Tao List Capabilities

TAO Skill Bank Capabilities

Use this skill when the user asks what tao-skill-bank can do, asks for plugin capabilities, asks which application or data workflows are available, asks which models are supported, or asks what models are capable with AutoML.

Quick Start

Run scripts/list_tao_capabilities.py for general capability questions, or scripts/list_tao_models.py for model/action and AutoML support questions.

Capability Answers

For a general capabilities answer, run the packaged helper:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_capabilities.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text

Use the helper output as the source of truth for the answer instead of manually enumerating capabilities from this skill or plugin metadata. Include:

• Every top-level application workflow under applications/ and what it can do.
• Every top-level data workflow under data/ and what it can do.
• Supported execution platforms from scripts/list_tao_platforms.py.
• The fine-tuning/deployment workflow coverage for models under models/: train,

evaluate, inference, export, and TensorRT engine generation when those actions are present in the packaged schema manifest.

• AutoML support and the AutoML train-schema gate.

Model Lists

When the user asks which TAO models are available or which actions a model can run, use the packaged model-list script instead of manually scanning model folders:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_models.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --scope all --format text

The model list comes from skills/models/schemas.manifest.json.

AutoML Lists

When the user asks what models are capable with AutoML, use the same model-list script in AutoML mode, or the compatibility wrapper:

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_models.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --scope automl --format text

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_automl_support.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text

AutoML support requires skills/models/<network>/schemas/train.schema.json to be packaged with the plugin and parse successfully as JSON. If that dataclass schema is missing or invalid, do not describe the model as AutoML-supported.

Evaluation Report

Evaluation of the tao-list-capabilities 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: tao-list-capabilities
• Evaluation date: 2026-06-22
• NVSkills-Eval profile: external
• Environment: astra-sandbox
• Dataset: 1 evaluation tasks
• Attempts per task: 1
• 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

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 1 checks and found 4 total findings.

Top findings:

• MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (skills/core/tao-list-capabilities)
• MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/core/tao-list-capabilities/SKILL.md)
• MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/core/tao-list-capabilities/SKILL.md)
• LOW SCHEMA/author_format: Author must be of the form 'Name <email@host>' (skills/core/tao-list-capabilities/SKILL.md)

Tier 2: Deduplication Summary

This tier was not run or did not produce findings in this report.

Publication Recommendation

The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.

Description: <br>

Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers use this skill to discover which TAO training, evaluation, inference, and AutoML workflows are available in the TAO Skill Bank plugin. <br>

Deployment Geography for Use: <br>

Global <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

• Agent Skills Open Standard <br>
• BENCHMARK.md <br>

Skill Output: <br>

Output Type(s): [Analysis, Shell commands] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

• Claude Code (claude-code) <br>
• Codex (codex) <br>

Evaluation Tasks: <br>

Evaluated against 1 evaluation task in the NVSkills-Eval external profile. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

• Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>
• Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>
• Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>
• Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>
• Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>

Underlying evaluation signals used in this run: <br>

• security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
• skill_execution: Verifies that the agent loaded the expected skill and workflow. <br>
• skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage. <br>
• accuracy: Grades final-answer correctness against the reference answer. <br>
• goal_accuracy: Checks whether the overall user task completed successfully. <br>
• behavior_check: Verifies expected behavior steps, including safety expectations. <br>
• token_efficiency: Compares token usage with and without the skill. <br>

Evaluation Results: <br>

Skill Version(s): <br>

0.1.0 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>