Challenge Poll Result logo

Challenge Poll Result

OrganizationPopular
AgibotTech
challenge-poll-result

Use to track a Simulation Challenge job's progress — list jobs, watch a job's status until terminal, fetch its per-task scores, or pull execution logs when it failed. Read-only; safe to run without confirmation.

Overview

PublisherAgibotTech
Repositorygenie_sim
Skill namechallenge-poll-result
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 Poll Result 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-poll-result .claude/skills/challenge-poll-result
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Challenge Poll Result 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 Poll Result 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 Poll Result 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-poll-result — Track job status, scores, and logs

All endpoints here are read-only — run them directly and report findings to the user.

Preconditions

  • CHALLENGE_TOKEN set (else → challenge-login).
  • JOB_ID set (else → challenge-submit-job, or list jobs first to find one).

Every Bash call should start with:

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

(See README "State file" — AI assistants spawn each command in a new subshell, so file-backed state is the only reliable handoff.)

Two job identifiers — do not mix them up

VariableShapeUsed for
JOB_IDinteger (e.g. 42), from the submit response's .idevery HTTP path here: /job/{JOB_ID}/result, /log, /episodes/videos
JOB_UUIDUUIDv4 string, from the submit response's .uuidonly the WebSocket tunnel dial (challenge-run-agent)

The path segment is parsed as an unsigned integer. Passing the UUID returns 400 {"status":"error","error":"invalid job_id: <uuid>"} — under curl -fsS that surfaces only as exit code 22, which reads like a server fault. If a call fails, check you used the numeric id first.

Where status actually lives

There is no single-job status endpoint, and /result carries no status field. Status comes from the job list:

bash
[ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env
curl -fsS "$BASE_URL/api/challenge/jobs?page=1&per-page=20" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" | jq

Query param is per-page with a hyphen. The server reads per-page; per_page is silently ignored and you get the default page size of 20. The response echoes it back as per_page (underscore) — the asymmetry is real, don't "fix" it in either direction.

Response envelope:

json
{ "items": [], "total": 7, "page": 1, "per_page": 20, "total_pages": 1 }

Each item, with the fields that matter for polling:

json
{
  "id": 42,
  "uuid": "3f7a18c2-8e1d-4bd9-9a42-9a14c0b7a7b1",
  "name": "challenge_test",
  "model_name": "test",
  "board": "instruction",
  "status": 1,
  "detailed_status": "evaluating",
  "progress": { "done": 3, "total": 12 },
  "score": 0,
  "time_cost": 0,
  "created_at": "…",
  "list_on_leaderboard": true
}

Other query params: model_name (fuzzy match), order / sort-by (col:dir, comma-separated). There is no id filter — select client-side with jq.

status is an integer, not a string

statusNameMeaning
0ReadyCreated, nothing dispatched yet
1RunningAt least one case dispatched
2SucceedAll cases landed successfully
3StoppedStopped by user or system
4FailedA case exhausted its retries → the whole job is dead
5WaitingForStopStop requested, teardown in flight

Do not compare it against "Finished" / "Pending" / "Cancelled" — those strings exist nowhere in this API.

detailed_status is the string you should actually branch on

detailed_statusMeaningNext action
pendingNo agent has ever connected for this jobLaunch agents (challenge-run-agent). Cases sit in queue until one is online.
queuedAn agent tunnel is live (connected, warming up, or idle) but no case is executing yet — usually waiting for a free GPU cluster-wideKeep the agents alive and keep polling. Nothing to fix.
evaluatingAt least one case is executingKeep polling.
inference_disconnectedAn agent connected earlier but none is live nowCheck the agent processes; see challenge-troubleshoot.
completedTerminal — all cases landed, or job reached Succeed/StoppedStop polling. Fetch /result, then suggest challenge-ranking.
failedTerminal — job is deadStop polling. Pull /log and route to challenge-troubleshoot.

Terminal set is exactly {completed, failed}. failed is checked before the "all cases done" rule, so a job whose cases all landed but which was judged failed still reports failed — trust detailed_status, not progress.

progress is case-level X/N. On a failed job it freezes at the real count (aborted in-flight cases count as done; never-started ones don't), so done < total on a failed job is expected, not a bug.

Polling loop

Poll every 2–5 seconds until detailed_status is terminal. Don't poll faster — the platform doesn't change state that quickly and you'll burn rate budget.

bash
while true; do
  [ -f ~/.simubotix-challenge.env ] && . ~/.simubotix-challenge.env
  JOB=$(curl -fsS "$BASE_URL/api/challenge/jobs?page=1&per-page=100" \
    -H "Authorization: Bearer $CHALLENGE_TOKEN" \
    | jq -c --argjson id "$JOB_ID" '.items[] | select(.id == $id)')
  if [ -z "$JOB" ]; then
    echo "job $JOB_ID not found on page 1 — widen per-page or check the id"; break
  fi
  DS=$(echo "$JOB" | jq -r '.detailed_status // "?"')
  PROG=$(echo "$JOB" | jq -r '"\(.progress.done)/\(.progress.total)"')
  SCORE=$(echo "$JOB" | jq -r '.score')
  printf "detailed_status=%-22s progress=%-8s score=%s\n" "$DS" "$PROG" "$SCORE"
  case "$DS" in
    completed|failed) break ;;
  esac
  sleep 3
done

--argjson (not --arg) matters: .id is a JSON number, and select(.id == "42") never matches a string.

If the job might be off page 1 (many submissions), either raise per-page or add &sort-by=id:desc.

When you call this from an AI assistant, do not loop indefinitely without checking in — every ~30 seconds report progress (detailed_status, progress, score) so the user can interrupt if something looks wrong (e.g. stuck at pending because no agent is connected).

