skill
Use with beam-debug-issue-tasks for a complete workflow:
# 1. Debug and identify failed tasks
python 03-skills/beam-debug-issue-tasks/scripts/debug_issue_tasks.py <agent_id> \
--days 7 --output /tmp/failed_tasks.json
# 2. Review the output
cat /tmp/failed_tasks.json
# 3. Retry all failed tasks
python 03-skills/beam-retry-tasks/scripts/retry_tasks.py \
--file /tmp/failed_tasks.json --output /tmp/retry_results.json
API Details
Uses Beam.ai retry endpoint:
- Endpoint:
POST /agent-tasks/retry - Body:
{"taskId": "<task_id>"} - Response: HTTP 201 (success, empty body)
Rate limiting: Built-in 0.2s delay between requests (configurable with --delay)
Retry logic: Automatically retries on 502, 503, 504 errors
Error Handling
| Error | Solution |
|-------|----------|
| BEAM_API_KEY not found | Add to .env file |
| 401 Unauthorized | Verify API key is valid |
| 404 Not Found | Task ID doesn't exist |
| 503 Service Unavailable | Transient error, will auto-retry |
Related Skills
beam-debug-issue-tasks- Find failed tasks and diagnose root causebeam-create-agent-task- Create new tasksbeam-list-agents- List available agents
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