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 varunk130/ai-ux-skill-library --skill ai-error-resiliencegit clone --depth 1 https://github.com/varunk130/ai-ux-skill-libraryWrote 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/varunk130/ai-ux-skill-library/ai-error-resilience)<a href="https://agentmods.dev/skills/varunk130/ai-ux-skill-library/ai-error-resilience"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-error-resilience/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/varunk130/ai-ux-skill-library/ai-error-resilience"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-error-resilience.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 | $0.00055 | $0.02115 |
| Opus 5.5 | $0.00022 | $0.00846 |
| Sonnet 5.5 | $0.00011 | $0.00423 |
| Haiku 4.5 | $0.00006 | $0.00212 |
Grade A, and why
ai-error-resilience 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 24d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Error Resilience
Design AI products that fail gracefully, communicate uncertainty honestly, and help users recover without losing trust. The RECOVER framework treats AI errors as a design material, not a bug to hide.
Core Principle
Traditional software has bugs. AI has probabilistic outputs on a spectrum of correctness. You cannot design AI UX using binary error/success patterns. Instead, design for a continuum: right, mostly right, partially right, uncertain, wrong, and dangerously wrong.
The RECOVER Framework
| Letter | Phase | Design Question |
|---|---|---|
| R | Recognize | Can the system detect when its output may be unreliable? |
| E | Express Uncertainty | Does the interface clearly communicate degrees of confidence to the user? |
| C | Contain Blast Radius | If the AI is wrong, what's the worst that can happen? How is damage limited? |
| O | Offer Alternatives | Does the user get a Plan B when Plan A might be wrong? |
| V | Verify Collaboratively | Can the user easily check, correct, or confirm the AI's output? |
| E | Evolve from Errors | Does the system learn from this error type to prevent future occurrences? |
| R | Restore Confidence | After a failure, how does the product rebuild the user's willingness to try again? |
AI Error Taxonomy
Not all AI errors are created equal. Each type requires a different UX response.
| Error Type | Description | Severity | UX Response Pattern |
|---|---|---|---|
| Confident hallucination | AI invents facts and states them as truth | Critical | Citation requirement + verification prompt |
| Stale knowledge | AI references outdated information | High | Timestamp + "knowledge cutoff" indicator |
| Context misread | AI misinterprets the user's intent or context | Medium | Reflect understanding before answering |
| Partial answer | AI addresses some but not all parts of a query | Medium | Checklist showing what was/wasn't addressed |
| Formatting error | Content is correct but presented poorly | Low | Easy reformat/regenerate affordance |
| Refusal overreach | AI refuses a legitimate request due to overly strict safety filters | Medium | Explain why + offer alternative path |
| Confidence inversion | AI is most confident when it's most wrong | Critical | Mandatory human review for high-stakes outputs |
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.
- 24d ago Changed 0091db31b3ee
- 1mo ago First seen · 162 lines · 55 tokens per session scan A 22f69192ae54
ai-error-resilience is a skill published in the GitHub repository varunk130/ai-ux-skill-library (3 stars, last pushed 23d ago), licensed MIT. It adds 55 tokens to every session and 2,115 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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