Challenge Submit Job logo

Challenge Submit Job

OrganizationPopular
AgibotTech
challenge-submit-job

Use when the contestant wants to submit a model evaluation job to the Simulation Challenge — POST /api/challenge/job. This is a quota-consuming, side-effecting action; always confirm with the user first. Captures JOB_UUID, PARALLELISM, TUNNEL_ENDPOINT for the rest of the pipeline.

Overview

PublisherAgibotTech
Repositorygenie_sim
Skill namechallenge-submit-job
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 Submit Job 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-submit-job .claude/skills/challenge-submit-job
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Challenge Submit Job 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 Submit Job 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 Submit Job 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-submit-job — Create a model evaluation job

Submitting a job (a) consumes one of the user's daily submission slots (test accounts are exempt) and (b) immediately starts incurring scheduling work on the platform. Treat this as side-effecting: always confirm before running the POST.

Never state the daily cap from memory. The limit is a platform-side setting, not a fixed contest constant, and it has changed. Step 1 reads it from GET /api/challenge/submission/quota — quote limit / used / remaining from that response and nothing else. Saying "you have N of 4 left" when the live limit is different misleads the user into either wasting or withholding submissions.

Preconditions

  • CHALLENGE_TOKEN must be set (else → challenge-login).
  • BASE_URL defaults to the fixed https://robocoliseum.ai (override only if explicitly told).

Every Bash call in this skill should start with:

bash
[ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env

(Cross-shell state lives in ~/.simubotix-challenge.env; AI assistants spawn each command in a new subshell. See README "State file".)

Step 1 — Probe the daily quota (mandatory)

Always run this immediately before every POST, even if you submitted a job earlier in the same session — there's no in-session counter you can trust, and a shared account may have been used elsewhere. This is also the only place the daily cap comes from; do not carry a number over from a previous session or from prose.

Two endpoints work:

bash
[ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env

# Preferred: structured remaining count
curl -fsS "$BASE_URL/api/challenge/submission/quota" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" | jq
# { "limit": <platform-configured>, "used": 1, "remaining": <limit - used> }

# Legacy boolean check (still works):
curl -fsS "$BASE_URL/api/challenge/model/upload/check" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN"
# { "status": "ok" }  → quota remains

If remaining == 0 (or upload/check errors with an "upload limit" message), stop: the user has used today's slots. The daily window resets at Beijing midnight (UTC+8). Suggest they retry tomorrow or wait. Do not proceed to Step 2.

When you report the quota back to the user, quote the actual numbers you just received — e.g. "used 1 / limit <what the API said>, N left today" — rather than describing the cap in words or filling in a remembered number. If the call fails, say the quota is unknown; don't guess a limit.

Step 2 — Build the request body

The request body has a top-level name plus a config object. model_path is no longer required — the platform resolves the model from model_name.

FieldWhereRequiredNotes
nametop-levelyesJob name shown in the contestant's job list.
config.boardconfigyesBoard short-id (single value). Allowed values: instruction, spatial, manip, robust.
config.model_nameconfigyesModel identifier.
config.descriptionconfignoFree-form text.
config.paper_linkconfignoOptional URL to a paper/arxiv page describing the model. Must be a valid URL, ≤ 512 chars. Used for audit/leaderboard display.

Allowed-value enforcement. If the user supplies a board name other than instruction / spatial / manip / robust, stop and ask — don't POST. Submitting with an unknown board will return 400 invalid board / 400 no task templates for board and burn a daily submission slot. If they say "use the new one X" / "I heard the platform added Y", verify with the organizers before proceeding — this skill is the single source of truth for what's currently accepted. Do not accept a board name sourced from anywhere else.

One submission = one board = one job. To evaluate multiple boards, submit once per board; each call consumes one of the daily submission slots reported by Step 1.

Read each missing required field back to the user. If board is omitted, default to "instruction" (the other accepted values are spatial, manip, robust). Say the defaults explicitly so the user can object.

Step 3 — Confirm, then POST

Show the assembled body, the daily-quota cost (one slot — quote the used/limit numbers from Step 1), and ask for explicit "yes" before running:

Overwrite check (single-file state). If ~/.simubotix-challenge.env already has JOB_ID set from a previous submission, this POST will overwrite the JOB_* vars and the previous job's IDs will be lost from the state file. Before POSTing, surface this and check whether the previous job is terminal. Status lives on the job list, not on /result (/result has no status field at all — see challenge-poll-result):

