Tao List Capabilities
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-list-capabilities skill provides a standardized method for querying the contents and features of the TAO Skill Bank. By leveraging packaged manifests and helper scripts, it allows users to programmatically discover supported models, available data and application workflows, execution platforms, and AutoML support. It ensures that information provided to the user is accurate and derived directly from the current environment's configuration rather than static documentation.
What it does
- Lists all available application and data workflows within the TAO Skill Bank.
- Identifies supported models and their specific action capabilities like training, evaluation, and inference.
- Reports on AutoML support for specific models and compression actions.
- Discovers and lists compatible execution platforms.
- Uses internal manifests to ensure accurate reporting of model and action schemas.
When to use it
- When a user asks for a general overview of what the TAO Skill Bank can do.
- When a user needs to know which specific AI models are available for use.
- When a user wants to verify if a specific model supports AutoML or compression actions.
- When a user asks about available workflows for data processing or application development.
Advantages
- Provides a single source of truth by reading directly from packaged manifests.
- Reduces manual effort in scanning directories or documentation for capability discovery.
- Ensures compatibility with the current environment's installed skills.
- Supports granular queries for specific actions like pruning, distillation, or quantization.
Usage examples
- “What can the TAO Skill Bank do?”
- The agent executes the capability script to return a comprehensive list of available application workflows, data processing tasks, and supported execution platforms.
- “Which TAO models support AutoML?”
- The agent runs the AutoML support script to filter and display only those models that have valid AutoML schema definitions.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-list-capabilities/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-list-capabilities.zip -d .agents/skills
Documentation by NVIDIA Corporation
TAO Skill Bank Capabilities
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
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-skill-bank}/scripts/list_tao_capabilities.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --format textUse 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, discovered from the installed platform skills (tao-run-on-docker / -slurm / -kubernetes / -brev, plus any external one); on a runtime that surfaces only the core router skills, read
skills/platform/tao-run-on-*/SKILL.mdfrontmatter.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 per-action 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-skill-bank}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --scope all --format textThe 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-skill-bank}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --scope automl --format text${TAO_SKILL_BANK_PATH:-~/tao-skill-bank}/scripts/list_automl_support.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --format textUse --action distill, --action prune, or --action quantize when the user
asks about a specific compression action.
AutoML support for an action requires
skills/models/<model_skill>/schemas/<action>.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/action as AutoML-supported.
Files
- ATTRIBUTION.mdAdded by AutoGineer933 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md3.4 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json784 B
- skill-card.md4.0 KiB
- skill.oms.sig4.7 KiB
BENCHMARK.md
Skill Benchmark: tao-list-capabilities
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
tao-list-capabilitiesEvaluation date: 2026-09-17
Evaluator version:
1.5.6Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)Tasks: 1 evaluation tasks (1 positive)
Dataset digest:
sha256:37b53f2b0f6a6937497a17b00feacbe45029e33d7a2b28eb1b3ebfaaa89414ec(skill-evaluator-dataset-snapshot/1)Attempts per task: 3
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
What This Report Answers
The three-tier evaluation checks whether the skill:
is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.
Results at a Glance
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 99.7% — baseline ran, but no comparable score was available; uplift unavailable | 76.5% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Correctness | 0.0% → 100.0% (+100.0 points) | 40.0% → 70.0% (+30.0 points) |
| Discoverability | 100.0% — baseline ran, but no comparable score was available; uplift unavailable | 47.5% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 16.7% → 100.0% (+83.3 points) | 48.3% → 65.8% (+17.5 points) |
| Efficiency | 98.6% — baseline ran, but no comparable score was available; uplift unavailable | 99.1% — baseline ran, but no comparable score was available; uplift unavailable |
How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 63,259 | 90,592 | N/A | N/A | skill 1/1; base 3/3 |
| claude-code | tao-list-capabilities-basic | 63,259 | 90,592 | N/A | N/A | skill 1/1; base 3/3 |
| codex | All cases | 42,474 | 13,506 | N/A | N/A | skill 2/2; base 1/1 |
| codex | tao-list-capabilities-basic | 42,474 | 13,506 | N/A | N/A | skill 2/2; base 1/1 |
| ALL AGENTS | Dataset aggregate | 105,733 | 104,098 | N/A | N/A | skill 3/3; base 4/4 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 12 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 1 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (
skills/core/tao-list-capabilities/SKILL.md)MEDIUM SCHEMA/frontmatter_field_placement: Root field 'tags' is ignored; use 'metadata.tags' (
skills/core/tao-list-capabilities/SKILL.md)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)7 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did it avoid wasted tool calls and token usage? | skill_efficiency (50%) + token_efficiency (50%) |
Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (
goal_accuracy) and expected workflow adherence (behavior_check).Efficiency is 50% tool-call productivity (the backward-compatible
skill_efficiencywire id) and 50%token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.skill_efficiency(Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).accuracy(Accuracy): final-answer correctness against the reference answer.goal_accuracy(Goal Accuracy): whether the user's goal was achieved.behavior_check(Behavior Check): whether the expected workflow behavior was followed.token_efficiency(Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools
The author says: Requires the packaged TAO skill bank helper scripts.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-list-capabilities · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-list-capabilities.zip