Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill failure-taxonomygit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/failure-taxonomy)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/failure-taxonomy"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/failure-taxonomy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/failure-taxonomy"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/failure-taxonomy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00587 |
| Opus 5 | $0.00011 | $0.00293 |
| Sonnet 5 | $0.00004 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
failure-taxonomy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 48 lines · 22 tokens per session scan A 82e19579e0ec
failure-taxonomy is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 587 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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