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/justvugg/tools-factory/randomusernpx skills add JustVugg/tools-factory --skill randomusergit clone --depth 1 https://github.com/JustVugg/tools-factoryWhat 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 | $0.00034 | $0.00333 |
| Opus 5 | $0.00017 | $0.00167 |
| Sonnet 5 | $0.00007 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade A, and why
randomuser 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 2d 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.
What it actually says
randomuser
Generated by Gutenberg. Use this skill to call the randomuser API through the local randomuser CLI.
When to use
- The user asks for data exposed by randomuser (api).
- An agent task needs structured JSON from https://randomuser.me.
- You need to perform a write/destructive operation on randomuser — always preview first, run with
--yesonly on explicit user confirmation.
How to invoke
The skill assumes the generated CLI is on PATH (or invoke via scripts/use-go.sh run ./cmd/randomuser from the project directory).
randomuser operations
randomuser call getRoot --json
randomuser sync --json
randomuser search "<keyword>" --json
Authentication
No authentication required.
Operations index
getRoot(GET /api) — read — GET /api
Output contract
- All commands accept
--jsonand emit machine-readable output. callreturns{ dryRun, operation, request, response }.dryRun: truemeans the call was not executed (write op without--yes).searchreturns cached records.syncpopulates the local SQLite cache.
Provenance
- Schema: gutenberg.blueprint.v1
- Generated by: gutenberg 0.3.0
- Source spec: /tmp/randomuser.openapi.json
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.
- 2d ago First seen · 48 lines · 34 tokens per session scan A 9b13151cee8d
randomuser is a skill published in the GitHub repository JustVugg/tools-factory (51 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 333 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
code-review
Use when code has been written and needs validation before committing, or when the user asks for a code review or security check.
scientific-schematics
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways…
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
taiyi-ui-design
TaiyiForge 第 4 阶段 — UI/UX 契约,产出 UI-DESIGN.md。四端通用。.
taiyi-diagram-arch
TaiyiForge 辅助 — 系统/产品架构图(Mermaid · SVG 海报 · 嵌入 DESIGN.md)。OpenCode / Claude / Codex / Cursor 通用。.
taiyi-evolve
TaiyiForge 辅助 — 实现后架构与文档同步(architecture-sync)。OpenCode / Claude / Codex / Cursor 通用。.