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 trust-calibrationgit 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/trust-calibration)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/trust-calibration"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/trust-calibration/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/trust-calibration"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/trust-calibration.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 | $0.00027 | $0.01638 |
| Opus 5 | $0.00014 | $0.00819 |
| Sonnet 5 | $0.00005 | $0.00328 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
trust-calibration 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 13d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trust Calibration
Calibrated trust is the difference between an AI that augments user judgment and one that displaces it. Overtrust causes harm when the AI is wrong. Undertrust wastes the AI when it's right. Both failure modes are common, and neither shows up in standard accuracy metrics.
Designing for trust means giving users the information they need to update their trust appropriately, turn by turn.
What shapes user trust in the moment
- Surface confidence — how certain the AI sounds, regardless of whether it should
- Track record — prior interactions in this and previous sessions
- Stakes legibility — how clearly the user understands what could go wrong
- Source visibility — whether the AI shows reasoning, sources, or alternatives
- Persona fit — a "professional" persona gets more trust than a "friendly" one for the same content
These shape trust whether you design for them or not. Designing for them deliberately is what trust calibration is.
Trust failure modes
- Sycophancy-driven overtrust: AI tells the user what they want to hear; user trusts the agreement and acts on it
- Confidence-mismatch overtrust: AI sounds certain about something it shouldn't be (hallucinations, edge cases)
- Defensive undertrust: AI hedges everything ("might be", "could possibly") even when right; user tunes out the qualifier
- Authority-collapse undertrust: one wrong answer in a high-stakes context destroys trust for the whole product
- Trust laundering: low-confidence outputs presented with high-confidence formatting (bold headers, decisive bullets) — visual authority disconnected from epistemic authority
Calibration signals from the AI side
The AI shapes trust deliberately through:
- Confidence markers proportionate to actual epistemic state: "I'm fairly sure" / "I'd verify this" / "I don't know" — used because they're true, not as decoration
- Source attribution: "According to [X]" rather than unsourced assertion. Cite when possible; flag the gap when not.
- Alternative surfacing: "Two interpretations: A and B. I went with A because…" — shows the model's working
- Failure transparency: "I got that wrong earlier — here's the correction." Long-term trust gain at short-term cost.
- Capability fence-posting: "I can help with X but not Y." Defines the boundary so trust isn't tested in the wrong place.
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.
- 13d ago First seen · 103 lines · 27 tokens per session scan A 6f82115af1af
trust-calibration is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 1,638 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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