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.
npx skills add zli12321/LHTB --skill create-taskgit clone --depth 1 https://github.com/zli12321/LHTBWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zli12321/lhtb/create-task)<a href="https://agentmods.dev/skills/zli12321/lhtb/create-task"><img src="https://agentmods.dev/badge/skills/zli12321/lhtb/create-task/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zli12321/lhtb/create-task"><img src="https://agentmods.dev/badge/skills/zli12321/lhtb/create-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.03448 |
| Opus 5.5 | $0.00022 | $0.01379 |
| Sonnet 5.5 | $0.00011 | $0.00690 |
| Haiku 4.5 | $0.00006 | $0.00345 |
Grade A, and why
create-task 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 1mo 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.
This is a copy
88% identical to create-task — 124 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guide the user through creating a new Harbor task end-to-end. Don't just dump commands — walk them through each decision, especially around the verifier (which is usually the hardest part).
Step 1: Scaffold the task
harbor task init "<org>/<task-name>"
Useful flags:
--description "..."--author "Jane Doe <[email protected]>"(repeat for multiple authors)--no-pytest— skip the pytest test template (use if planning Reward Kit or custom verifier)--no-solution— skip solution/ directory--metadata-template path.toml— pre-populate task.toml
Produces:
<task-name>/
├── instruction.md # Task prompt for the agent
├── task.toml # Config and metadata
├── environment/Dockerfile # Container definition
├── solution/solve.sh # Reference solution (optional)
└── tests/test.sh # Verifier script
If the user wants a multi-step task (ordered steps with per-step
instructions, tests, and early stopping against a shared container), scaffold
the single-step layout first, then convert to the steps/ layout described in
the Multi-step tasks section below.
Step 2: Write instruction.md
This is the prompt the agent receives. Help the user write it clearly:
- State the goal concretely — what file to create, what behavior to produce
- Specify expected outputs — paths, formats, content
- Include constraints — language, tools, approach
- Don't leak the tests — describe what "done" looks like, not how you'll check it
Example (from the ssh-key-pair tutorial):
# SSH Key Pair Generation
Generate an SSH key pair in the files `~/.ssh/id_rsa` and `~/.ssh/id_rsa.pub`.
Don't make them password protected.
Step 3: Build the environment
Edit environment/Dockerfile to install dependencies the task needs. The agent works
inside this container.
FROM ubuntu:24.04
WORKDIR /app
# Install what the task requires — NOT the solution
RUN apt-get update && apt-get install -y openssh-client && rm -rf /var/lib/apt/lists/*
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.
- 1mo ago First seen · 399 lines · 56 tokens per session scan F a6b488aa3bcd
create-task is a skill published in the GitHub repository zli12321/LHTB (562 stars, last pushed 5d ago), licensed Apache-2.0. It adds 56 tokens to every session and 3,448 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to create-task, differing in 124 lines, and is treated as a copy.
Other skills, from other repositories
rewardkit
Write Harbor task verifiers using Reward Kit. Use when creating or editing a task's tests/ directory, adding grading criteria, setting up LLM/agent judges, or designing verifiers that produce a reward score.
create-adapter
Scaffold a new Harbor benchmark adapter by running harbor adapter init and then guide implementation using the Adapters Agent Guide as the authoritative spec.
analyze-task
Check OSWorld tasks. Validate the evaluation function, verify that the instruction is feasible given the task setup and agent-visible files, inspect setup artifacts when needed, and produce both markdown and structured JSON reports.
create-task
Create a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution scripting.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.