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 frustration-detectiongit 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/frustration-detection)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/frustration-detection"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/frustration-detection/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/frustration-detection"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/frustration-detection.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 | $0.00031 | $0.01152 |
| Opus 5 | $0.00015 | $0.00576 |
| Sonnet 5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
frustration-detection 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frustration Detection
Most AI products treat every user message as having the same emotional weight. They don't. "Cancel my subscription." and "PLEASE just cancel my subscription!!!" deserve different responses. Frustration detection is the perception skill that picks up the signal so the rest of the system can adapt.
This is the unnamed skill that sits behind tone calibration, escalation design, and graceful repair. Without it, those skills can't fire at the right moment.
Signals
Linguistic
- Capitalisation shifts: ALL CAPS, sudden "PLEASE", "URGENT"
- Punctuation density:
?!?!,...., multiple!! - Hedge-stripping: terse, demand-form ("just fix it", "stop")
- Repetition: same concept restated across turns
- Profanity (mild or strong)
Structural
- Decreasing turn latency (rapid-fire replies)
- Increasing turn latency past a threshold (walk-away)
- Re-asking the same question after a response
- Explicit human-handoff request ("speak to a person")
- Sudden topic switches
Lexical / affect
- Negative affect words ("useless", "annoying", "broken")
- Time-pressure words ("now", "immediately", "deadline")
- Doubt words ("really?", "are you sure", "is that right")
- Disengagement words ("never mind", "forget it", "whatever", "fine")
Detection should be cumulative across signals and turns, not single-feature.
Decision rules
- One signal is noise; two is a pattern. Don't act on a single CAPS message; act when CAPS plus repetition appear.
- If frustration is rising AND the AI has already attempted a fix once, escalate. Don't reach for a third rephrase.
- Urgency without frustration ≠ frustration. Speed up; don't change tone.
- Long latency after a long AI response is a walk-away, not deep reading. Save state, offer re-engagement, don't continue.
- Adapt silently. Don't name the emotion. "I sense you're frustrated" is patronising. Lower confidence, slow pace, raise warmth — but in the prose, not the meta-commentary.
Anti-patterns
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 · 92 lines · 31 tokens per session scan A 4a8da34b980f
frustration-detection is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 1,152 once invoked, about $0.0002 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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