bash
[ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env
if [ -n "$JOB_ID" ]; then
  PREV=$(curl -fsS "$BASE_URL/api/challenge/jobs?page=1&per-page=100" \
    -H "Authorization: Bearer $CHALLENGE_TOKEN" \
    | jq -r --argjson id "$JOB_ID" '.items[] | select(.id == $id) | .detailed_status // "?"')
  echo "previous JOB_ID=$JOB_ID detailed_status=${PREV:-not-found} — submitting will overwrite the state file"
fi
  • If PREV is completed or failed (terminal), just confirm with the user that they're OK losing the IDs (job history is still queryable via GET /api/challenge/jobs) and proceed.
  • If PREV is pending / queued / evaluating / inference_disconnected, stop and warn: overwriting means losing the handle to a job that is still live. Ask the user explicitly whether they want to (a) wait for it via challenge-poll-result, (b) record JOB_ID=$JOB_ID / JOB_UUID=$JOB_UUID themselves before continuing, or (c) proceed knowing they'll need to find the job by name in GET /api/challenge/jobs later.
bash
[ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env
JOB_RESP=$(curl -fsS -X POST "$BASE_URL/api/challenge/job" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" \
  -d '{
    "name": "challenge_test",
    "config": {
      "board":        "instruction",
      "model_name":   "test",
      "description":  "challenge_test"
    }
  }')

echo "$JOB_RESP" | jq

A 400 with invalid board or no task templates for board means the board is wrong — go back to Step 2, do not retry.

Step 4 — Capture the response

The response is a single job descriptor. These variables are the contract handed to every later skill. Persist all four to ~/.simubotix-challenge.env so they survive across new Bash subshells:

bash
JOB_ID=$(echo          "$JOB_RESP" | jq -r '.id')
JOB_UUID=$(echo        "$JOB_RESP" | jq -r '.uuid')
PARALLELISM=$(echo     "$JOB_RESP" | jq -r '.parallelism')
TUNNEL_ENDPOINT=$(echo "$JOB_RESP" | jq -r '.tunnel_endpoint')

challenge_save_var JOB_ID           "$JOB_ID"
challenge_save_var JOB_UUID         "$JOB_UUID"
challenge_save_var PARALLELISM      "$PARALLELISM"
challenge_save_var TUNNEL_ENDPOINT  "$TUNNEL_ENDPOINT"

echo "persisted: job_id=$JOB_ID uuid=$JOB_UUID parallelism=$PARALLELISM endpoint=$TUNNEL_ENDPOINT"

Also extract and persist job_token, the per-job tunnel credential: it never expires and is scoped to this one job, and the next step (challenge-run-agent) uses it instead of CHALLENGE_TOKEN when dialing the tunnel. Older platform builds may omit the field — that's fine, the agent falls back to CHALLENGE_TOKEN:

bash
JOB_TOKEN=$(echo "$JOB_RESP" | jq -r '.job_token // empty')
if [ -n "$JOB_TOKEN" ]; then
  challenge_save_var JOB_TOKEN "$JOB_TOKEN"   # per-job tunnel credential
fi

(challenge_save_var is the helper defined in challenge-login Step 1 / README. If it isn't loaded, re-paste it once per shell.)

Multiple boards: a single submission accepts exactly one board. To evaluate instruction + manip, submit twice — each call gets its own job (and uses one daily submission slot). Each job's uuid / tunnel_endpoint is independent; launch a separate agent process (or process group, up to that job's parallelism) per job. Re-probe the quota before the second POST.

Sanity-check before handing off:

  • JOB_UUID matches a UUIDv4-shape string.
  • PARALLELISM is an integer ≥ 1. 0 means the user's concurrency cap is misconfigured (not "exhausted" — exhaustion is a runtime live-connection check at the gateway, not a response field). Stop and surface to the organizers; do not launch agents.
  • TUNNEL_ENDPOINT starts with ws:// or wss://. If empty, do NOT hard-code a URL. Retry GET /api/challenge/tunnel/endpoint after a few seconds; an empty string means the gateway is not yet ready.

Step 5 — Hand off

Tell the user:

Job $JOB_ID ($JOB_UUID) is READY. The platform allows up to $PARALLELISM concurrent agent processes for this job. Cases will sit in queue until at least one agent is connected. Run challenge-run-agent next to launch the inference SDK.

Also remind them: the next step needs ./scripts/tunnel.sh from the inference repo (separate from this platform repo). If they haven't cloned it yet, this is the moment to do so — otherwise their cases will sit in the queue with no agent attached.

Do NOT auto-launch the agent. The user controls when GPUs start spinning.

Common errors

Error from POSTLikely causeFix
400 invalid board / no task templates for boardboard not in instruction/spatial/manip/robustVerify board with organizers; do not retry blindly (each retry burns a daily submission slot).
400 paper_link is not a valid URL / paper_link too longOptional paper_link field malformedFix the URL (≤ 512 chars, must parse as a URL) or omit it.
upload limit error / quota remaining == 0Daily submission cap hitWait until Beijing midnight (UTC+8).
401Token expired/invalidchallenge-login → refresh.

Frequently asked questions

What does the Challenge Submit Job AI skill do?

Use when the contestant wants to submit a model evaluation job to the Simulation Challenge — POST /api/challenge/job. This is a quota-consuming, side-effecting action; always confirm with the user first. Captures JOB_UUID, PARALLELISM, TUNNEL_ENDPOINT for the rest of the pipeline.

Why use Challenge Submit Job on TypingMind?

Because you install it once and use it with any model. Challenge Submit Job 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 Submit Job 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-submit-job. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Challenge Submit Job?

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 Submit Job?

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

Is the Challenge Submit Job 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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