Experiment StatusSAFE
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
Overview
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
d8030f4516acOBSERVED · 2026-10-12Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned | |
| codex | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: experiment-status description: "Check status of running autonomous experiment loops" --- # experiment-status Check the current status of your autonomous experiment agent. ## Usage ``` Claude Code: /experiment-status Claude Code: /experiment-status --project /path/to/project Codex: $experiment-status ``` ## Behavior 1. Read `PROJECT_BRIEF.md` — show the research goal 2. Read `MEMORY_LOG.md` — show key results and recent decisions 3. Read `.cycle_counter` — show how many cycles completed 4. Check for running training processes via the configured execution backend 5. If training is running, tail the log file for latest output 6. Show GPU utilization through the configured backend 7. Check if `HUMAN_DIRECTIVE.md` exists (pending directive) If `execution.mode=ssh`, controller state still comes from the local project directory, but PID checks, training logs, and GPU status come from the configured remote host. ## Output Format ```markdown # Experiment Status — my-project ## Goal Train ViT-B/16 on ImageNet to 78%+ accuracy ## Progress - Cycles completed: 4 - Current best: 78.3% (Exp004, ViT-B/16 + cosine + mixup) - Status: TRAINING (PID 12345, GPU 0, running 3.2h) ## Latest Training Log Epoch 45/90 | loss: 2.134 | acc: 77.1% | lr: 1.2e-4 ## Recent Decisions 1. [04-08 14:45] Target reached with mixup, trying stronger augmentation 2. [04-08 06:00] Cosine schedule helped, adding regularization ## Pending Directive None (drop a file at workspace/HUMAN_DIRECTIVE.md to intervene) ```
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
d8030f4516acfull audit observations/trust-audit/skill/xiangyue-zhang__experiment-status.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-12 | d8030f4516ac | SAFE | B | 89 | first audit |
Questions
What does the Experiment Status skill do?
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
Is Experiment Status safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Experiment Status access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Experiment Status work with?
Its documentation mentions claude-code and codex. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (d8030f4516ac), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.