create-adapter

A procedure for creating a new Harbor benchmark adapter, which connects a benchmark dataset to the Harbor evaluation system. It scaffolds the adapter and follows Harbor's adapter guide for implementation.

In plain words
What is it for?
Initializing an adapter, verifying expected results, registering its dataset, running parity experiments, and preparing the required files and README.
Why use it?
It provides the required structure and checks for benchmark tasks, datasets, metadata, and result matching instead of relying on guesswork.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/harbor-framework/harbor/create-adapter
Any agent
npx skills add harbor-framework/harbor --skill create-adapter
Clone the repo
git clone --depth 1 https://github.com/harbor-framework/harbor

Made for: Claude Code, Codex.

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,674 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00034 $0.01674
Opus 5 $0.00017 $0.00837
Sonnet 5 $0.00007 $0.00335
Haiku 4.5 $0.00003 $0.00167

Measured 2d ago against content hash e9e912e237f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

create-adapter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

skills/create-adapter/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Create Adapter

Bootstrap a new benchmark adapter in the Harbor repository. This skill scaffolds the adapter directory with harbor adapter init and then defers to the adapter tutorial for every implementation decision.

Authoritative reference

The adapter tutorial is the authoritative specification for this skill. Read it in full before taking any action beyond scaffolding:

docs/content/docs/datasets/adapters.mdx

That path is relative to the Harbor repo root (a skill prerequisite — see below). The tutorial contains:

  • Required directory structures for the generated tasks and the adapter code package.
  • Step-by-step process (steps 1-9) covering benchmark analysis, oracle verification, parity experiments, dataset registration, and submission.
  • Schemas for task.toml, parity_experiment.json, adapter_metadata.json, and dataset.toml.
  • Parity matching criterion, pre-flight checklist, and debug playbook.
  • README format rules (machine-parsed; deviations break automation).

Do not substitute prior knowledge for the contents of that file. Treat it as the contract.

Prerequisites

  • Harbor CLI is installed and available on PATH (harbor --version succeeds).
  • Working directory is the Harbor repository root.
  • Harbor checkout is current: run git fetch origin && git status and pull main if behind. Stale checkouts miss recent adapter and agent fixes and are a common source of spurious parity failures later on.

Workflow

1. Read the tutorial

Before any filesystem or CLI action, Read docs/content/docs/datasets/adapters.mdx in full. Pay particular attention to:

  • Required Directory Structures — the contract for generated task and adapter layouts.
  • Step 1. Understand the Original Benchmark — what to identify upstream before coding.
  • Step 8. Register the Dataset → Naming rules — the name field and <org>/<task> format requirements.

2. Gather benchmark context from the user

Collect the following before scaffolding. If the user has not provided an item, ask before proceeding — these inputs shape the scaffold and the tutorial steps that follow.

Read the full file on GitHub · 116 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 116 lines · 34 tokens per session scan A e9e912e237f1

Subscribe to this mod's changes

create-adapter is a skill published in the GitHub repository harbor-framework/harbor (4,854 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 1,674 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.