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Agent skill

Monitor Cron Sweep Iris

by open-thoughts in open-thoughts/OpenThoughts-Agent

The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only.

Apache-2.0Auto-check passedProductivity & Automation

Install Monitor Cron Sweep Iris

skills CLI
$ npx skills add open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep-iris -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install open-thoughts/OpenThoughts-Agent monitor-cron-sweep-iris --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/monitor-cron-sweep-iris .claude/skills/monitor-cron-sweep-iris && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
monitor-cron-sweep-iris
GitHub stars
301
Token cost
~3.3k tokens
SKILL.md length
1,527 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
Apache-2.0

At a glance

The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only.

  • Works in 6 steps: Query both clusters (active +… → Metrics for each ACTIVE marin… → Report + classify (## Iris jobs status — ) → …
  • Run an iris sweep / cluster sweep now
  • SKILL.md covers 1. Query both clusters (active…, 2. Metrics for each ACTIVE…, 3. Report + classify (## Iris… and 4. AUTO-RESCUE — DATAGEN ONLY…, plus 3 more sections
  • Calls python and gsutil; needs DAYTONA_API_KEY

What it does

Monitor Cron Sweep Iris is an agent skill from open-thoughts/OpenThoughts-Agent. The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only. Query both clusters, run the harbor analyzer on each active TPU datagen/eval job, classify every job (datagen / eval / other / GPU-RL) and apply its treatment, then take the standing DATAGEN-ONLY autonomous actions (auto-rescue, keep-2-in-flight). This is the methodology the recurring cron prompt runs; the cron itself is (re)installed…

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Scheduled and recurring tasks. The repository describes itself as: Data recipes and robust infrastructure for training AI agents. The licence is Apache-2.0.

When your agent uses it

  • Run an iris sweep / cluster sweep now
  • As the reference behind each cron tick

Example prompts

  • “run an iris sweep / cluster sweep now”
  • “/monitor-cron-sweep-iris”

Requirements

  • Python 3
  • A credential in DAYTONA_API_KEY

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Query both clusters (active + recently-terminal)
  2. Metrics for each ACTIVE marin datagen/eval job → use analyze-job-history-iris
  3. Report + classify (## Iris jobs status — )
  4. AUTO-RESCUE — DATAGEN ONLY (autonomous; overrides read-only)
  5. KEEP TWO DATAGEN IN-FLIGHT (datagen only)
  6. No-kill guardrail

What it can do on your machine

Read from SKILL.md and the folder at commit 3bd1917. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • gsutil

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DAYTONA_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Monitor Cron Sweep Iris loads about 3.3k tokens when it runs. Until then it costs about 164 tokens; SKILL.md has 1,527 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from open-thoughts/OpenThoughts-Agent at commit 3bd1917, republished under its Apache-2.0 licence (© open-thoughts). 1,527 words, ~3,274 tokens.

Download SKILL.mdSave it as .claude/skills/monitor-cron-sweep-iris/SKILL.md (or your agent's skills folder).
name
monitor-cron-sweep-iris
description
The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only. Query both clusters, run the harbor analyzer on each active TPU datagen/eval job, classify every job (datagen / eval / other / GPU-RL) and apply its treatment, then take the standing DATAGEN-ONLY autonomous actions (auto-rescue, keep-2-in-flight). This is the methodology the recurring cron prompt runs; the cron itself is (re)installed via monitor-restore-iris. Use for "run an iris sweep / cluster sweep now" or as the reference behind each cron tick.

monitor-cron-sweep-iris

The per-tick procedure for the lightweight Iris monitor: the marin TPU datagen/eval pipeline (the autonomous-action surface) plus CoreWeave (cw-us-east-02a) GPU-RL as monitor-only. Distinct from the broader tri-cluster monitor-cron-sweep (Leonardo + CoreWeave + TACC). The recurring cron that fires this is installed/restored by monitor-restore-iris (holds the verbatim cron prompt); this skill is the methodology behind it and can be run ad-hoc.

