Harbor Adapter Creator

Creating Harbor Benchmark Adapters

Adapters convert external benchmarks (SimpleQA, GAIA, AiderPolyglot, CodePDE, spider2-dbt, etc.) into Harbor's standardized task directory format. Each adapter reads source benchmark data and generates many individual task directories, one per benchmark instance.

When to Use an Adapter vs. Creating Tasks Directly

Use an adapter when:

• You have an existing benchmark dataset with many instances
• Tasks share the same structure but differ in data (questions, code, etc.)
• You want to track evaluation parity with the original benchmark

Create tasks directly when:

• You're authoring original evaluation challenges
• Each task has unique structure and environment
• There are fewer than ~10 tasks

Adapter Directory Structure

adapters//
├── adapter.py              # Core conversion logic (adapter class)
├── run_adapter.py          # CLI entry point
├── run_.yaml   # Job config for oracle and parity experiment runs
├── README.md               # Benchmark docs, license, parity, citation
├── parity_experiment.json  # Parity tracking results (JSON array)
├── adapter_metadata.json   # Adapter metadata (JSON array)
└── template/               # Task template files
    ├── task.toml
    ├── instruction.md
    ├── environment/
    │   └── Dockerfile
    ├── tests/
    │   └── test.sh
    └── solution/
        └── solve.sh

All template files, parityexperiment.json, adaptermetadata.json, and README.md are required. The validator (harbor adapters validate) checks for all of them. The YAML job config is not validated but is expected for parity experiments.

Scaffolding with harbor adapters init

harbor adapters init my-benchmark

The interactive AdapterWizard prompts for:

• Benchmark name -- Vanilla name (e.g., "SWE-bench")
• Adapter ID -- Lowercase, no spaces (e.g., swebench)
• Class name -- Python class name (auto-derived or overridden, e.g., SwebenchAdapter)
• Description -- One-line description for the README
• Source URL -- Link to original benchmark paper or repo
• License -- Dataset license for the README

If all fields are provided as CLI arguments, the wizard runs non-interactively. The wizard renders Jinja2 templates from src/harbor/cli/template-adapter/ and places the result under adapters//.

After scaffolding, validate:

harbor adapters validate adapters/my-benchmark

Writing adapter.py

The adapter class has no formal base class, but every adapter must implement the same interface: a NAME attribute, make_local_task_id static method, and generate_task method.

Core Class Interface

import shutil
from pathlib import Path
from loguru import logger

class MyBenchmarkAdapter:
    """Converts MyBenchmark instances into Harbor task directories."""

    NAME = "mybenchmark"

    @staticmethod
    def make_local_task_id(source_id: str) -> str:
        """Convert a source benchmark ID to a Harbor task directory name.

        Convention: lowercase, hyphens as separators, prefixed with
        the adapter ID to prevent collisions across benchmarks.
        """
        normalized = source_id.lower().replace("_", "-")
        return f"mybenchmark-{normalized}"

    def __init__(self, task_dir: Path, **kwargs: object):
        """Initialize the adapter.

        Args:
            task_dir: Root directory where generated tasks will be written.
                      Each task gets a subdirectory: task_dir / local_task_id
            **kwargs: Adapter-specific configuration (data paths, splits, etc.)
        """
        self.task_dir = Path(task_dir)
        self._config = kwargs

        # Locate template files relative to this file
        self.template_dir = Path(__file__).parent / "template"
        if not self.template_dir.exists():
            raise FileNotFoundError(f"Template directory not found: {self.template_dir}")

        # Load your benchmark data here.
        # Examples from real adapters:
        #   SimpleQA: pandas.read_csv() from a URL
        #   GAIA: HuggingFace datasets or local JSONL
        #   AiderPolyglot: walk language dirs, parse .meta/config.json
        #   CodePDE: clone repo, load PDE descriptions
        logger.info(f"Initializing MyBenchmarkAdapter -> {self.task_dir}")

    def _copy_template(self, output_dir: Path) -> None:
        """Copy baseline template files into the output directory."""
        shutil.copytree(self.template_dir, output_dir, dirs_exist_ok=True)

    def generate_task(self, source_id: str, local_task_id: str) -> None:
        """Generate a single Harbor task directory.

        This is the core method. It must create a complete, valid task directory
        with all required files: task.toml, instruction.md, environment/Dockerfile,
        tests/test.sh, and optionally solution/solve.sh.

        Args:
            source_id: Identifier from the original benchmark
            local_task_id: Harbor task directory name (from make_local_task_id)
        """
        output_dir = self.task_dir / local_task_id
        output_dir.mkdir(parents=True, exist_ok=True)

        # 1. Copy baseline template
        self._copy_template(output_dir)

        # 2. Look up the source instance data
        # instance = self.data[source_id]

        # 3. Write task-specific files:
        #    - instruction.md  (the prompt the agent sees)
        #    - task.toml       (metadata, timeouts, resource limits)
        #    - environment/Dockerfile
        #    - tests/test.sh   (MUST write to /logs/verifier/reward.txt)
        #    - solution/solve.sh (reference solution for OracleAgent)
        #    - Copy attachments into environment/ if needed

        logger.info(f"Generated task {local_task_id} in {output_dir.resolve()}")

The generate_task Method in Detail

The generate_task method receives source_id (the original benchmark's identifier) and local_task_id (the Harbor directory name produced by make_local_task_id). It builds the output path from self.task_dir / local_task_id.

