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 mrDesign-ww/vault-os --skill scaffold-exercisesgit clone --depth 1 https://github.com/mrDesign-ww/vault-osWrote 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/mrdesign-ww/vault-os/scaffold-exercises)<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/scaffold-exercises/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/mrdesign-ww/vault-os/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/scaffold-exercises.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 | $0.00045 | $0.00949 |
| Opus 5 | $0.00023 | $0.00475 |
| Sonnet 5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
scaffold-exercises 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 7d 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.
This is a copy
100% identical to scaffold-exercises — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scaffold Exercises
Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.
Directory naming
- Sections:
XX-section-name/insideexercises/(e.g.,01-retrieval-skill-building) - Exercises:
XX.YY-exercise-name/inside a section (e.g.,01.03-retrieval-with-bm25) - Section number =
XX, exercise number =XX.YY - Names are dash-case (lowercase, hyphens)
Exercise variants
Each exercise needs at least one of these subfolders:
problem/- student workspace with TODOssolution/- reference implementationexplainer/- conceptual material, no TODOs
When stubbing, default to explainer/ unless the plan specifies otherwise.
Required files
Each subfolder (problem/, solution/, explainer/) needs a readme.md that:
- Is not empty (must have real content, even a single title line works)
- Has no broken links
When stubbing, create a minimal readme with a title and a description:
# Exercise Title
Description here
If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.
Workflow
- Parse the plan - extract section names, exercise names, and variant types
- Create directories -
mkdir -pfor each path - Create stub readmes - one
readme.mdper variant folder with a title - Run lint -
pnpm ai-hero-cli internal lintto validate - Fix any errors - iterate until lint passes
Lint rules summary
The linter (pnpm ai-hero-cli internal lint) checks:
- Each exercise has subfolders (
problem/,solution/,explainer/) - At least one of
problem/,explainer/, orexplainer.1/exists readme.mdexists and is non-empty in the primary subfolder- No
.gitkeepfiles - No
speaker-notes.mdfiles - No broken links in readmes
- No
pnpm run exercisecommands in readmes main.tsrequired per subfolder unless it's readme-only
Moving/renaming exercises
When renumbering or moving exercises:
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.
- 7d ago First seen · 107 lines · 45 tokens per session scan A 75f5c9d77160
scaffold-exercises is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 5d ago), licensed MIT. It adds 45 tokens to every session and 949 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to scaffold-exercises, differing in 0 lines, and is treated as a copy.
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