Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zhyx12/projtool/ml-research-workflownpx skills add zhyx12/projtool --skill ml-research-workflowgit clone --depth 1 https://github.com/zhyx12/projtoolWrote 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/zhyx12/projtool/ml-research-workflow)<a href="https://agentmods.dev/skills/zhyx12/projtool/ml-research-workflow"><img src="https://agentmods.dev/badge/skills/zhyx12/projtool/ml-research-workflow.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00206 | $0.12470 |
| Opus 5 | $0.00103 | $0.06235 |
| Sonnet 5 | $0.00041 | $0.02494 |
| Haiku 4.5 | $0.00021 | $0.01247 |
Grade A, and why
ml-research-workflow 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 4d 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 — 863 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML research workflow
projtool runs ML experiments across a local development machine and remote AutoDL containers. Three asset categories — code, outputs, reports — have different version-control needs and different storage backends. Your job is to keep them coherent without making the user do bookkeeping.
Hard policy: remote-first, watched, in the user's language
Remote-first (user ruling 2026-07-16). Experiment workloads —
training, evaluation, data preprocessing, dataset downloads — run on
the remote instance, never on the user's machine. The local machine is
for code editing, report writing, and light analysis (seconds of CPU,
no GPU, no bulk disk I/O). If the user explicitly asks for a local run,
warn about CPU/disk impact and run AT MOST ONE at a time. If remote
throughput is the bottleneck, scale remotely — start_runs(parallelism=N)
lanes or another slot — instead of spilling to the local box.
Do not self-ration to save money (user ruling: "我不缺钱"): renting
another instance or a bigger card is always preferable to queueing work
locally or serializing what could run in parallel. Cost forks (wait for
stock vs take a pricier spec) are the user's call — present the options;
don't silently pick the cheap one.
Long remote operations (>~2 minutes). Never run them as a blocking
MCP call. Anything that installs, downloads, trains, or converts for
minutes goes through run_script (tracked, cancellable, watchable);
remote_exec is for commands that finish in seconds. Two non-obvious
rules learned from incidents:
- A cancelled long call did NOT stop. Cancelling a blocking MCP call
only abandons the client side — the remote command keeps running and
may complete minutes later (2026-07-16: a cancelled 40-minute env
setup finished on its own and was recorded as a success). After any
cancel of a long projtool call, assume the remote side is still
executing: check state (
get_run_status, a shortremote_execprobe,diagnose) before retrying, or you will run the operation twice. - Re-launching after an interruption must be idempotent. After an
API stall, session restart, or a user "继续", call
list_run_statusesFIRST and reconcile against what is already running/exited. Blind re-launch duplicates runs and leaves orphans nobody finalizes (2026-07-17: two orphan runs from exactly this).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 863 lines · 206 tokens per session scan A d3c17605b4a8
ml-research-workflow is a skill published in the GitHub repository zhyx12/projtool (1 stars, last pushed 21d ago), licensed MIT. It adds 206 tokens to every session and 12,470 once invoked, about $0.0010 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 2026-08-31.
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