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 kitchen-engineer42/joharnessburg --skill app-design-thinkinggit clone --depth 1 https://github.com/kitchen-engineer42/joharnessburgWrote 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/kitchen-engineer42/joharnessburg/app-design-thinking)<a href="https://agentmods.dev/skills/kitchen-engineer42/joharnessburg/app-design-thinking"><img src="https://agentmods.dev/badge/skills/kitchen-engineer42/joharnessburg/app-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/kitchen-engineer42/joharnessburg/app-design-thinking"><img src="https://agentmods.dev/badge/skills/kitchen-engineer42/joharnessburg/app-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 | $0.00102 | $0.02225 |
| Opus 5 | $0.00051 | $0.01112 |
| Sonnet 5 | $0.00020 | $0.00445 |
| Haiku 4.5 | $0.00010 | $0.00222 |
Grade A, and why
app-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 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
app-design-thinking
The knowledge phases produced byte-identical knowledge inventories at <project>/.claude/skills/ and <project>/.agents/skills/. Now the question is: what app do we build with them, and how do we build it? That's the third and fourth links of the app-type definition cascade — app mechanism and build pipeline. This skill teaches how to settle those decisions from the knowledge already extracted.
[[schema-design]] is this skill's twin in the knowledge phases — same methodology, different links in the cascade. Both teach taste, neither prescribes.
What this skill is NOT
- It's not picking from a closed menu of app archetypes. The 5 reference apps are examples for pattern-matching, not a catalog. Every John project should invent its own runtime + pipeline shape; templates may ship common shapes as starting points, but users always override. If the project's runtime doesn't resemble any of the 5, that's normal — invent it.
- It's not one-shot. Runtime and pipeline iterate as the produced skills reveal what the app naturally wants to be.
- It's not an alternative to the user's intent. They own the project's what; this skill teaches you how to translate it into how.
The third decision — app mechanism
The runtime is how the produced app works for end-users. Pin it down by asking:
- Who's the end-user? (a teacher? a compliance officer? a player? a researcher?) Their context determines the runtime shape.
- What's their input? (a document upload? a chat? a click? nothing — they just browse?) The input shape constrains the runtime architecture.
- What's their output? (a verdict? a slide deck? an interactive widget? a downloadable file?) The output shape determines what the runtime must produce.
- Is there an LLM at runtime, or only at build time? (a static slide deck has no runtime LLM; a chat-based study companion calls a workerLLM on every user turn.) See
references/runtime-vs-buildtime-llm.md. - Is the runtime stateful? (does it remember the user across sessions? track progress? store uploads?) If end-users wait on expensive generation — upload → job → download — [[job-runtime]] supplies the runtime pattern.
What ships with it
3 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 · 120 lines · 102 tokens per session scan A 1d66809502fe
app-design-thinking is a skill published in the GitHub repository kitchen-engineer42/joharnessburg (9 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 2,225 once invoked, about $0.0005 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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