Iris orientation — read first: the Iris tools catalog (.agents/ops/iris/ops.md) + ops directory (.agents/ops/iris/ — CoreWeave GPU in ops.md, TPU marin in ops.md) carry the binding access/preamble/gotchas + helper-script inventory the steps below rely on.

Autonomous WRITE actions are datagen-only (§4 rescue, §5 keep-2). Eval + GPU-RL + everything else are monitor-only. Never kill/restart a RUNNING job without express permission — the ONE exception is a confirmed zombie DATAGEN job (§4b).

1. Query both clusters (active + recently-terminal)

  • marin (TPU): /Users/benjaminfeuer/Documents/marin/.venv/bin/iris --cluster=marin query "SELECT job_id, state FROM jobs WHERE state IN (1,2,3) AND job_id LIKE '/benjaminfeuer/%' ORDER BY job_id DESC LIMIT 20" -f csv
  • cw-us-east-02a (CoreWeave GPU) — the KUBECONFIG prefix is REQUIRED (else iris uses the shell-default kubeconfig and errors): KUBECONFIG=~/.kube/coreweave-iris-gpu /Users/benjaminfeuer/Documents/marin/.venv/bin/iris --cluster=cw-us-east-02a query "… same …" -f csv
  • For EACH cluster also query state IN (4,5,6) LIMIT 8 to catch jobs gone terminal since the last tick.
  • If the cw query errors (cluster down / creds), report it and continue with marin — don't fail the whole tick.
  • States: 1=PENDING 2=starting 3=RUNNING 4=SUCCEEDED 5=FAILED 6=KILLED.

2. Metrics for each ACTIVE marin datagen/eval job → use analyze-job-history-iris

Run the harbor analyzer via the analyze-job-history-iris skill — do NOT eyeball a --tail. That skill is SLOW (paginates the full history, minutes per job — run foreground with a long timeout and WAIT; offload to a patient subagent for big 16k/46k-task jobs). Report from the JSON sidecar: runtime_h, iris_preemption_count, cycles total/served, gen tok/s mean/peak (n), Running mean/peak, non_empty/total trials = productive rate, t_first_serve, top harbor_exception_stats — PLUS two log-only fields that skill extracts (NOT in the sidecar): mean reward and completed/total tasks (iris … job logs | grep -aoE '[0-9]+/[0-9]+ Mean: [-0-9.]+'). The analyzer is harbor-shaped; it does NOT apply to CoreWeave GPU-RL (see class D).

3. Report + classify (## Iris jobs status — <ISO UTC>)

Table formatting → monitor-job-tables (the authority): box-drawing tables (┌─┬─┐, NOT markdown), bucketed by type (RL · SFT · Datagen · Eval · Catch-all), with mandatory metric columns, signal thresholds, and benign-noise-vs-real-fault rules per bucket.

One line per job (name + state + CLOSED/PARTIAL/OPEN/DEAD) + a compact metrics block + a survival check (past cold compile? throughput sane vs S1 baseline gen mean400/peak1115? traces/results landing on HF?). Classify by job_id prefix:

