New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Official agent skill

Tao Analyze Changenet Rca

by NVIDIA in NVIDIA/skills

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation.

OfficialApache-2.0Auto-check: notesDevelopment

Install Tao Analyze Changenet Rca

skills CLI
$ npx skills add NVIDIA/skills --skill tao-analyze-changenet-rca -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills tao-analyze-changenet-rca --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tao-analyze-changenet-rca .claude/skills/tao-analyze-changenet-rca && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tao-analyze-changenet-rca
GitHub stars
3.6k
Token cost
~1.6k tokens
SKILL.md length
731 words
Files
16 (incl. references)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation.

  • Works in 4 steps: Experiment result directory — contains… → Training code directory — the… → Dataset directory — where CSV files and… → …
  • Analyzing ChangeNet model failures
  • SKILL.md covers Inputs, Visual Inspection Primer, Investigation Flow and Execution: Parallelize With…, plus 1 more section
  • Runs Shell scripts from its folder

What it does

Tao Analyze Changenet Rca is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NOPASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yaml` and `evals/evals.json`). Compatibility notes: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.

It sits in Development, covering Root cause analysis. It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Analyzing ChangeNet model failures
  • Investigating poor recall / FAR / PASS-NOPASS metrics
  • Auditing visual inspection pipeline quality
  • Running an RCA report for an AOI defect-detection model

Example prompts

  • “RCA on my ChangeNet model”
  • “why is my AOI model failing”
  • “audit ChangeNet predictions”
  • “/tao-analyze-changenet-rca”

Requirements

  • A Bash shell
  • Docker
  • Compatibility (from SKILL.md): Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Experiment result directory — contains train/ and inference/
  2. Training code directory — the visual_changenet/ source tree
  3. Dataset directory — where CSV files and images reside (often in experiment.yaml)
  4. Target KPI — default to Recall-first if not specified. Options: Recall-first (FAR at 100% recall), FAR-first (recall at target FAR)…

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.

    From compatibility in the SKILL.md frontmatter.

Context cost

Tao Analyze Changenet Rca loads about 1.6k tokens when it runs, and up to ~9.6k if it reads all its reference files. Until then it costs about 140 tokens; SKILL.md has 731 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~140
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 731 words, ~1,616 tokens.

Download SKILL.mdSave it as .claude/skills/tao-analyze-changenet-rca/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.
name
tao-analyze-changenet-rca
description
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".
allowed-tools
Read, Bash
compatibility
Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.
license
Apache-2.0
metadata.author
NVIDIA Corporation
metadata.version
0.1.0
tags
application, rca, changenet

TAO ChangeNet Classification RCA Skill

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

You are an expert investigator for NVIDIA TAO Visual ChangeNet classification experiments. Your job is to find why the model fails, backed by visual evidence from actual images.

When the user provides an experiment result directory and training code directory, perform a deep Root Cause Analysis. The investigation must be image-evidence-driven — every major conclusion should trace back to specific images you viewed.

Inputs

  1. Experiment result directory — contains train/ and inference/
  2. Training code directory — the visual_changenet/ source tree
  3. Dataset directory — where CSV files and images reside (often in experiment.yaml)
  4. Target KPI — default to Recall-first if not specified. Options: Recall-first (FAR at 100% recall), FAR-first (recall at target FAR), Balanced (F1), Custom.

Visual Inspection Primer

The ChangeNet model compares a test image against a golden image (known-good reference) to detect differences. When viewing images, check these three things:

  1. Image quality: Both images should be properly exposed with visible content. Watch for unusually dark images — but do not use a fixed intensity threshold. Some illumination types (e.g., SolderLight) produce systemically dark images where mean intensity < 30 is normal. Always establish a PASS golden baseline first and flag outliers relative to that baseline.
  2. Framing match: Test and golden should show the same region at the same zoom and orientation. Mismatched framing (e.g., wide-field vs close-up) indicates a golden pipeline error.
  3. Defect visibility: Can you see the difference between test and golden? Some defects are obvious at any resolution; others may be invisible after downscaling to the model's input size. Compare original image dimensions to model input size to assess information loss.

Investigation Flow

The investigation has 5 phases. Phase 1 (numbers) gives you hypotheses. Phase 2 (images) proves or disproves them. Phase 3 (cross-dimensional) finds hidden patterns. Phase 4 (config) explains the mechanism. Phase 5 (counterfactual) quantifies fixes. Phase 2 is the core — spend the most effort there. Phase 5 is the most actionable — never skip it.

