OpenClaw
Task Router Skill
Task Router
ml-experiment-tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
Installation
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install 0x-professor/ml-experiment-trackerExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Instructions
ML Experiment Tracker
Overview
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
Workflow
- Define dataset, target task, model family, and parameter search space.
- Define metrics and acceptance thresholds before training.
- Produce run plan with version and artifact expectations.
- Export the run plan for execution in tracking tools.
Use Bundled Resources
- Run
scripts/build_experiment_plan.pyto generate consistent run plans. - Read
references/tracking-guide.mdfor reproducibility checklist.
Guardrails
- Keep inputs explicit and machine-readable.
- Always include metrics and baseline criteria.
Informations sur la source
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/ml-experiment-tracker
- Version
- —
- Commit
- e35e314163a86a0c680a4b67047a4af977e56078
- Source de classification
- rules
- Statut
- active
- Visibilité
- public
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Contenu extrait
SKILL.md
# ML Experiment Tracker ## Overview Generate structured experiment plans that can be logged consistently in experiment tracking systems. ## Workflow 1. Define dataset, target task, model family, and parameter search space. 2. Define metrics and acceptance thresholds before training. 3. Produce run plan with version and artifact expectations. 4. Export the run plan for execution in tracking tools. ## Use Bundled Resources - Run `scripts/build_experiment_plan.py` to generate consistent run plans. - Read `references/tracking-guide.md` for reproducibility checklist. ## Guardrails - Keep inputs explicit and machine-readable. - Always include metrics and baseline criteria.
Skills associés
OpenClaw
x402hub
Register, communicate, and earn on the x402hub AI agent marketplace. Use when an agent needs to register on x402hub, browse or claim bounties, submit deliverables, send messages to other agents via x402 Relay,...
OpenClaw
claw
Real-time event bus for AI agents. Publish, subscribe, and share live signals across a network of agents with Unix-style simplicity.
OpenClaw
captchas-openclaw
OpenClaw integration guidance for CAPTCHAS Agent API, including OpenResponses tool schemas and plugin tool registration.