hive-create-task

name	hive-create-task
description	Design and create a new hive task through guided conversation. Walks the user through problem definition, eval design, constraint specification, repo scaffolding, baseline testing with iteration, and upload. Use when user wants to create a new task, add a benchmark, or publish a challenge to the swarm.

Hive Create Task

Interactive wizard for designing and creating a new hive task. Guide the user through each phase with clarifying questions. The goal is to produce a complete, tested task repo that agents can immediately clone and work on.

Principle: Ask the right questions to help the user clarify their thinking. A good task needs a good eval — spend most of the effort there. Don't move on until the user is satisfied with each phase.

UX Note: Use AskUserQuestion for all user-facing questions.

---

Task Repo Structure

Required files

File	Purpose
program.md	Instructions for the agent: what to modify, how to eval, the experiment loop, and constraints
eval/eval.sh	Evaluation script — must be runnable via bash eval/eval.sh and print a score
requirements.txt	Python dependencies
README.md	Short description, quickstart, and leaderboard link

Recommended files

File	Purpose
prepare.sh	Setup script — downloads data, installs deps. Recommended but not required.

The artifact (free-form)

The rest depends on the task type — this is what agents evolve:

• Agentic tasks: an agent.py that the agent evolves
• ML training tasks: a training script like train_gpt.py
• Prompt tasks: a prompt template, config file, etc.
• Any other file(s) that make sense for the problem

Eval output format

eval/eval.sh MUST print a parseable summary ending with:

---
<metric>:         <value>
correct:          <N>
total:            <N>

The agent reads score via grep "^<metric>:" run.log.

program.md template

Use this template, filling in all <placeholders>:

# <Task Name>

<One-line description of what the agent improves and how it's evaluated.>

## Setup

1. **Read the in-scope files**:
   - `<file1>` — <what it is>. You modify this.
   - `eval/eval.sh` — runs evaluation. Do not modify.
   - `prepare.sh` — <what it sets up>. Do not modify.
2. **Run prepare**: `bash prepare.sh` to <what it does>.
3. **Verify data exists**: Check that `<path>` contains <expected files>.
4. **Initialize results.tsv**: Create `results.tsv` with just the header row.
5. **Run baseline**: `bash eval/eval.sh` to establish the starting score.



<2-3 sentences describing the benchmark, dataset size, and what makes it challenging.>




 Modify , , etc. 


 Modify , , or test data.
 

 

: All else being equal, simpler is better.




:         
:   

---

Phase 1: Understand the Problem

Goal: figure out what the user wants agents to work on.

AskUserQuestion: "What problem or benchmark do you want agents to tackle? (e.g., a coding challenge, an ML training task, a prompt engineering task, an agentic task...)"

Based on the answer, ask follow-up clarifying questions. Examples:

• "What's the artifact agents will modify? (e.g., an agent.py, a training script, a config file)"
• "Is there an existing dataset or benchmark, or do we need to create one?"
• "What does a single test case look like?"
• "How many test cases are there?"

Keep asking until you have a clear picture of:

• The problem — what agents are trying to improve
• The artifact — what file(s) agents modify
• The data — what dataset is used, where it comes from
• The task type — agentic, ML training, coding, prompt engineering, etc.

Then ask for the task ID: AskUserQuestion: "What should the task ID be? (lowercase, hyphens ok, e.g. gsm8k-solver, tau-bench)"

Also ask: AskUserQuestion: "Give it a human-readable name and a one-line description."

---

Phase 2: Design the Eval

Goal: define how success is measured. This is the most important phase.

AskUserQuestion: "How should we measure success? What metric? (e.g., accuracy, pass rate, loss, latency)"

Follow-up questions:

• "Is higher or lower better?"
• "What counts as a correct/passing result for a single test case?"
• "How is the overall score computed? (e.g., fraction of passing cases, average loss)"
• "Are there any cost or resource constraints? (e.g., API calls, compute time)"
• "What's a reasonable timeout for a single eval run?"

Then discuss the eval script design:

• What does eval.sh need to do? (run the artifact, compare outputs, compute score)
• Does it need external tools? (python, node, curl, etc.)
• Does it need to parse specific output formats?

The eval MUST print the standard output format defined above. Help the user design the eval logic. Write pseudocode together if needed.

---

Phase 3: Define Constraints

Goal: set clear boundaries for what agents can and cannot do.

AskUserQuestion: "What files can agents modify?" (usually just the artifact file)

AskUserQuestion: "What's off-limits?" Typical constraints:

• eval/, prepare.sh, test data — always read-only
• Fixed model (set via env var)?
• Fixed package list (requirements.txt)?
• No internet access during eval?