  • A. Datagen (qwen3.5-122b-32k-%): HF repo penfever/<slug>-qwen3.5-122b-32k-traces; image ae085bc8+ auto-uploads on state-4 — verify the repo self-created before rescuing. Short-task datasets run lower gen tok/s — judge by productive trial rate. Watch the heavy-dataset OOM-fix + stuck-PENDING (unpinned relaunch). §4-5 standing actions apply to datagen ONLY.
  • B. Eval (eval-%): auto-syncs to Supabase + HF on completion (--upload_to_database); NO rescue, NO keep-2, NEVER auto-relaunch. ALWAYS report the leading metric — the <done>/<total> Mean: <X> harbor progress line from iris --cluster=marin job logs <job_id> — plus productive rate + harness exceptions; on a terminal job, whether results landed. (See eval-agentic-launch-iris.)
  • C. Other (matches none of A/B/D/E — e.g. serve-% inference jobs): report state + a one-line health read; no autonomous write action.
  • E. Executor/Levanter training (a marin-executor training run — a CPU coordinator <run>-coord PLUS its nested v5p training child <run>-coord/checkpoints-<step>-<hash>; e.g. delphi-%/iris-run-midtrain_*): monitor-only — NO rescue, NO keep-2, NO auto-relaunch, NEVER kill. The §2 harbor analyzer does NOT apply (no harbor trial sidecars, like GPU-RL). Run analyze-training-run-iris on the CHILD job and report its compact line: step=<cur>/<total> (X%) loss=<L> ~<T>tok/s preempts=<P> gaps=<G>/<H>h ckpt=step-<C> + a health read (past setup/compile? step rate sane vs last tick? loss finite & trending down? preemptions resuming cleanly — checkpoint advancing? ETA to the K-budget target). That skill reads W&B per-step history (nyu-dice-lab/delphi-midtraining, run = the GCS output-path hash) + iris job summary (preemptions) + GCS step-* checkpoints; empty W&B history = pre-first-step (still HF-download/XLA-compile), not a gap.
  • D. GPU-RL (CoreWeave, rl-% / rl-iris-% / MarinSkyRL GRPO on H100×8, possibly multi-node replicas>1): monitor-only — NO rescue, NO keep-2, NO auto-relaunch, NEVER kill/relaunch. The §2 analyzer does NOT apply (no harbor trial sidecars). Report state + latest RL progress from the persistent finelog (pods GC on terminal): KUBECONFIG=~/.kube/coreweave-iris-gpu iris --cluster=cw-us-east-02a job logs <job_id> --max-lines 100000 --no-tail then grep WANDB_MIRROR kind=train step= for the latest trainer/global_step, loss/avg_raw_reward, generate/num_failed_trajectories/errors; for multi-node confirm All N Ray node(s) joined. On terminal, report exit state. For a NEW/untested RL run, deep-probe via rl-job-health-deep-dive (KILL/NO-KILL). (Finelog retention is finite — report what survives.)
Show full SKILL.md (809 more words)Show less

