Mission control tensorboard observability
Use Mission Control to turn TensorBoard logs and training curves into explicit validation evidence.From its SKILL.md
npx -y skills add MN755/Codex-Mission_Control --skill mission-control-tensorboard-observabilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.4 KB, 262 tokens by cl100k_base, as published. Nobody here has run it
Mission Control TensorBoard Observability
Purpose
Use Mission Control to plan or review TensorBoard-backed observability so training claims have actual telemetry behind them.
The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager.
Use when
- The repo logs to TensorBoard.
- The user wants to understand training behavior, regressions, or tuning outcomes.
- Model quality claims need evidence.
Workflow
- Confirm where TensorBoard logs live.
- Ask Mission Control to read the training lane together with tests or export results.
- Summarize the useful signals, not every pixel in the curve set.
- Keep missing logs visible as a blocker instead of faking insight.
Mission Control calls
Tools:
mission_control_start_taskmission_control_get_statusmission_control_get_handoff_summary
Resources:
mission-control://projects/{project_id}/validation-summarymission-control://projects/{project_id}/handoff
Never do
- Do not describe training as healthy if there are no logs or metrics.
- Do not confuse pretty curves with product readiness.
Example invocation
Use Mission Control to review the TensorBoard evidence for this TensorFlow training run.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.