Typical steps inside generate_task:

1. Create directory and copy template -- shutil.copytree with dirs_exist_ok=True
2. Load instance data -- look up the specific benchmark record
3. Write instruction.md -- replace placeholders or write dynamically
4. Write task.toml -- set metadata, tags, timeouts
5. Write or modify Dockerfile -- add COPY commands for attachments
6. Write tests/test.sh -- the verification script (see test.sh requirements below)
7. Write solution/solve.sh -- reference solution for oracle agent
8. Copy attachments -- instance-specific files into environment/workspace/ or environment/

Data Loading Patterns (from real adapters)

| Adapter | Data Source | Loading Pattern | |---------|-----------|----------------| | SimpleQA | OpenAI public CSV | pandas.read_csv(url) in __init__ | | GAIA | HuggingFace or local JSONL | datasets library or line-by-line JSON | | AiderPolyglot | Exercism repo | Walk language dirs, parse .meta/config.json | | CodePDE | CodePDE repo | Clone repo, load PDE description files | | spider2-dbt | Spider2-DBT repo | Clone repo, read dbt project structures |

Template Filling Patterns

• Simple string replacement: content.replace("{KEY}", value) -- used by GAIA
• Placeholder replacement: content.replace("{{PLACEHOLDER}}", value) -- common pattern
• Direct file writes: Build content programmatically and write -- used by AiderPolyglot
• Jinja2: For complex templates with conditionals (not used by existing adapters but supported)

Handling Attachments

When source benchmarks include per-instance files (images, CSVs, databases):

1. Copy files into environment/workspace/ in the task directory
2. Add COPY workspace/ /app/files/ to the Dockerfile (GAIA pattern)
3. Reference files from instruction.md using the container path (e.g., /app/files/data.csv)
4. Mention the attachment in the instruction so the agent knows about it

Writing run_adapter.py

The CLI entry point follows a standard pattern with helper functions for argument parsing, ID collection, repo cloning, and benchmark processing. The default output directory is datasets/.

#!/usr/bin/env python3
import argparse
import sys
from pathlib import Path
from loguru import logger

sys.path.insert(0, str(Path(__file__).parent.parent))
from mybenchmark.adapter import MyBenchmarkAdapter

def _parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Generate Harbor tasks from MyBenchmark",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--output-dir", type=Path,
        default=Path("datasets/mybenchmark"),
        help="Directory to write generated Harbor task directories",
    )
    parser.add_argument(
        "--task-ids", nargs="*",
        help="Specific source task IDs to generate (space-separated)",
    )
    parser.add_argument(
        "--ids-file", type=Path,
        help="Path to a file containing source task IDs, one per line",
    )
    parser.add_argument(
        "--limit", type=int,
        help="Limit the number of tasks to generate",
    )
    return parser.parse_args()

def _collect_ids(task_ids: list[str] | None, ids_file: Path | None) -> list[str]:
    """Collect task IDs from command line args or a file."""
    if task_ids:
        return task_ids
    if ids_file:
        return [line.strip() for line in ids_file.read_text().splitlines() if line.strip()]
    return []

def _process_benchmark(output_dir: Path, task_ids: list[str]) -> None:
    """Initialize adapter and generate all tasks."""
    output_dir.mkdir(parents=True, exist_ok=True)
    adapter = MyBenchmarkAdapter(task_dir=output_dir)

    if not task_ids:
        # Load all available IDs from the benchmark data
        task_ids = adapter.get_all_source_ids()

    errors = []
    for i, source_id in enumerate(task_ids, 1):
        local_task_id = MyBenchmarkAdapter.make_local_task_id(source_id)
        try:
            adapter.generate_task(source_id, local_task_id)
            logger.info(f"[{i}/{len(task_ids)}] {source_id} -> {local_task_id}")
        except Exception as e:
            errors.append((source_id, str(e)))
            logger.error(f"[{i}/{len(task_ids)}] {source_id} FAILED: {e}")

    logger.info(f"Generated {len(task_ids) - len(errors)}/{len(task_ids)} tasks")
    if errors:
        sys.exit(1)

def main() -> None:
    args = _parse_args()
    task_ids = _collect_ids(args.task_ids, args.ids_file)
    if args.limit and task_ids:
        task_ids = task_ids[:args.limit]
    _process_benchmark(args.output_dir, task_ids)

if __name__ == "__main__":
    main()

test.sh Requirements

This is the most common source of adapter validation failures. The verifier uploads the entire tests/ directory to /tests inside the container, makes test.sh executable, and runs it.

test.sh MUST write a reward value to /logs/verifier/reward.txt -- a single float (e.g., 0 or 1 or 0.75). If this file is missing, the verifier raises RewardFileNotFoundError and the trial fails.

Optionally, test.sh can also write /logs/verifier/reward.json for structured reward data (e.g., {"reward": 1.0, "quality": 0.8}). The verifier checks reward.txt first, then falls back to reward.json.

