Borrowing it
Nothing to install: this file belongs to vlasenkoalexey/tpu_performance_autoresearch_wiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vlasenkoalexey/tpu_performance_autoresearch_wiki/main/.claude/skills/start-experiment/SKILL.mdgit clone --depth 1 https://github.com/vlasenkoalexey/tpu_performance_autoresearch_wikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/start-experiment)<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/start-experiment"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/start-experiment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/start-experiment"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/start-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 167 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Tool Misuse · line 272 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 235 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 549 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.09932 |
| Opus 5 | $0.00051 | $0.04966 |
| Sonnet 5 | $0.00021 | $0.01986 |
| Haiku 4.5 | $0.00010 | $0.00993 |
Grade A, and why
start-experiment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 662 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are starting one autoresearch session. Follow this sequence precisely. Do not skip steps.
Step 1 — Determine context (model, lane, parallelism)
KERNEL FAST-PATH — check this FIRST. If the invocation names a kernel (a kernel/benchmark path, a wiki/kernels/ family slug, or "tpu chip N"), this is a KERNEL session. Steps 2–8 are model-lane machinery — skip them ALL (no XPK cluster discovery, no xprof probe, no model-lane hardware question). The kernel lane asks its own, much smaller run-target question at Step 9·K below. Do exactly:
- Read
wiki/kernel_experiments/program.mdend-to-end; derive the family slug per its Quick-start rule 1. - Step 9·K — choose the run target (ASK ONCE, then it is fixed for the whole run). See below.
- Step 9·0 — arm the watcher (kernel mode:
family+home_repofrom the family binding; bootstrap the binding first if new, per Quick-start rule 2). - Step 9b — start marker, into the FAMILY's log:
wiki/kernel_experiments/<slug>/pallas/log.md. Record the run target chosen at 9·K. - Hand off: run the kernel Quick start (load
/author-kernel, then K0–K9 viakexec.sh run).
Step 9·K — Run target: local chip or cluster pod (KERNEL ONLY)
The chip named in the prompt is the local default. Before arming anything, ask the user with AskUserQuestion (--yes skips the question and takes the local default — same convention as Step 8):
Question: "Run this kernel family on the local chip, or on a cluster pod?" Options:
Local chip <N>(recommended) — fastest edit→measure cycle; right for authoring.Cluster pod— persistent GKE pod,kubectl exec. For a different target generation or more chips than local. Costs contended capacity for the whole run.
(When to pick which — the full rationale — is canonical in wiki/kernel_experiments/program.md's run-target section; don't restate it here.)
If Cluster pod:
tools/kernel_exec/kexec.sh discover— prints the TPU capacity actually present (nodepool, accelerator, topology, machine type, chips). Never assume a generation; the GKE accelerator label value is generation-specific (tpu7x,tpu-v6e-slice,tpu-v5p-slice,tpu-v5-lite-podslice, …).- Ask which row to use, and for how long (
--hours, default 8 — the pod's hard TTL). - Bring it up — it stays up for the whole run:
tools/kernel_exec/kexec.sh up --family <slug> \ --accelerator <A> --topology <T> [--chips N] --hours <H> --image <IMG>--imagemust havejax[tpu]+ libtpu + kgate.--chipsdefaults to the node's allocatable count. kexec.sh sync --family <slug>after each K4 authoring pass, so the pod sees the current.repo.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed c73bbaa39e48
- 11d ago First seen · 662 lines · 103 tokens per session scan A 96f70bc1c648
start-experiment is a skill published in the GitHub repository vlasenkoalexey/tpu_performance_autoresearch_wiki (55 stars, last pushed 7d ago), licensed MIT. It adds 103 tokens to every session and 9,932 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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