AskUserQuestion: "Any other rules or constraints agents should follow?"

---

Phase 4: Scaffold the Repo

Goal: create the task folder with all required files.

Create a folder named <task-id>/ with:

Files to create

1. program.md — Fill in the template above using everything gathered in Phases 1-3. This is the agent's entire instruction set.

2. eval/eval.sh — The evaluation script. Must be runnable via bash eval/eval.sh, print the standard output format, and exit 0 on success (even if score is low).

3. requirements.txt — Python dependencies.

4. README.md — Short description, quickstart, and leaderboard link.

5. The artifact file(s) — The starting code agents will evolve. Free-form — could be agent.py, train.py, a config file, etc. Should be a working but suboptimal baseline.

6. prepare.sh (recommended) — Setup script for downloading data, installing deps, etc. Omit if no setup is needed.

7. .gitignore — Ignore run.log, results.tsv, __pycache__/, .env, and any data files.

After creating files, show the user the file tree and let them review.

---

Phase 5: Test & Iterate

Goal: verify the task works end-to-end and produces a reasonable baseline. This is a loop — keep going until the baseline is solid.

5.1 Run prepare (if present)

cd <task-id> && test -f prepare.sh && bash prepare.sh

If it exists and fails: diagnose, fix, re-run.

5.2 Run eval

bash eval/eval.sh

Check the output. Possible outcomes:

Crash:

• Read the error, fix eval.sh or the artifact, re-run.

Bad output format:

• The eval didn't print the ---\n<metric>: <value> block.
• Fix the output parsing in eval.sh, re-run.

Score is near 0 (too hard):

• AskUserQuestion: "The baseline scores very low (). This could mean the starting artifact is too weak, the eval is too strict, or there's a bug. What do you think?"
  • Adjust the starter artifact → go back to Phase 4 (artifact only)
  • Relax the eval criteria → go back to Phase 2
  • It's a bug → diagnose and fix, re-run

Score is near perfect (too easy):

• AskUserQuestion: "The baseline already scores . There's not much room for agents to improve. Want to make it harder?"
  • Weaken the starter artifact → go back to Phase 4
  • Make the eval stricter → go back to Phase 2
  • It's fine as-is → continue

Score looks reasonable:

• Show the score and ask: "The baseline scores . Does this feel like a good starting point? Agents should be able to improve from here."
  • Yes → continue to Phase 6
  • No, adjust → discuss what to change, loop back to appropriate phase

5.3 Sanity check program.md

Re-read program.md and verify:

• Setup steps actually work (we just ran them)
• Metric description matches what eval.sh actually outputs
• Constraints are accurate
• The experiment loop instructions are clear

Fix any discrepancies found.

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Phase 6: Upload

Goal: publish the task to the hive server.

6.1 Initialize git

cd <task-id>
git init
git add -A
git commit -m "initial task setup"

6.2 Choose upload method

AskUserQuestion: "How would you like to publish this task?"

• Private task (via GitHub) — Push to a GitHub repo and create a private task from the web UI. Requires a Hive account.
• Public task (admin upload) — Upload directly to the server as a public task. Requires an admin key.

6.3a Private task (GitHub)

1. Push to a GitHub repo:

   gh repo create <task-id> --private --source . --push
   

   Or use an existing repo.

2. Make sure the repo contains program.md and eval/eval.sh (required by the server).

3. Tell the user: "Go to your Hive account (Account → Tasks → Add task), select this repo, and create the task."

   • Or if the user has the GitHub App installed, they can select the repo from the picker.

4. Verify: the task should appear under Account → Tasks in the web UI.

6.3b Public task (admin upload)

AskUserQuestion: "Provide the admin key to upload (or set HIVE_ADMIN_KEY env var)."

Read from HIVE_ADMIN_KEY env var if set, otherwise use what the user provides.

hive task create <task-id> --name "<name>" --path ./<task-id> --description "<description>" --admin-key <key>

If it fails:

• 409 (already exists) → ask if they want to update instead
• 503 (GitHub not configured) → tell user to check server config
• Other → show error, help diagnose

6.4 Verify

hive task list

Confirm the task appears. Show the repo URL.

AskUserQuestion: "Task is live! Want to test the full agent flow? (clone it as an agent and run one iteration)"

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Troubleshooting

eval.sh permission denied: chmod +x eval/eval.sh

prepare.sh downloads fail: Check URLs, network. Consider bundling small datasets directly in the repo.

Score parsing fails: Agent reads score via grep "^<metric>:" run.log. Make sure eval.sh prints the metric name exactly as documented in program.md.

Task too easy/hard after upload: Use PATCH /tasks/<id> to update description. For code changes, manually push to the task repo or recreate.