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 vosslab/vosslab-skills --skill human-interact-expertgit clone --depth 1 https://github.com/vosslab/vosslab-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/vosslab/vosslab-skills/human-interact-expert)<a href="https://agentmods.dev/skills/vosslab/vosslab-skills/human-interact-expert"><img src="https://agentmods.dev/badge/skills/vosslab/vosslab-skills/human-interact-expert/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/vosslab/vosslab-skills/human-interact-expert"><img src="https://agentmods.dev/badge/skills/vosslab/vosslab-skills/human-interact-expert.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 | $0.00048 | $0.00991 |
| Opus 5 | $0.00024 | $0.00495 |
| Sonnet 5 | $0.00010 | $0.00198 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade A, and why
human-interact-expert 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human interaction expert
Overview
Use this skill to turn a product question into an evidence-based HCI method, task model, guideline rationale, or evaluation plan. Frame the user goal, context, and success evidence before selecting an interaction method. Build project-specific artifacts such as a task model, study plan, heuristic ledger, and accessibility acceptance criteria so each recommendation can guide implementation and later evaluation.
Workflow
- Classify the HCI question and select a method.
- Identify the user group, goal, setting, device, task frequency, consequence of failure, and decision the team needs to make.
- Read references/task_selection.md when the request could use research, task analysis, guidelines, or evaluation.
- Consult references/topic_index.md to connect the question to a method, evidence artifact, guideline source, and oracle.
- Detect the project shape and establish the evidence baseline.
- Existing project: inventory user journeys, research findings, support issues, analytics, interface states, accessibility checks, and prior study results.
- Greenfield project: write a concise HCI brief with users, goals, context, assumptions, risks, and success measures before interface construction.
- Read references/project_workflow.md for the project-shape path and required HCI artifacts.
- Model the user's task and mental model.
- Map triggers, goals, actions, decisions, information needs, errors, recovery, handoffs, and completion evidence for each critical task.
- Capture the user's vocabulary, expectations, and visible cues that support recognition, memory, and confidence at each decision point.
- Turn the model into testable task-completion criteria and a study scenario.
- Select the smallest method that answers the decision.
- Choose interviews or contextual inquiry for unknown context and mental models.
- Choose task analysis for workflow structure and allocation questions.
- Choose cognitive walkthrough or heuristic evaluation for expert inspection.
- Choose usability sessions for observed task completion and comprehension.
- Choose an evaluation study when a hypothesis, comparison, or measured outcome requires controlled or naturalistic evidence.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 95 lines · 48 tokens per session scan A 072e1c29ec7b
human-interact-expert is a skill published in the GitHub repository vosslab/vosslab-skills (2 stars, last pushed 13d ago), licensed MIT. It adds 48 tokens to every session and 991 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 2026-08-31.
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