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The harbor jobs command group provides commands for managing evaluation jobs. Jobs orchestrate the execution of multiple trials across agents, tasks, and attempts.

Commands

harbor jobs start

Start a new evaluation job. This is the same as the harbor run command.
See harbor run for detailed documentation and all available options.

harbor jobs resume

Resume an existing job from its job directory.

Options

Path
required
Path to the job directory containing the config.json file.
list[string]
Remove trials with these error types before resuming. Can be used multiple times.Default: ["CancelledError"]

Examples

Resume a job that was interrupted:
Resume a job and filter out timeout errors:

How It Works

  1. Loads the job configuration from config.json in the job directory
  2. Optionally removes trial directories matching specified error types
  3. Re-runs the job, skipping trials that already have results
  4. Displays results tables upon completion

harbor jobs summarize

Summarize trial failures in a job using Claude Agent SDK.

Arguments

Path
required
Path to a job directory or a parent directory containing multiple job subdirectories.

Options

int
Maximum number of concurrent summarization queries. Default: 5
string
Model to use for summarization (e.g., haiku, sonnet, opus). Default: haiku
boolean
Analyze all trials or only failed trials. Default: --failed (only failed trials are analyzed)
boolean
Overwrite existing summary.md files. Default: false (skip trials with existing summaries)

Examples

Summarize failures in a single job:
Summarize all trials (including successes):
Summarize multiple jobs in a directory:
Force regenerate summaries:

Output

The command generates:
  • Individual summary.md files in each trial directory
  • A top-level summary.md in the job directory (for multi-job summarization)
  • Analysis of failures, common patterns, and potential fixes

Job Directory Structure

A typical job directory structure:

Job Configuration

The config.json file contains the complete job configuration in the JobConfig schema. This includes:
  • Agent configurations
  • Environment settings
  • Dataset/task specifications
  • Orchestrator settings
  • Timeout multipliers
  • Retry policies
You can inspect this file to understand exactly how a job was configured, and use it as a template for future jobs.

Job Results

The result.json file contains:
  • Per-agent, per-dataset statistics
  • Reward distribution
  • Exception statistics
  • Metrics aggregations
  • Trial counts (total, errors, successes)

Examples

Resume a Failed Job

If a job was interrupted (e.g., by Ctrl+C or system crash):

Analyze Failures

After running a job, analyze what went wrong:

Clean Up and Retry

Remove trials with specific errors and resume:

See Also