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 ysskrishna/ai-agent-skills --skill design-thinkinggit clone --depth 1 https://github.com/ysskrishna/ai-agent-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/ysskrishna/ai-agent-skills/design-thinking)<a href="https://agentmods.dev/skills/ysskrishna/ai-agent-skills/design-thinking"><img src="https://agentmods.dev/badge/skills/ysskrishna/ai-agent-skills/design-thinking/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/ysskrishna/ai-agent-skills/design-thinking"><img src="https://agentmods.dev/badge/skills/ysskrishna/ai-agent-skills/design-thinking.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 | $0.00122 | $0.00814 |
| Opus 5 | $0.00061 | $0.00407 |
| Sonnet 5 | $0.00024 | $0.00163 |
| Haiku 4.5 | $0.00012 | $0.00081 |
Grade A, and why
design-thinking 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 10d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Thinking
Fall in love with the problem, not the first solution. End with what to learn next, not just ideas.
How to run it with this skill: one clearly headed section per stage in this order: Empathize → Define → Ideate → Prototype → Test plan.
Setup (run before starting)
In one short block:
- Design challenge — who is affected and in what situation?
- Default pass — Empathize → Define → Ideate → Prototype → Test plan (state this line)
If users, constraints, or success signals are missing, ask at most 3 questions in one message, then proceed. Note any remaining gaps or working guesses in plain language (no bracket tags in Setup).
If Empathize is thin (no real user input), say so honestly in Define and keep the POV narrow instead of inventing research.
The Stages
Empathize
Who — primary user or stakeholder (facts from user vs [INFERRED]). Jobs / pains / gains — what they are trying to do and what hurts. Context — when/where the need shows up.
No fabricated quotes; paraphrase only what the user supplied.
Define
Insight statement — non-obvious tension connecting pains and context. Point of View (POV) — "[User] needs [verb] because [insight]." How Might We (HMW) — 2–3 well-scoped questions opened by the POV.
Ideate
Quantity + variety. Use HMW as prompts. Tag ideas desirable / feasible / viable as hypotheses (not proven). Produce a substantive list (no fixed count unless the user specifies one).
Prototype
Describe low-fidelity artifacts: paper flow, roleplay script, landing smoke test, clickable sketch. For each: Purpose: what question does this answer? Fidelity note: one line placing the artifact on a sketch-only vs interactive spectrum (no low/medium labels).
Test plan
Learning goals — what would convince you the idea is wrong? Participants / sample (or [TBD]). Signals — behaviors or metrics to observe. Next iteration — what changes if results are mixed.
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.
- 10d ago First seen · 81 lines · 122 tokens per session scan A b4ae675a6e75
design-thinking is a skill published in the GitHub repository ysskrishna/ai-agent-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 122 tokens to every session and 814 once invoked, about $0.0006 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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