owl-listener/ai-design-skills124 installs
failure-taxonomy
Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.
How do I install this agent skill?
npx skills add https://github.com/owl-listener/ai-design-skills --skill failure-taxonomyIs this agent skill safe to install?
- Gen Agent Trust Hubpass
This skill is a documentation-only resource providing a taxonomy for classifying AI failures. It contains no executable code, tool calls, or network operations and poses no security risk.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
Failure Taxonomy
Not all AI failures are the same. A hallucination is different from a refusal, which is different from a tone mismatch. A failure taxonomy classifies failure types so teams can track, prioritise, and address them systematically.
Failure Categories
Content Failures:
- Hallucination: The AI presents false information as fact
- Inaccuracy: The AI gets details wrong (dates, numbers, names)
- Incompleteness: The AI misses important information
- Irrelevance: The AI's response doesn't address the user's actual question
- Contradiction: The AI contradicts itself within or across responses Behavioral Failures:
- Inappropriate refusal: The AI refuses a reasonable request
- Missing refusal: The AI fulfils a request it should have declined
- Tone mismatch: The AI's tone is wrong for the context
- Persona break: The AI drops out of its defined persona
- Over-generation: The AI produces far more than needed Technical Failures:
- Latency: Response takes too long
- Truncation: Response is cut off
- Format errors: Output is in the wrong format or structure
- Tool failures: The AI attempts to use a tool and fails
- Context loss: The AI loses track of conversation history Safety Failures:
- Harmful content: The AI generates content that could cause harm
- Privacy violation: The AI reveals sensitive information
- Bias manifestation: The AI's output shows bias against a group
- Manipulation: The AI's output could be used to deceive or manipulate
Severity Levels
- Critical: Causes harm or creates serious trust violation. Requires immediate fix.
- High: Significantly degrades user experience or task success. Fix within days.
- Medium: Noticeable quality issue that users can work around. Fix within weeks.
- Low: Minor quality issue. Track and batch with other fixes.
Using the Taxonomy
- Logging: Classify every detected failure by type and severity
- Trending: Track failure type frequency over time
- Prioritisation: Address highest-severity, highest-frequency failures first
- Root cause analysis: Group failures by type to identify systemic causes
- Prevention: Use failure patterns to inform guardrail design and prompt improvements
Design Artefacts
- Failure taxonomy reference document
- Failure logging templates
- Severity classification rubric
- Failure trend dashboards
- Root cause analysis protocols
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/owl-listener/ai-design-skills/failure-taxonomy">View failure-taxonomy on skillZs</a>