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 skills/holymonkey/youtube-example-ai-studio/prototypenpx skills add HolyMonkey/youtube-example-ai-studio --skill prototypegit clone --depth 1 https://github.com/HolyMonkey/youtube-example-ai-studioWrote 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/holymonkey/youtube-example-ai-studio/prototype)<a href="https://agentmods.dev/skills/holymonkey/youtube-example-ai-studio/prototype"><img src="https://agentmods.dev/badge/skills/holymonkey/youtube-example-ai-studio/prototype.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 | $0.00031 | $0.00774 |
| Opus 5 | $0.00015 | $0.00387 |
| Sonnet 5 | $0.00006 | $0.00155 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
prototype 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 yesterday.
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
97% identical to prototype — 2 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.
What it actually says
When this skill is invoked:
-
Read the concept description from the argument. Identify the core question this prototype must answer. If the concept is vague, state the question explicitly before proceeding.
-
Read CLAUDE.md for project context and the current tech stack. Understand what engine, language, and frameworks are in use so the prototype is built with compatible tooling.
-
Create a prototype plan: Define in 3-5 bullet points what the minimum viable prototype looks like. What is the core question? What is the absolute minimum code needed to answer it? What can be skipped?
-
Create the prototype directory:
prototypes/[concept-name]/where[concept-name]is a short, kebab-case identifier derived from the concept. -
Implement the prototype in the isolated directory. Every file must begin with:
// PROTOTYPE - NOT FOR PRODUCTION // Question: [Core question being tested] // Date: [Current date]Standards are intentionally relaxed:
- Hardcode values freely
- Use placeholder assets
- Skip error handling
- Use the simplest approach that works
- Copy code rather than importing from production
-
Test the concept: Run the prototype. Observe behavior. Collect any measurable data (frame times, interaction counts, feel assessments).
-
Generate the Prototype Report and save it to
prototypes/[concept-name]/REPORT.md:
## Prototype Report: [Concept Name]
### Hypothesis
[What we expected to be true -- the question we set out to answer]
### Approach
[What we built, how long it took, what shortcuts we took]
### Result
[What actually happened -- specific observations, not opinions]
### Metrics
[Any measurable data collected during testing]
- Frame time: [if relevant]
- Feel assessment: [subjective but specific -- "response felt sluggish at
200ms delay" not "felt bad"]
- Player action counts: [if relevant]
- Iteration count: [how many attempts to get it working]
### Recommendation: [PROCEED / PIVOT / KILL]
[One paragraph explaining the recommendation with evidence]
### If Proceeding
[What needs to change for a production-quality implementation]
- Architecture requirements
- Performance targets
- Scope adjustments from the original design
- Estimated production effort
### If Pivoting
[What alternative direction the results suggest]
### If Killing
[Why this concept does not work and what we should do instead]
### Lessons Learned
[Discoveries that affect other systems or future work]
- Output a summary to the user with: the core question, the result, and
the recommendation. Link to the full report at
prototypes/[concept-name]/REPORT.md.
Important Constraints
- Prototype code must NEVER import from production source files
- Production code must NEVER import from prototype directories
- If the recommendation is PROCEED, the production implementation must be written from scratch -- prototype code is not refactored into production
- Total prototype effort should be timeboxed to 1-3 days equivalent of work
- If the prototype scope starts growing, stop and reassess whether the question can be simplified
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.
- yesterday First seen · 100 lines · 31 tokens per session scan A 5e3c577b025d
prototype is a skill published in the GitHub repository HolyMonkey/youtube-example-ai-studio (11 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 774 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to prototype, differing in 2 lines, and is treated as a copy.
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