Openclaw Skills turns raw system failure descriptions into visual, quantitative fault tree analyses with probabilities, importance ranking, and reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-fta-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-fta-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Fault Tree Analysis?
Fault Tree Analysis (FTA) is an Openclaw Skills workflow for modeling how basic failures combine into higher-level system faults using AND/OR logic. It converts a free-form system failure description into a structured, machine-readable fault tree that can be rendered, validated, and analyzed.
Use this skill when you need to estimate top-event probability, rank the most critical basic events, identify minimum cut sets, and produce a professional HTML report for reliability, safety, and quality reviews. Openclaw Skills keeps the process repeatable whether you start from a conversation, a JSON file, or an imported YAML dataset.
Fault Tree Analysis Use Cases
- Reliability and safety reviews for systems with serial, parallel, or redundant architectures.
- Converting natural-language failure descriptions into standard FTA JSON for Openclaw Skills workflows.
- Quantitative top-event probability analysis for small failure probabilities and risk estimation.
- Importance ranking and minimum cut set discovery to prioritize corrective actions.
- Generating Graphviz diagrams and HTML reports for audits, engineering handoffs, or quality reviews.
- Importing and exporting existing JSON or YAML fault tree datasets.
How Fault Tree Analysis Works
Define the system and top event. Start with a clear description of the system boundary, the failure you want to analyze, and any known component failure probabilities.
Build the fault tree structure. Create a standard JSON dataset with nodes for basic, intermediate, AND, and OR events, then connect them with directed edges.
Validate the model. Check that probabilities are between 0 and 1, that the event relationships make sense, and that the tree is structurally complete.
Render the fault tree. Use the Graphviz-backed visualizer to generate a readable diagram for communication and review.
Run quantitative analysis. Calculate the top-event probability, importance measures, and minimum cut sets to understand where the system is most vulnerable.
Generate the final report. Compile the inputs and analysis results into an HTML report, and optionally export the model to another format for downstream use.
Fault Tree Analysis Setup
- Install Graphviz so the visualizer can render fault tree diagrams.
# Ubuntu/Debian
sudo apt-get update && sudo apt-get install -y graphviz
# macOS
brew install graphviz
Make sure Python and the Graphviz executable are available in your PATH.
Prepare an FTA dataset in JSON, or start from YAML if you plan to import an existing model.
If your source data is YAML, convert it to the standard JSON format first.
python scripts/fta_io.py --mode import --input source.yaml --output fta_data.json
- Run the Openclaw Skills analysis pipeline.
python scripts/fta_visualizer.py --input fta_data.json --output fault_tree.png
python scripts/fta_calculator.py --input fta_data.json --output calc_result.json
python scripts/fta_report.py --input fta_data.json --calc-result calc_result.json --output report.html
- If you need to share the model in another format, export it after analysis.
python scripts/fta_io.py --mode export --input fta_data.json --output export.yaml
Fault Tree Analysis Data Schema & Taxonomy
Input dataset structure
| Field | Type | Purpose |
|---|---|---|
name |
string | Human-readable system name |
top_event |
string | Root event ID representing the system-level failure being analyzed |
nodes |
object | Event registry keyed by event ID |
edges |
array | Directed parent-child relationships between events |
Node metadata
| Field | Type | Allowed values / meaning |
|---|---|---|
type |
enum | basic, intermediate, and, or |
name |
string | Event label shown in the tree and report |
probability |
number | Failure probability, expected to stay between 0 and 1 |
description |
string | Optional supporting notes for review and documentation |
Relationship metadata
edgesentries use{ from, to }semantics.frompoints to the parent event or gate.topoints to the child event.- The tree should remain consistent with the chosen AND/OR logic.
Generated files
| File | Purpose |
|---|---|
fault_tree.png |
Graphviz-rendered fault tree visualization |
calc_result.json |
Probability calculation output and analysis results |
report.html |
Shareable HTML report for review and handoff |
export.yaml |
Optional exported model format for interoperability |
Analysis taxonomy
- System metadata:
name,top_event - Event metadata:
id,type,name,probability,description - Structural metadata:
edgesrelationships between events - Analysis outputs: top-event probability, importance ranking, minimum cut sets
Notes
- Probabilities should stay in the 0 to 1 range.
- For very small failure probabilities, the approximation
P ≈ λtcan be used. - JSON and YAML are both supported for import/export.
Fault Tree Analysis Advanced Features
- Graphviz-backed rendering for publishable fault tree diagrams.
- Quantitative top-event probability calculation for reliability and safety analysis.
- Importance analysis to identify the most critical basic events and prioritize fixes.
- Minimum cut set analysis to expose weak points in redundant architectures.
- JSON and YAML import/export for interoperating with existing FTA datasets.
- End-to-end automation from model input to visual output to HTML report.
- Supports both qualitative and quantitative FTA in one Openclaw Skills workflow.
- Practical small-probability handling with the
P ≈ λtapproximation for near-zero failure rates.
SKILL.md
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