- name
- swanlog
- description
- Pull a SwanLab cloud experiment (metrics + config + metadata + requirements) down to a local directory in one shot. Use this whenever the user wants to inspect, sync, archive, diff, or analyze a remote SwanLab run from the laptop — including phrases like "拉一下 swanlab", "看下最新实验", "fetch SwanLab logs", "sync experiment from cloud", "I can't ssh into the GPU box, just grab the run", or any time a user references a SwanLab experiment ID/URL and needs the data on disk. Trigger this skill aggressively for any read-side SwanLab interaction; do NOT trigger when the user is starting a new training run, editing configs, troubleshooting login itself, or asking generic ML/SwanLab questions that don't require pulling data to disk.
swanlog
Fetch a SwanLab cloud experiment to a local directory and brief the user on what came back. The skill is a thin wrapper around scripts/fetch_swanlog.py, which handles auth, API calls, profile extraction, and produces a ready-to-read brief.md. Your job is to invoke it correctly and translate that brief into a short message for the user.
How to invoke
The script lives next to this SKILL.md at scripts/fetch_swanlog.py. Use the absolute path you were given when this skill was activated — that's the directory containing this file; tack on /scripts/fetch_swanlog.py.
Default form (no args from user → fetch latest):
python <skill-dir>/scripts/fetch_swanlog.py --latest --project <project>If the user passes --exp-id <id>, forward it instead of --latest (they're mutually exclusive). If the user wants a custom output location, pass -o <dir>; otherwise the script writes to ./swanlog_<YYYY-MM-DD_HH-MM-SS>/ in the current working directory.
The script auto-enumerates every scalar metric the run actually logged and pulls all of them — the user does not need to know what was logged in advance. Only pass --keys "k1,k2,..." if the user explicitly wants a subset (e.g. "I just want train and val loss"). If auto-enumeration fails (SwanLab API drift, auth issue), the script falls back to a generic key list and prints a warning; surface that warning, but the dump itself still succeeds.
If the user hasn't named a project and SWANLAB_PROJECT isn't set, ask once: *"Which SwanLab project?"*
Workflow
- Run the script with
Bash, capturing full stdout/stderr.
- If the script exited non-zero, show the last 10 lines of stderr verbatim, then stop. Common signals and the right hint:
- ApiError mentioning auth / 401 / netrc → tell the user to run swanlab login - ModuleNotFoundError: swanlab (or pandas / omegaconf) → pip install swanlab>=0.7.15 pandas omegaconf - RuntimeError: No experiments in ... → user named the wrong project, or hasn't pushed any run yet - 404 on the experiment ID → ID typo or experiment was deleted - Network errors → relay verbatim, don't speculate
- On success, read
<output_dir>/brief.md— the script writes a fixed-format Markdown brief containing run metadata, the latest non-NaN value of every scalar with its step, and the dump's row × col count. Don't reinvent this withawk/ one-liners overmetrics.csv; the brief is the contract. Ifbrief.mdis missing or empty, that's a bug in the script — surface it.
- Tell the user what's in
brief.md, in their language (mirror Chinese if they wrote Chinese). Keep it tight — one heading for the run, one short list / table for the latest scalars, end with the absolute path of the output directory so they cancdoropenit. You don't need to dump every scalar if there are many; pick the ones the user is likely to care about (typically atrain/loss*,epoch/avg_loss,val/*, plus anything they named earlier in the conversation).
Idempotency
Re-running the script for the same run (same --exp-id, or --latest resolving to the same run) overwrites files in the matching swanlog_<timestamp>/ directory rather than accumulating duplicates. The timestamp comes from run.created_at, not the wall clock at fetch time, so the directory name is stable across re-fetches. Safe to call repeatedly to refresh metrics on a still-running experiment.
Boundaries
- This skill only reads from SwanLab. It never starts training, modifies configs, deletes runs, or pushes anything to the cloud.
- It does not assume any particular ML framework, dataset, or directory layout — it works against any SwanLab project.
metrics.csvonly contains scalar columns (FLOAT / INTEGER). IMAGE / AUDIO / TEXT artifacts are skipped — they don't fit a flat CSV. If the user needs those, point them to the WebUI URL from the brief.