Trackio - Experiment Tracking for ML Training

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/hugging-face-trackio

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Trackio - Experiment Tracking for ML Training Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Three Interfaces, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Initialize tracking with trackio.init()
• Log metrics with trackio.log() or use TRL's report_to="trackio"
• Finalize with trackio.finish()
• trackio.alert(title="...", level=trackio.AlertLevel.WARN) — fire an alert
• Three severity levels: INFO, WARN, ERROR
• Alerts are printed to terminal, stored in the database, shown in the dashboard, and optionally sent to webhooks (Slack/Discord)

Operating Table

metadata.json

ORIGIN.md

references/alerts.md

references/logging_metrics.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Set up training with alerts — insert trackio.alert() calls for diagnostic conditions
2. Launch training — run the script in the background
3. Poll for alerts — use trackio list alerts --project <name> --json --since <timestamp> to check for new alerts
4. Read metrics — use trackio get metric ... to inspect specific values
5. Iterate — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: Minimal Logging Setup

import trackio

trackio.init(project="my-project", space_id="username/trackio")
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()

Minimal Retrieval

trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json

Imported: Autonomous ML Experiment Workflow

When running experiments autonomously as an LLM agent, the recommended workflow is:

1. Set up training with alerts — insert

   trackio.alert()

   calls for diagnostic conditions
2. Launch training — run the script in the background
3. Poll for alerts — use

   trackio list alerts --project <name> --json --since <timestamp>

   to check for new alerts
4. Read metrics — use

   trackio get metric ...

   to inspect specific values
5. Iterate — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run

import trackio

trackio.init(project="my-project", config={"lr": 1e-4})

for step in range(num_steps):
    loss = train_step()
    trackio.log({"loss": loss, "step": step})

    if step > 100 and loss > 5.0:
        trackio.alert(
            title="Loss divergence",
            text=f"Loss {loss:.4f} still high after {step} steps",
            level=trackio.AlertLevel.ERROR,
        )
    if step > 0 and abs(loss) < 1e-8:
        trackio.alert(
            title="Vanishing loss",
            text="Loss near zero — possible gradient collapse",
            level=trackio.AlertLevel.WARN,
        )

trackio.finish()

Then poll from a separate terminal/process:

trackio list alerts --project my-project --json --since "2025-01-01T00:00:00"

Imported: Three Interfaces

Examples

Example 1: Ask for the upstream workflow directly

Use @hugging-face-trackio-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @hugging-face-trackio-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @hugging-face-trackio-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @hugging-face-trackio-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/hugging-face-trackio

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @grafana-dashboards-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @graphql-architect-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @graphql-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @growth-engine-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/alerts.md

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

• alerts.md
• logging_metrics.md
• retrieving_metrics.md
• plugin.json
• alerts.md
• logging_metrics.md

Imported Reference Notes

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.