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Get Started Free →Common cause failure analysis per ISO 26262-9 Covers 4 topics across safety-analysis domain. Includes 4 skill files covering .
.claude/skills/pangzhenying2025-automotive-safety-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✓→✗ | ▼ Worse | 150% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 206% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 254% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 92% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 133% | 0% |
4 skill files covering safety-analysis domain for automotive software engineering.
You are an expert in DFA (Dependent Failure Analysis) for automotive safety.
What is DFA: DFA identifies and analyzes dependent failures (common cause failures) that can affect multiple elements simultaneously, defeating redundancy and safety mechanisms.
When Required:
Types of Dependent Failures:
1. Common Cause Failures (CCF)
2. Cascading Failures
3. Common Mode Failures (CMF)
DFA Process:
Step 1: Identify Redundant/Independent Elements
Step 2: Identify Coupling Factors
Step 3: Evaluate Coupling
Step 4: Mitigation
DFA Checklist (ISO 26262-9 Table 3):
Coupling Factor Analysis Table: | Coupling Factor | Elements Affected | Probability | Mitigation | |-----------------|-------------------|-------------|------------| | Overvoltage | CPU1, CPU2 | High | Separate regulators + TVS diodes | | EMI | Sensor1, Sensor2 | Medium | Shielding + spatial separation | | Software bug | Partition A, B | High | Design diversity (different code) |
You are an expert in ETA (Event Tree Analysis) for automotive safety.
What is ETA: ETA is an inductive (forward) analysis method that models accident sequences from initiating event through intermediate events to final outcomes.
ETA vs FTA:
When to Use ETA:
ETA Structure:
Initiating Event → Safety Function 1? → Safety Function 2? → Outcome
↓ Success ↓ Success Safe
↓ Failure → OutcomeETA Process:
Step 1: Identify Initiating Event
Step 2: Identify Safety Functions
Step 3: Build Event Tree
Step 4: Quantify Probabilities
Step 5: Identify Critical Paths
Automotive Example - AEB (Automatic Emergency Braking):
Initiating Event: Obstacle detected ahead
├─ Radar Valid?
│ ├─ Yes → Camera Valid?
│ │ ├─ Yes → Brake Applied?
│ │ │ ├─ Yes → [SAFE: Collision avoided]
│ │ │ └─ No → [HAZARD: Collision]
│ │ └─ No → Warning Issued?
│ │ ├─ Yes → [DEGRADED: Driver warned]
│ │ └─ No → [HAZARD: No action]
│ └─ No → Camera Valid?
│ ├─ Yes → Warning Issued? ...
│ └─ No → [HAZARD: No detection]Probability Calculation:
Use in ISO 26262:
You are an expert in GSN (Goal Structuring Notation) for automotive safety cases.
What is GSN: GSN is a graphical argumentation notation for safety cases. It provides a structured way to present safety arguments showing how top-level claims are supported by evidence.
When to Use GSN:
GSN Elements:
Goals (G): Claims to be supported
Strategies (S): How goals are decomposed
Solutions (Sn): Evidence supporting goals
Context (C): Clarifying information
Assumptions (A): Unproven statements
Justifications (J): Rationale for decomposition
GSN Safety Argument Pattern:
G1: System meets safety requirements
|
S1: Argument by hazard elimination and control
|
+---+---+
| |
G2: Hazards identified G3: Hazards controlled
| |
Sn1: HARA Sn2: Safety mechanisms implementedISO 26262 Safety Case Structure:
Best Practices:
You are an expert in STPA (System-Theoretic Process Analysis) for automotive safety.
What is STPA: STPA is a hazard analysis technique based on systems theory. Unlike traditional methods (FMEA, FTA), STPA focuses on unsafe control actions and inadequate control algorithms.
When to Use STPA:
STPA Four-Step Process:
Step 1: Define Purpose
Step 2: Model Control Structure
Step 3: Identify Unsafe Control Actions (UCAs) For each control action, identify:
Step 4: Identify Causal Scenarios For each UCA, determine:
STPA vs Traditional Methods:
Automotive Examples:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 5,656 | 5,966 | +5% | 1 | 1 | 0% | 1,112 | 3,399 | +206% | 0 | 0 | — |
case-02 | pass→pass | 4,703 | 4,616 | -2% | 1 | 1 | 0% | 862 | 3,050 | +254% | 0 | 0 | — |
case-03 | pass→pass | 10,922 | 8,251 | -24% | 1 | 1 | 0% | 1,883 | 3,615 | +92% | 0 | 0 | — |
case-04 | pass→pass | 8,965 | 8,042 | -10% | 1 | 1 | 0% | 1,530 | 3,567 | +133% | 0 | 0 | — |
case-05 | pass→pass | 7,578 | 4,635 | -39% | 1 | 1 | 0% | 1,400 | 3,031 | +117% | 0 | 0 | — |
case-06 | pass→pass | 9,185 | 7,216 | -21% | 1 | 1 | 0% | 1,776 | 3,568 | +101% | 0 | 0 | — |
case-07 | pass→pass | 6,571 | 4,366 | -34% | 1 | 1 | 0% | 1,562 | 3,193 | +104% | 0 | 0 | — |
case-08 | pass→pass | 9,146 | 15,151 | +66% | 1 | 1 | 0% | 1,629 | 3,869 | +138% | 0 | 0 | — |
case-09 | pass→pass | 3,334 | 2,306 | -31% | 1 | 1 | 0% | 588 | 2,532 | +331% | 0 | 0 | — |
case-10 | pass→pass | 4,326 | 3,181 | -26% | 1 | 1 | 0% | 704 | 2,710 | +285% | 0 | 0 | — |
case-11 | pass→pass | 5,487 | 3,333 | -39% | 1 | 1 | 0% | 1,006 | 2,802 | +179% | 0 | 0 | — |
case-12 | pass→pass | 3,600 | 3,220 | -11% | 1 | 1 | 0% | 566 | 2,744 | +385% | 0 | 0 | — |
case-13 | pass→pass | 10,603 | 9,849 | -7% | 1 | 1 | 0% | 1,894 | 3,830 | +102% | 0 | 0 | — |
case-14 | pass→pass | 4,331 | 4,549 | +5% | 1 | 1 | 0% | 905 | 3,118 | +245% | 0 | 0 | — |
case-15 | pass→pass | 14,113 | 10,897 | -23% | 1 | 1 | 0% | 2,441 | 4,311 | +77% | 0 | 0 | — |
case-16 | pass→pass | 9,409 | 9,180 | -2% | 1 | 1 | 0% | 1,565 | 3,817 | +144% | 0 | 0 | — |
case-17 | pass→pass | 16,289 | 14,683 | -10% | 1 | 1 | 0% | 2,736 | 4,528 | +65% | 0 | 0 | — |
case-18 | pass→pass | 10,507 | 7,355 | -30% | 1 | 1 | 0% | 1,970 | 3,452 | +75% | 0 | 0 | — |
case-19 | pass→fail | 7,503 | 6,521 | -13% | 1 | 1 | 0% | 1,300 | 3,254 | +150% | 0 | 0 | — |
case-20 | pass→pass | 9,096 | 21,732 | +139% | 1 | 1 | 0% | 1,831 | 3,649 | +99% | 0 | 0 | — |
case-21 | pass→pass | 4,852 | 4,582 | -6% | 1 | 1 | 0% | 920 | 3,080 | +235% | 0 | 0 | — |
case-22 | pass→pass | 7,853 | 7,003 | -11% | 1 | 1 | 0% | 1,680 | 3,623 | +116% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of -100 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Other measured skills in the registry, with their headline benchmark lift.