Nemo Mbridge Mlm Bridge Training
Run NVIDIA NeMo mBridge MLM bridge training workflows with an agent-guided procedure validated for correctness and safety before you publish or reuse the skill.
Overview
Nemo-mbridge-mlm-bridge-training is an agent skill for the Build phase that guides NVIDIA NeMo mBridge MLM bridge training workflows with NVSkills-Eval–verified agent behavior.
Install
npx skills add https://github.com/nvidia/skills --skill nemo-mbridge-mlm-bridge-trainingWhat is this skill?
- NVSkills-Eval external profile benchmark with PASS verdict documented
- 3-Tier evaluation across security, correctness, discoverability, effectiveness, and efficiency
- Benchmarked with claude-code and codex agents (2 attempts per task, 50% pass threshold)
- Designed for agents that must load the skill only when MLM bridge training is relevant
- NVSkills-Eval: 2 attempts per task, 50% pass threshold, overall PASS
- Agents benchmarked: claude-code and codex
Adoption & trust: 1 installs on skills.sh; 1.1k GitHub stars; trending (+100% hot-view momentum).
What problem does it solve?
You need to run NeMo mBridge MLM bridge training but lack a repeatable, agent-safe procedure agents can discover without hallucinating flags or skipping validation steps.
Who is it for?
Indie ML builders or small teams already on NVIDIA NeMo who delegate training setup to Claude Code or Codex and want an eval-backed skill.
Skip if: Builders with no GPU/NeMo environment who only need generic LLM API integration in a web app.
When should I use this skill?
When setting up or running NVIDIA NeMo mBridge MLM bridge training and the agent should follow the published NVSkills skill workflow.
What do I get? / Deliverables
After the skill runs, the agent follows the evaluated training workflow with documented pass criteria for security and correctness rather than improvised shell commands.
- Configured bridge training run per skill workflow
- Training artifacts or logs from the guided procedure
Recommended Skills
Journey fit
Training and bridge configuration is core product/backend ML work during build, not distribution or production monitoring. mBridge MLM bridge training targets model training pipelines and bridge setup rather than UI or go-to-market assets.
How it compares
Specialized NeMo training skill package—not a generic Hugging Face fine-tuning tutorial or an MCP server.
Common Questions / FAQ
Who is nemo-mbridge-mlm-bridge-training for?
Solo builders and small teams training with NVIDIA NeMo mBridge who use coding agents for repeatable MLM bridge training setup.
When should I use nemo-mbridge-mlm-bridge-training?
Use it in the build phase when you are configuring or launching mBridge MLM bridge training and want the agent to load NVIDIA’s procedural skill instead of improvising.
Is nemo-mbridge-mlm-bridge-training safe to install?
Evaluation docs report security checks in NVSkills-Eval; still review the Security Audits panel on this Prism page and your cluster secrets policy before agent-driven training runs.
SKILL.md
READMESKILL.md - Nemo Mbridge Mlm Bridge Training
# Evaluation Report Evaluation of the `nemo-mbridge-mlm-bridge-training` skill before publication through NVSkills-Eval. This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use. ## Evaluation Summary - Skill: `nemo-mbridge-mlm-bridge-training` - Evaluation date: 2026-06-02 - NVSkills-Eval profile: `external` - Environment: `local` - Dataset: 1 evaluation tasks - Attempts per task: 2 - Pass threshold: 50% - Overall verdict: PASS ## Agents Used - `claude-code` - `codex` ## Metrics Used Reported benchmark dimensions: - Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. - Correctness: checks whether the agent follows the expected workflow and produces the correct final output. - Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant. - Effectiveness: checks whether the agent performs measurably better with the skill than without it. - Efficiency: checks whether the agent uses fewer tokens and avoids redundant work. Underlying evaluation signals used in this run: - `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access. - `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow. - `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage. - `accuracy` (Accuracy): grades final-answer correctness against the reference answer. - `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully. - `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations. - `token_efficiency` (Token Efficiency): compares token usage with and without the skill. ## Test Tasks The benchmark dataset contained 1 evaluation tasks: - Positive tasks: 1 tasks where the skill was expected to activate. - Negative tasks: 0 tasks where no skill was expected. - Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred. Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases. ## Results | Dimension | Num | `claude-code` | `codex` | |---|---:|---:|---:| | Security | 2 | 100% (+0%) | 100% (+0%) | | Correctness | 2 | 100% (+0%) | 88% (+0%) | | Discoverability | 2 | 100% (+0%) | 62% (+0%) | | Effectiveness | 2 | 100% (+0%) | 100% (+0%) | | Efficiency | 2 | 93% (-0%) | 60% (-0%) | Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available. ## Tier 1: Static Validation Summary Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 12 total findings. Top findings: - MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.author' (`skills/nemo-mbridge-mlm-bridge-training/SKILL.md`) - MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (`skills/nemo-mbridge-mlm-bridge-training/SKILL.md`) - MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/nemo-mbridge-mlm-bridge-training/SKILL.md`) - MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/nemo-mbridge-mlm-bridge-training/SKILL.md`) - MEDIUM SCHEMA/author_missing: Author not specified in metadata (`skills/nemo-mbridge-mlm-bridge-training/SKILL.md`) ## Tier 2: Deduplication Summary Tier 2 validation passed. NVSkills-Eval ran 2 checks and found 0 total findings. Notable observations: - Context Deduplication: Collected 1 file(s) - Inter-Skill Deduplication