  • Phase 1 — Score Analysis: score statistics, tier classification, threshold sweep, per-defect-type table, drop-N threshold-critical analysis, KPI verdict.
  • Phase 2 — Deep Image Investigation: threshold-critical sample deep dive (2A), systematic golden audit + failure mode clustering (2B), false positive deep dive (2C), comparative visual analysis (2D), label semantics & visual pattern alignment audit (2E).
  • Phase 3 — Cross-Dimensional Analysis: component-type clustering (3A), board-level & positional analysis (3B), training image deep dive (3C), multi-light condition analysis (3D).
  • Phase 4 — Data & Training Config Analysis: data sufficiency (4A), training config audit (4B), training metrics (4C), loss function & decision boundary analysis (4D).
  • Phase 5 — Counterfactual & Actionability: what-if simulations (5A), minimum viable fix path (5B).

See references/investigation-phases.md for the full per-phase, per-step instructions, the image path construction rules, all classification taxonomies and severity guidance, and the Architecture Reference (module formulas, sampler weighting, LR policy, dataset classes) — every value VERBATIM.

Show full SKILL.md (244 more words)Show less

Execution: Parallelize With Subagents

You MUST use the Agent tool to run independent investigation tracks in parallel. Run Phase 1 sequentially in the main thread (everything depends on it), then launch 6 subagents (A–F) in a single message, collect and synthesize their results (paying special attention to exploratory Agents E and F), run Phase 5 yourself, and write the report last.

Before writing RCA_Report.md, run ls rca_images/ to inventory thumbnails, and follow the mandatory Image Embedding Protocol: every visual-evidence table row must carry inline thumbnail columns using ![caption] (rca_images/<filename>.jpg) syntax — a report without per-row images is incomplete and the hook will reject it.

See references/parallelization.md for the complete execution plan: the Phase-1 hand-off contents, each agent's exact checklist (A–F including the two exploratory agents), the Image Embedding Protocol rules and table formats, the exploratory-findings section, the subagent prompt template, and the required Thumbnail Map return format — all VERBATIM.

Report Structure and Output

Produce RCA_Report.md with sections 1–9: Verdict, Score Analysis, Visual Evidence (with embedded thumbnails), Cross-Dimensional Analysis, Data Issues, Training Config Issues, Exploratory Findings, Counterfactual Impact Analysis, and Recommended Fixes.

Always save into a timestamped folder under the experiment result directory:

<experiment_result_dir>/rca_results/YYYY-MM-DD_HHMMSS/
├── RCA_Report.md
├── rca_images/
├── rca_config/
└── claude_session.jsonl

Get the real timestamp by running date +%Y-%m-%d_%H%M%S in Bash — never hardcode or guess it. If the user specifies a custom path, use that instead but keep the same structure.

See references/output-structure.md for the complete section-by-section report skeleton (every table header and summary line) and the full output layout with hook-copied contents — VERBATIM.

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 15 other files (references) in skills/tao-analyze-changenet-rca of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • config/skillspector-baseline.yaml
  • evals/evals.json
  • hooks/_parse-stdin.sh
  • hooks/rca-defect-coverage.sh
  • hooks/rca-depth-check.sh
  • hooks/rca-package.sh
  • hooks/rca-phase-completeness.sh
  • hooks/rca-report-check.sh
  • hooks/rca-script-check.sh
  • references/investigation-phases.md
  • references/output-structure.md
  • references/parallelization.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 14a98ae

Compare with similar skills

Tao Analyze Changenet Rca next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tao Analyze Changenet Rca compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tao Analyze Changenet Rca this skillNVIDIA/skills3.6k—~1.6kAutomated safety check: NotesApache-2.0
Megatron-LM CI Failure TriageNVIDIA/Megatron-LM18k—~1.6kAutomated safety check: PassApache-2.0
LLM Torch Profiler Analysissgl-project/sglang37k2 repos~6.4kAutomated safety check: PassApache-2.0
OpenLogi macOS Permissions TriageAprilNEA/OpenLogi23k—~2.5kAutomated safety check: NotesApache-2.0
Bug Finder for daisyUIsaadeghi/daisyui43k—~2.3kAutomated safety check: PassMIT
Root Cause Debugginggarrytan/gstack136k—~1.4kAutomated safety check: PassMIT

Similar skills

  • Megatron-LM CI Failure Triage

    NVIDIA/Megatron-LM

    Official

    Investigates a failing GitHub Actions run or job for Megatron-LM, finds the root cause plus the PR and test author involved, and files a structured bug issue.

    18k GitHub stars~1.6k tokensUpdated today
    DevOps & CloudAuto-check passed

  • LLM Torch Profiler Analysis

    sgl-project/sglang

    Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.

    37k GitHub starsUsed in 2 repos~6.4k tokens
    DevelopmentAuto-check passed

  • OpenLogi macOS Permissions Triage

    AprilNEA/OpenLogi

    Decides whether an OpenLogi device problem on macOS is a privacy-permission (TCC) problem, using agent log lines, and says which identity needs which grant.

