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 human-avatar/skills-for-humanity --skill s4h-emotional-trust-auditgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-emotional-trust-audit)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-emotional-trust-audit"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-emotional-trust-audit/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/human-avatar/skills-for-humanity/s4h-emotional-trust-audit"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-emotional-trust-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00066 | $0.01378 |
| Opus 5 | $0.00033 | $0.00689 |
| Sonnet 5 | $0.00013 | $0.00276 |
| Haiku 4.5 | $0.00007 | $0.00138 |
Grade A, and why
s4h-emotional-trust-audit 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 9d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Emotional Trust Audit
Trust does not fail suddenly — it erodes incrementally through small signals that accumulate below the surface. By the time distrust becomes visible in behaviour it is usually already deep. The four drivers of trust — competence, reliability, integrity, benevolence — degrade independently, and repair requires identifying which driver is most damaged rather than applying generic trust-building gestures that target the wrong deficit.
Your Process
Step 1: Name the Relationship Specify the relationship being audited — two individuals, a team and its stakeholders, a vendor relationship, a product and its users. Be precise about direction: whose trust in whom is being assessed?
Framing check: Confirm the specific relationship and trust direction before continuing. State what you've identified — the two parties and which direction of trust is under audit — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the relationship and trust direction]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Assess Each Driver For each of the four trust drivers, identify recent concrete evidence on both sides. Evidence must be specific — named events, observed behaviours, cited decisions. Generalisations don't diagnose.
- Competence — Can they do what they say? Evidence for: delivered results, demonstrated expertise, track record. Evidence against: failures, skill gaps, over-promising relative to delivery.
- Reliability — Do they do what they say? Evidence for: consistent follow-through, keeping commitments under pressure. Evidence against: broken commitments, dropped items, variable responsiveness.
- Integrity — Do they act in line with their stated values? Evidence for: transparent communication when it's costly, decisions consistent across contexts. Evidence against: values invoked selectively, principles abandoned under pressure.
- Benevolence — Do they have my interests at heart? Evidence for: advocacy on behalf of the other party, proactive disclosure of relevant information. Evidence against: decisions made without considering impact, information withheld.
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.
- 9d ago First seen · 128 lines · 66 tokens per session scan A 99116132fa93
s4h-emotional-trust-audit is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,378 once invoked, about $0.0003 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 2026-09-03.
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