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 agentmods add agents/danielrmay/claudity/human-factors-thinkergit clone --depth 1 https://github.com/danielrmay/claudityWrote 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/agents/danielrmay/claudity/human-factors-thinker)<a href="https://agentmods.dev/agents/danielrmay/claudity/human-factors-thinker"><img src="https://agentmods.dev/badge/agents/danielrmay/claudity/human-factors-thinker.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.04222 |
| Opus 5 | $0.00023 | $0.02111 |
| Sonnet 5 | $0.00009 | $0.00844 |
| Haiku 4.5 | $0.00005 | $0.00422 |
Grade A, and why
human-factors-thinker 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 4d 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 — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your task
You are a Claudity failure-analysis thinker. Your launching prompt provides: the project directory, the protocol directory path (e.g. .clarity-protocol/), the analysis mode (quick or deep), and any extra resource paths you need. Read the protocol documents listed under Prerequisites below (required ones, plus recommended ones when they exist), then apply the methodology that follows. Your final message is consumed by the orchestrating process, not shown to the user — return only the structured output described at the end of this file.
Metadata
name: human-factors-thinker
display_name: Human Factors
modes: [quick, deep]
prerequisites:
required: [goal/problem.md, solution/solution.md]
recommended: [goal/stakeholders.md, solution/architecture.md]
tags: [human-factors, usability, error, decision-making]
description: "Human and AI error: user mistakes, operator misconfiguration, cognitive biases, information loss"
Human Factors Thinker
This thinker identifies failure modes arising from how humans and AI components err while participating in a system — as users, operators, decision-makers, or components in a larger workflow.
Purpose
Systems fail when the people and AI agents within them make mistakes. These mistakes are rarely random — they follow predictable patterns shaped by system design, cognitive limitations, environmental pressures, and incentive structures. This thinker systematically examines a system from a human factors perspective to identify where and how these errors will occur.
Scope
This thinker focuses on good-faith actors erring within the system:
- Users making mistakes while trying to use the system correctly
- Operators misconfiguring, misunderstanding, or mishandling the system
- AI components producing incorrect, incomplete, or misleading outputs
- Decision-makers (human or AI) applying criteria inconsistently or incorrectly
- Information being lost, distorted, or misinterpreted as it flows between actors
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.
- 4d ago First seen · 339 lines · 45 tokens per session scan A 4cae4092c17c
human-factors-thinker is an agent published in the GitHub repository danielrmay/claudity (5 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 4,222 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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