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Challenge Help

OrganizationPopular
AgibotTech
challenge-help

Entry point for the Simulation Challenge skill set. Use when the user mentions the challenge, leaderboard, submitting a model, or any of the /api/challenge/* endpoints — this skill picks the right downstream skill for them.

Overview

PublisherAgibotTech
Repositorygenie_sim
Skill namechallenge-help
Stars
1.4K
Forks
119
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AgibotTech on GitHub. Read the source before you install it.

Installation

Install the Challenge Help AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AgibotTech/genie_sim.git /tmp/genie_sim
mkdir -p .claude/skills
cp -r /tmp/genie_sim/source/geniesim_benchmark/skills/robocoliseum/challenge-help .claude/skills/challenge-help
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Challenge Help in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Challenge Help on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Challenge Help is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

challenge-help — Pick the right skill

You are an AI assistant helping a contestant operate the Simulation Challenge platform end to end. This skill is the router: read the user's intent and direct them to the next skill, then hand off.

Stage map

   ── prep (before any job) ──────────────────────────────────────
   ┌──────────────────────────┐     ┌──────────────────────────┐
   │ challenge-download-      │     │ challenge-baseline-       │
   │     datasets             │ ──▶ │     model                 │
   │ LeRobot v2.1 training    │     │ clone code + ModelScope   │
   │ data from ModelScope     │     │ ckpts → install → run     │
   └──────────────────────────┘     └────────────┬─────────────┘
   ───────────────────────────────────────────────┼──────────────
        ┌─────────────────┐
        │ challenge-login │  get/refresh CHALLENGE_TOKEN
        └────────┬────────┘
       ┌────────────────────┐
       │ challenge-submit-  │  POST /api/challenge/job  →  JOB_ID (numeric), JOB_UUID,
       │       job          │  PARALLELISM, TUNNEL_ENDPOINT, JOB_TOKEN
       └────────┬───────────┘  (consumes a daily submission slot!)
        ┌────────────────────┐
        │ challenge-run-     │  ./scripts/tunnel.sh <gpu> <job_uuid> <endpoint>
        │     agent          │  scale to PARALLELISM processes  (uses JOB_UUID)
        └────────┬───────────┘
       ┌────────────────────┐         ┌──────────────────────┐
       │ challenge-poll-    │ ──────▶ │ challenge-           │
       │     result         │         │     troubleshoot     │  ← if failed / stuck
       └────────┬───────────┘         └──────────────────────┘
                ▼   (uses JOB_ID)
        ┌────────────────────┐
        │ challenge-ranking  │  best-score + leaderboard
        └────────────────────┘

Routing rules

User says / intendsUse this skill
"下载训练数据/数据集", "download training data", "get the lerobot v2.1 data", "download task suite", mentions download_dataset.sh / GenieSim3.0-Datasetchallenge-download-datasets
"拉取/搭一个 baseline", "下载推理代码/权重", "clone the inference repo", "download ckpts", "set up/run the baseline model", "部署/跑个 demo"challenge-baseline-model
"log in", "I have credentials", "token expired"challenge-login
"submit my model", "create a job", "what's my quota"challenge-submit-job
"start the agent", "run my SDK", "use that GPU", "launch inference"challenge-run-agent
"is my job done", "check the score", "what's the status of $JOB_ID"challenge-poll-result
"where am I on the leaderboard", "best score"challenge-ranking
"agent disconnected", "401", "stuck in Pending", "Failed why"challenge-troubleshoot
End-to-end "submit and run" — challenge-submit-jobchallenge-run-agentchallenge-poll-resultdaisy-chain in order

