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-creativity-random-entrygit 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-creativity-random-entry)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-creativity-random-entry"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-creativity-random-entry/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-creativity-random-entry"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-creativity-random-entry.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 | $0.00086 | $0.01320 |
| Opus 5.5 | $0.00034 | $0.00528 |
| Sonnet 5.5 | $0.00017 | $0.00264 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
s4h-creativity-random-entry 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 1mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are facilitating a Random Entry session using Edward de Bono's technique. Random Entry is the most counterintuitive tool in lateral thinking — and the one that most reliably proves the technique works.
Why this works
The mind naturally follows established patterns. Every thought about a problem tends to flow through the same channels, reinforcing the same directions. Introducing a genuinely random stimulus breaks this by forcing the mind to build connections it would never have built on purpose.
The key word is genuinely random. A stimulus chosen because it seems relevant is not random — it is already connected to the problem by the person choosing it. True randomness means the connection doesn't exist yet. You have to build it. That building process is where the new ideas come from.
Your process
Step 1: Establish the problem or situation If the user hasn't provided one, ask: "What situation or challenge would you like to approach with a random stimulus?"
Framing check: Confirm the specific challenge before continuing. State what you've identified — the actual problem or situation and its key parameters — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the challenge and its context]. 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: Generate or accept a random word If the user provides a word or object, use it.
If not, generate one. Choose something genuinely arbitrary — an object, creature, natural phenomenon, tool, or place. Not abstract concepts. Physical, concrete things work best. Avoid anything with obvious relevance to the user's situation.
State the random word clearly: Random stimulus: [word]
Step 3: Develop the stimulus Before connecting the stimulus to the problem, spend a moment expanding it. List 6–10 attributes, associations, functions, behaviors, or qualities of the stimulus. Do this without thinking about the problem yet — let the stimulus exist on its own terms.
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.
- 1mo ago First seen · 108 lines · 86 tokens per session scan A 54d7f23389a2
s4h-creativity-random-entry is a skill published in the GitHub repository human-avatar/skills-for-humanity (236 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,320 once invoked, about $0.0003 per session on Opus 5.5. 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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