Verification Patterns

String matching (GAIA pattern):

#!/bin/bash
mkdir -p /logs/verifier
EXPECTED=$(cat /tests/expected_answer.txt)
if [ ! -f /app/answer.txt ]; then
    echo 0 > /logs/verifier/reward.txt
    exit 0
fi
ACTUAL=$(cat /app/answer.txt | tr '[:upper:]' '[:lower:]' | xargs)
EXPECTED_NORM=$(echo "$EXPECTED" | tr '[:upper:]' '[:lower:]' | xargs)
if [ "$ACTUAL" = "$EXPECTED_NORM" ]; then
    echo 1 > /logs/verifier/reward.txt
else
    echo 0 > /logs/verifier/reward.txt
fi

Pytest-based (CodePDE, hello-world pattern):

#!/bin/bash
# Install test dependencies inside test.sh, not in the Dockerfile
apt-get update && apt-get install -y curl
curl -LsSf https://astral.sh/uv/0.9.7/install.sh | sh
source $HOME/.local/bin/env
uvx --with pytest==8.4.1 pytest /tests/test_solution.py -rA
if [ $? -eq 0 ]; then
    echo 1 > /logs/verifier/reward.txt
else
    echo 0 > /logs/verifier/reward.txt
fi

LLM judge (SimpleQA pattern):

#!/bin/bash
mkdir -p /logs/verifier
cd /tests
python3 llm_judge.py  # reads ground_truth.json, writes reward.txt

task.toml Configuration

version = "1.0"
source = "mybenchmark/instance-42"

[metadata]
author_name = "Your Name"
difficulty = "medium"           # easy, medium, hard
category = "qa"
tags = ["factual", "reasoning"]

[agent]
timeout_sec = 600.0             # max wall-clock seconds for the agent

[verifier]
timeout_sec = 600.0             # max seconds for test.sh
# env = { API_KEY = "..." }    # env vars injected into test.sh

[environment]
build_timeout_sec = 600.0       # max seconds for docker build
cpus = 1                        # CPU cores (default: 1)
memory_mb = 2048                # RAM in MB (default: 2048)
storage_mb = 10240              # disk in MB (default: 10240)
gpus = 0                        # GPUs (default: 0)
allow_internet = true           # network access (default: true)

[solution]
# env = { SECRET = "..." }     # env vars for solve.sh (OracleAgent)

parity_experiment.json

A JSON array of experiment objects tracking how Harbor results compare to the original benchmark. Required fields per entry: adapter_name, agent, model, date, metrics.

[
  {
    "adapter_name": "mybenchmark",
    "agent": "codex@0.77.0",
    "model": "openai/gpt-4o-2025-03-01",
    "date": "2026-01-15",
    "notes": "50 tasks; averaged over 3 trials",
    "original_parity_repo": "https://github.com/example/mybenchmark/tree/harbor-parity",
    "adapter_pr": [
      "https://github.com/harbor-framework/harbor/pull/123"
    ],
    "dataset_pr": [
      "https://github.com/harbor-framework/harbor-datasets/pull/45"
    ],
    "parity_pr": [
      "https://huggingface.co/datasets/harborframework/parity-experiments/discussions/10"
    ],
    "metrics": [
      {
        "benchmark_name": "MyBenchmark",
        "metric": "Accuracy",
        "original": "0.72 +/- 0.03",
        "harbor": "0.71 +/- 0.02",
        "original_trials": [0.73, 0.69, 0.74],
        "harbor_trials": [0.72, 0.70, 0.71]
      }
    ]
  }
]

The adapter_pr, dataset_pr, and parity_pr fields in parity_experiment.json must be arrays of URLs when present. Each metric object needs benchmark_name, metric, and at least one of original, tb_adapter, or harbor.

The harbor_adapter entry must include parity_matching_agents (array of "agent@version+model" strings). In the harbor-datasets registry.json, use "version": "parity" to enable harbor jobs start -d mybenchmark@parity.

adapter_metadata.json

A JSON array describing the adapter and its relationship to the original benchmark. Required fields per entry: adapter_name, adapter_builders, original_benchmark, harbor_adapter.

[
  {
    "adapter_name": "mybenchmark",
    "adapter_builders": [
      "Your Name (you@example.com)"
    ],
    "original_benchmark": [
      {
        "split": "test",
        "size": 500,
        "harness": "agent",
        "supported_agents": ["openhands"],
        "adaptable": true,
        "notes": "Only test split has ground-truth answers."
      }
    ],
    "harbor_adapter": [
      {
        "split": "test",
        "adapted_benchmark_size": 500,
        "parity_benchmark_size": 50,
        "parity_sampling_rate": 0.1,
        "registry_benchmark_size": 500,
        "parity_costs": "150",
        "parity_matching_agents": ["codex@0.77.0+openai/gpt-4o-2025-03-01"],
        "notes": "Full benchmark adapted. Parity on 10% sample."
      }
    ]
  }
]

Validation

The `harbor adapters

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: harbor-framework
• Source: harbor-framework/skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.