4. AUTO-RESCUE — DATAGEN ONLY (autonomous; overrides read-only)

  • 4a. TERMINAL rescue: a datagen job terminal (4/5/6) with productive GCS trials that did NOT auto-upload (HF repo missing/stale) → rescue automatically, no need to ask.
  • 4b. ZOMBIE kill-then-rescue (datagen only): state 3 AND harbor frozen ≥3h (harbor_updated_at stale) AND the task log shows ONLY [fd-monitor] heartbeats in that window (no vLLM/harbor/trial activity) → confirmed zombie: iris --cluster=marin job stop <job>, then rescue per 4a. The fd-monitor-ONLY clause is the safety gate (a healthy cold-compiling/preempt-recompiling job emits XLA/vLLM logs, so it won't match). Unsure wedge vs slow long-task cycle → do NOT kill; report and ask. Before killing, ALSO rule out the §4c resume-scan (a healthy vLLM engine ⇒ NOT a zombie).
  • 4c. RESUME-SCAN check (report, NEVER kill) — the benign twin of §4b: a datagen job can match §4b's signals (state 3, harbor frozen, fd-monitor-only) yet be healthy — it's mid harbor GCS-jobs_dir resume after a preempt, emitting only [fd-monitor] while the vLLM engine is already up. DISCRIMINATOR: a recent engine-ready marker with NO serving yet ⇒ resume-scan, not zombie. Detect + report per active datagen job:
    1. engine-ready ts: iris --cluster=marin job logs <job> --max-lines 20000 | grep -aE "Application startup complete|Starting vLLM API server" | tail -1
    2. progress advancing? compare the harbor <done>/<total> Mean: line across the tick (or harbor_updated_at from the §2 sidecar). Frozen + recent-tail fd-monitor-only (iris ... job logs --max-lines 300 | grep -aoE 'fd-monitor|Mean:|serving|vllm' | sort | uniq -c) ⇒ in a resume scan. Report it explicitly: RESUME-SCAN ~<Nh> (engine up @<T>, harbor idle, <done>/<total> trials) — this is EXPECTED on pre-fix builds and self-resolves; do NOT kill and do NOT count it as a stall. The durable fix is the harbor O(existing) resume speedup (harbor 7010e48c, in the :tpu image rebuilt 2026-07-02) — jobs on the current image resume in minutes; surface "relaunch on the current image to eliminate the resume tax" for a job that keeps paying it. ESCALATE (report as an OUTLIER, still NO auto-kill) only if the resume exceeds ~8h with a still-healthy engine (beyond the observed max); if there is NO healthy engine, it is the §4b zombie path.
  • Rescue mechanics (both): resolve the job's RECORDED output prefix — never hardcode a bucket (jobs now pin to single-region gs://marin-<region>, older jobs to the multi-region mirror): OUT=$(…/otagent/bin/python -m hpc.iris.job_output_resolver "$JOB" --cluster …/marin.yaml) — then gsutil rsync the OUTER $OUT/ (.../ot-agent/<job>/) → /tmp/<job>_traces (the OUTER <job>/ — carries the trial dirs AND the sibling logs/<slug>_literal.jsonl; do NOT rsync only the inner <job>/<job>/, which drops logs/ and silently loses the literals for a --record_literal job), then make_and_upload_trace_dataset.py --job_dir /tmp/<job>_traces --repo_id penfever/<slug>-qwen3.5-122b-32k-traces --episodes last --filter none --skip_register (source $DC_AGENT_SECRET_ENV first). Literals AUTO-INCLUDE when a logs/*_literal.jsonl is present (--no_literal_tokens to force text-only; FAILS LOUD on a present-literal / 0-bind). Report row count AND the Literal yield: X/Y line (X>0 for a --record_literal job); update the tracker. (Full launch/rescue detail: datagen-launch-iris.)

5. KEEP TWO DATAGEN IN-FLIGHT (datagen only)

If active datagen (qwen3.5-122b-32k-%, state 1/2/3) < 2, auto-launch the next pending dataset from /Users/benjaminfeuer/Documents/experiments/active/datagen/qwen3.5-122b-tt/tracker.md via the datagen launch template (S1, ctx32k_verified.yaml, --tpu v5p-8 --preemptible; omit --gcs-output-dir so the launcher auto-pins a co-located single-region output bucket for the chosen region — passing it explicitly opts OUT of the region pin and forces that exact bucket, so only pass it to deliberately override; repo penfever/<slug>-qwen3.5-122b-32k-traces) — see datagen-launch-iris. Flip its tracker row to RUNNING. Eval jobs do NOT count toward the 2 and are never auto-launched.

Snapshot-cap hygiene — PROACTIVE every tick (2026-07-10 operator directive): every tick, unconditionally reclaim idle harbor__ snapshots on the cli-org (DAYTONA_API_KEY) via daytona_snapshot_manager.py --api-key-env DAYTONA_API_KEY --delete-stale --yes (run from the OT-Agent dir, secrets sourced) — at the stale threshold defined in .agents/projects/daytona/daytona.md § "How to clean stale snapshots" (GT — don't restate the value). Do NOT wait for a refill to be blocked or the org to hit ~58/60; keep the org clean by default. Report before/after count. This deletes ONLY idle harbor__ env snapshots (rebuilt on demand by harbor auto_snapshot) — the --name-prefix harbor__ default guards the shared base images (daytonaio/sandbox:*, daytona-*, windows-*), which must never be deleted, and the idle threshold never hits an ACTIVE-recent snapshot in use by a running job. Full procedure: utils-reclaim-stale-snapshots.

6. No-kill guardrail

Never kill/restart/bounce a RUNNING job or the cluster without express permission — the ONLY exception is the §4b confirmed-zombie DATAGEN kill. Autonomous write actions = datagen rescue (incl. §4b) + datagen refill. GPU-RL and everything else stay strictly no-touch (flag, never kill). Stuck PENDING (no capacity) → report + surface the unpinned-relaunch option; don't kill a placed job unprompted.