Fetch a job's scores

bash
curl -fsS "$BASE_URL/api/challenge/job/$JOB_ID/result" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" | jq

Exact response shape — two keys, no status, no job id:

json
{
  "tasks": {
    "pick_block_color": { "score": [95.0, 92.0, 88.0], "total": 91.67 },
    "place_on_plate":   { "score": [100.0, 100.0, 98.0], "total": 99.33 }
  },
  "total": 191.0
}
  • Keys of tasks are task names (EmuTask.Name), which vary by board — don't hard-code them.
  • tasks[…].score is a per-step average across the task's latest episodes (element i is the mean of step i), not a per-case score list.
  • tasks[…].total is the mean of those episodes' average scores.
  • Top-level total is the job's stored score. It is 0 until scoring lands, so a 0 here on a still-running job means "not scored yet", not "scored zero".
  • A job with no episodes yet returns {"tasks":{},"total":0} — that is a success, not an error.

Because there's no status in this body, never use /result to decide whether to keep polling. Call it once, after detailed_status reaches completed.

Fetch failed-job log

Only meaningful when detailed_status == "failed":

bash
curl -fsS "$BASE_URL/api/challenge/job/$JOB_ID/log" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" | jq

Returns the newest failed case's EmuCaseExecLog:

json
{
  "id": 913,
  "job_id": 42,
  "task_id": 88,
  "case_id": 401,
  "run_instance_id": 7,
  "worker_id": 3,
  "status": 1,
  "failure_reason": 1,
  "result": { "stdout": "https://…presigned…" },
  "created_at": "…"
}
  • If the job has no failed case log, the endpoint errors (HTTP 500, body {"status":"error","error":"failed to get case exec log…"}). Check detailed_status first rather than probing.

  • status: 0 success, 1 failed, 2 running. This endpoint only serves 1.

  • failure_reason is the platform's own classification — read it before reading any log text:

    ValueMeaning
    0none
    1execution error (your code or the sim crashed)
    2submit error
    3timeout
    4terminated by signal
    5startup timeout — the pod never reported started
    6stale placement — the pod started then hung
    7manual reap by an operator
    8job already dead; this case was aborted as an in-flight sibling
    9tunnel lost — your agent disconnected and did not return within the 30 s reconnect window

    5/6/7/8 are platform-side teardowns, not your bug — for 8 in particular the real root cause is a different case, so don't debug this log. 1/3/4 point at your inference code or the case itself. 9 is usually on your side: the agent dropped the tunnel (process exit, OOM, or a long inference call starving the heartbeat). Read the sub-reason in result.error — only startup reconcile is platform-side; reconnect window expired and liveness probe mean your agent went away.

  • result is a free-form JSON map written by the worker. There is no guaranteed exit_code or stderr key — inspect what's actually there (jq '.result | keys') instead of assuming.

  • result.stdout is usually a URL, not the log text. When the worker archived stdout to object storage, the platform rewrites it into a 1-hour HTTP presigned link, so you must fetch it separately:

    bash
    LOG=$(curl -fsS "$BASE_URL/api/challenge/job/$JOB_ID/log" \
      -H "Authorization: Bearer $CHALLENGE_TOKEN")
    echo "$LOG" | jq '{failure_reason, status, case_id, run_instance_id}'
    URL=$(echo "$LOG" | jq -r '.result.stdout // empty')
    case "$URL" in
      http*) curl -fsS "$URL" | tail -100 ;;
      error) echo "platform failed to sign the stdout URL — report to organizers" ;;
      *)     echo "$LOG" | jq -r '.result' ;;
    esac

    The literal string "error" means presigning failed platform-side. Platform-generated logs (e.g. a manual reap) put a plain message under result.error instead.

When the log is large, don't dump everything. Surface the load-bearing tail:

  1. failure_reason and status (always include).
  2. The last Traceback (most recent call last): block, if present, in full — that's the actual failure.
  3. The last ~20 lines around it for context.

A common failure pattern: a Traceback in the observation-decode / handler path during a case that started with an empty frame is the warmup empty-frame bug — see challenge-troubleshoot row #11. It can surface as failed mid-job, not only as "stuck in WARMUP".

Episode videos (optional)

bash
curl -fsS "$BASE_URL/api/challenge/job/$JOB_ID/episodes/videos" \
  -H "Authorization: Bearer $CHALLENGE_TOKEN" | jq

Returns {job_id, status, expires_in, episodes:[{task_name, episode_uuid, task_id, case_id, score, head}]}. Here status is a video-availability code, unrelated to job status: 0 normal, 1 past the 72-hour retention window (head is an empty string), 2 evaluation didn't finish successfully. Links expire after expires_in seconds (8h).

Hand-off

  • completed → fetch /result, then suggest challenge-ranking to see where the score lands.
  • failed → pull /log, read failure_reason first, then challenge-troubleshoot.
  • pending / inference_disconnected for more than a few minutes → challenge-troubleshoot (no agent connected, or all dropped past the gateway's 30 s reconnect window).
  • queued → nothing is wrong; an agent is attached but no case is running yet (usually no free GPU). Keep agents online and wait.

Frequently asked questions

What does the Challenge Poll Result AI skill do?

Use to track a Simulation Challenge job's progress — list jobs, watch a job's status until terminal, fetch its per-task scores, or pull execution logs when it failed. Read-only; safe to run without confirmation.

Why use Challenge Poll Result on TypingMind?

Because you install it once and use it with any model. Challenge Poll Result 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 Poll Result 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-poll-result. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Challenge Poll Result?

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 Poll Result?

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

Is the Challenge Poll Result 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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