    23k GitHub stars~2.5k tokensUpdated today
    DevelopmentAuto-check: notes

  • Bug Finder for daisyUI

    saadeghi/daisyui

    Investigates suspected bugs in the daisyUI monorepo through read-only analysis, then writes a decision-ready fix plan in tmp/bugs without changing any product code.

    43k GitHub stars~2.3k tokensUpdated 2 days ago
    DevelopmentAuto-check passed

  • Root Cause Debugging

    garrytan/gstack

    Investigates bugs, errors and stack traces in phases and requires a root-cause hypothesis to be confirmed before any fix is written.

    136k GitHub stars~1.4k tokensUpdated today
    DevelopmentAuto-check passed

  • Review PR

    apache/shardingsphere

    Review Apache ShardingSphere or user-authorized downstream pull requests and PR discussions from public or authorized repository evidence.

    21k GitHub stars~6.5k tokensUpdated today
    DevelopmentAuto-check passed

More from NVIDIA/skills

All 390 skills in this repo

  • Vss Deploy Detection Tracking 2D

    NVIDIA/skills

    Official

    A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.

    3.6k GitHub starsUsed in 1 repo~4.5k tokens
    Auto-check passed

  • HSB IP Def File Tool

    NVIDIA/skills

    Official

    Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.

    3.6k GitHub stars~2.9k tokensUpdated yesterday
    Auto-check passed

  • Isaac Sim Mission Control Showcase

    NVIDIA/skills

    Official

    Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.

    3.6k GitHub stars~4.8k tokensUpdated yesterday
    Auto-check passed

  • Defect Image Generation with Cosmos AnomalyGen

    NVIDIA/skills

    Official

    Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.

    3.6k GitHub stars~5k tokensUpdated yesterday
    Auto-check: notes

  • Physical AI Video Augmentation on OSMO

    NVIDIA/skills

    Official

    Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.

    3.6k GitHub stars~4.7k tokensUpdated yesterday
    Auto-check: notes

  • TAO Detection KPI Analysis

    NVIDIA/skills

    Official

    Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.

    3.6k GitHub stars~2.7k tokensUpdated yesterday
    Auto-check: notes

Works with

Categories

Questions about Tao Analyze Changenet Rca

What does Tao Analyze Changenet Rca do?

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Tao Analyze Changenet Rca is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation.

When should I use Tao Analyze Changenet Rca?

Tao Analyze Changenet Rca fits situations like: analyzing ChangeNet model failures; investigating poor recall / FAR / PASS-NOPASS metrics; auditing visual inspection pipeline quality; running an RCA report for an AOI defect-detection model.

How do I install Tao Analyze Changenet Rca in Claude Code?

Run `npx skills add NVIDIA/skills --skill tao-analyze-changenet-rca -a claude-code`. Or copy the skill folder (skills/tao-analyze-changenet-rca in NVIDIA/skills) into .claude/skills/tao-analyze-changenet-rca in your project. Claude Code loads it when a task matches its description.

How do I install Tao Analyze Changenet Rca in Codex?

Run `npx skills add NVIDIA/skills --skill tao-analyze-changenet-rca -a codex`. Or copy the skill folder (skills/tao-analyze-changenet-rca in NVIDIA/skills) into .agents/skills/tao-analyze-changenet-rca in your project. Codex loads it when a task matches its description.

Can I use Tao Analyze Changenet Rca in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill tao-analyze-changenet-rca -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tao-analyze-changenet-rca, .gemini/skills/tao-analyze-changenet-rca, .github/skills/tao-analyze-changenet-rca and .opencode/skills/tao-analyze-changenet-rca in your project.

What does Tao Analyze Changenet Rca need to run?

Going by SKILL.md and its folder, Tao Analyze Changenet Rca needs a shell for the scripts in its folder. Our summary lists: A Bash shell; Docker. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Requires docker + nvidia-container-toolkit. Workflows declare additional requirements..

Does Tao Analyze Changenet Rca access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tao Analyze Changenet Rca safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Tao Analyze Changenet Rca use?

Tao Analyze Changenet Rca is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tao Analyze Changenet Rca use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8k tokens, read only when the agent opens those files.

What are the alternatives to Tao Analyze Changenet Rca?

Skills that share tags, products or a category with Tao Analyze Changenet Rca: Megatron-LM CI Failure Triage (NVIDIA/Megatron-LM, 18k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), OpenLogi macOS Permissions Triage (AprilNEA/OpenLogi, 23k stars) and Bug Finder for daisyUI (saadeghi/daisyui, 43k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tao Analyze Changenet Rca?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.