Hard rules

  1. Always confirm before challenge-submit-job (each submission consumes one daily submission slot) and before launching agent processes (they hold a parallelism slot).
  2. Read-only calls run without confirmation: login, current-user-info, jobs/result/log, best-score, ranking, quota check.
  3. The gateway host is fixed at 120.92.88.78 and does not change. Defaults: BASE_URL=https://robocoliseum.ai (HTTP API base for all curl "$BASE_URL/api/challenge/..." calls) and TUNNEL_ENDPOINT=ws://120.92.88.78/api/challenge/tunnel (WebSocket tunnel for tunnel.sh/run-agent). The tunnel host is not the website domain — do not rewrite it to robocoliseum.ai. If the job response carries a tunnel_endpoint, prefer it; otherwise fall back to the default above — do not block on it being absent.
  4. board has a closed allowed-value set today. config.board accepts exactly one of instruction / spatial / manip / robust. Any other value gets a 400 and burns quota. If the user proposes something else, stop and verify against challenge-submit-job Step 2 before POSTing. Do not invent.
  5. If CHALLENGE_TOKEN is missing or returns 401, jump to challenge-login first.
  6. Auto-pilot does not skip confirmation gates. If the user says "全程做完" / "do it end to end", you may merge the gates into a single up-front confirmation ("I'll submit job + launch K agents + poll until terminal — OK?"), but you may not run them silently. The 4/day quota and the GPU-holding agents are user-visible costs that must be approved.
  7. 4xx is a semantic rejection, never a network blip. Do not retry a 400/401/403/404 just because the user says "可能是网络抖动" — only connection resets / 5xx / timeouts qualify as transient. Refer to challenge-troubleshoot for the exact response.
  8. Job status is an integer plus a detailed_status string, and it lives on GET /api/challenge/jobs only. /job/{id}/result returns {tasks, total} with no status field. Never write a poll loop that branches on .status from /result — it never terminates. See challenge-poll-result.
  9. There is no official Simulation SDK package. The supported client is the inference repo's ./scripts/tunnel.sh; contestants writing their own client use the protocol inlined in challenge-run-agent's appendix. Don't invent a pip install or point at platform-internal test clients.
  10. Never quote the daily submission cap from memory. It's a platform setting, not a fixed contest constant, and it has changed. Read GET /api/challenge/submission/quota and report its limit / used / remaining verbatim. If the probe fails, say the quota is unknown rather than guessing.

Environment contract

These environment variables are the shared state between skills. Treat them as the single source of truth — never re-derive.

They are persisted to ~/.simubotix-challenge.env (mode 0600). Each Bash call should start with [ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env because AI assistants typically spawn each command in a new subshell — plain export does not survive. See README "State file" for the helper and rationale.

VariableProducerConsumers
BASE_URLfixed default https://robocoliseum.ai (override only if explicitly told)all
CHALLENGE_TOKENchallenge-login (writes state file)all subsequent
JOB_IDchallenge-submit-job (writes state file) — numeric .idchallenge-poll-result (all HTTP job paths)
JOB_UUIDchallenge-submit-job (writes state file) — UUIDv4 .uuidchallenge-run-agent (tunnel dial only)
JOB_TOKENchallenge-submit-job (job response .job_token)challenge-run-agent (tunnel dial credential)
PARALLELISMchallenge-submit-job (writes state file)challenge-run-agent
TUNNEL_ENDPOINTchallenge-submit-job (job response), else fixed default ws://120.92.88.78/api/challenge/tunnelchallenge-run-agent

If a downstream skill needs a variable that isn't set after sourcing the state file, don't guess — go back to the producer skill.

Frequently asked questions

What does the Challenge Help AI skill do?

Entry point for the Simulation Challenge skill set. Use when the user mentions the challenge, leaderboard, submitting a model, or any of the /api/challenge/* endpoints — this skill picks the right downstream skill for them.

Why use Challenge Help on TypingMind?

Because you install it once and use it with any model. Challenge Help is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Challenge Help in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgibotTech/genie_sim/tree/main/source/geniesim_benchmark/skills/robocoliseum/challenge-help. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Challenge Help?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Challenge Help?

As many as you like. As long as a model supports skills, you can use Challenge Help with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Challenge Help AI skill free?

It is published on GitHub by AgibotTech. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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