  • monitor-job-tables — the §3 report FORMATTING authority.
  • monitor-restore-iris — (re)installs the recurring 3-hour cron that runs this procedure (holds the verbatim prompt).
  • analyze-job-history-iris — the §2 analyzer recipe for class A/B datagen+eval (foreground-and-wait; mean-reward + completed/total extraction).
  • analyze-training-run-iris — the class-E recipe for executor/Levanter training runs (step progress + major-gap detection via W&B + iris job summary + GCS checkpoints).
  • datagen-launch-iris — launch / rescue / snapshot-cleanup mechanics for §4–§5.
  • rl-job-health-deep-dive — deep per-RL-job KILL/NO-KILL probe for class-D GPU-RL in new/untested settings.
  • monitor-cron-sweep / monitor-restore — the separate broader tri-cluster (Leonardo+CoreWeave+TACC) campaign.

© open-thoughts, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/monitor-cron-sweep-iris of open-thoughts/OpenThoughts-Agent.

Open the folder on GitHubat commit 3bd1917

Compare with similar skills

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Categories

Questions about Monitor Cron Sweep Iris

What does Monitor Cron Sweep Iris do?

The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only. Monitor Cron Sweep Iris is an agent skill from open-thoughts/OpenThoughts-Agent. The PROCEDURE for one every-3-hours Iris job-status sweep — primarily the marin TPU datagen/eval jobs ("iris" here = the marin TPU cluster), plus CoreWeave GPU-RL as monitor-only.

When should I use Monitor Cron Sweep Iris?

Monitor Cron Sweep Iris fits situations like: run an iris sweep / cluster sweep now; as the reference behind each cron tick.

How do I install Monitor Cron Sweep Iris in Claude Code?

Run `npx skills add open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep-iris -a claude-code`. Or copy the skill folder (.agents/skills/monitor-cron-sweep-iris in open-thoughts/OpenThoughts-Agent) into .claude/skills/monitor-cron-sweep-iris in your project. Claude Code loads it when a task matches its description.

How do I install Monitor Cron Sweep Iris in Codex?

Run `npx skills add open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep-iris -a codex`. Or copy the skill folder (.agents/skills/monitor-cron-sweep-iris in open-thoughts/OpenThoughts-Agent) into .agents/skills/monitor-cron-sweep-iris in your project. Codex loads it when a task matches its description.

Can I use Monitor Cron Sweep Iris in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep-iris -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/monitor-cron-sweep-iris, .gemini/skills/monitor-cron-sweep-iris, .github/skills/monitor-cron-sweep-iris and .opencode/skills/monitor-cron-sweep-iris in your project.

What does Monitor Cron Sweep Iris need to run?

Going by SKILL.md and its folder, Monitor Cron Sweep Iris needs the command-line tools its instructions call (python and gsutil) and credentials named DAYTONA_API_KEY. Our summary lists: Python 3; A credential in DAYTONA_API_KEY.

Does Monitor Cron Sweep Iris access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Monitor Cron Sweep Iris safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Monitor Cron Sweep Iris use?

Monitor Cron Sweep Iris is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Monitor Cron Sweep Iris use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Monitor Cron Sweep Iris?

Skills that share tags, products or a category with Monitor Cron Sweep Iris: Schedule (TinyAGI/tinyagi, 3.6k stars), Send User Message (TinyAGI/tinyagi, 3.6k stars), Cron Ops (czl9707/build-your-own-openclaw, 1.9k stars) and X Bookmarks (sharbelxyz/x-bookmarks, 289 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Monitor Cron Sweep Iris?

open-thoughts (a GitHub organization) maintains it in open-thoughts/OpenThoughts-Agent, which has 301 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on September 28, 2026.

Source: open-thoughts/OpenThoughts-Agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.