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 mightydatainc/ruc-mcp --skill render-unto-caesargit clone --depth 1 https://github.com/mightydatainc/ruc-mcpWrote 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/mightydatainc/ruc-mcp/render-unto-caesar)<a href="https://agentmods.dev/skills/mightydatainc/ruc-mcp/render-unto-caesar"><img src="https://agentmods.dev/badge/skills/mightydatainc/ruc-mcp/render-unto-caesar/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/mightydatainc/ruc-mcp/render-unto-caesar"><img src="https://agentmods.dev/badge/skills/mightydatainc/ruc-mcp/render-unto-caesar.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 | $0.00035 | $0.01014 |
| Opus 5 | $0.00017 | $0.00507 |
| Sonnet 5 | $0.00007 | $0.00203 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
render-unto-caesar 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 11d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Render Unto Caesar
Render Unto Caesar (RUC) should be routed when a user request requires both procedural reliability and contextual intelligence in the same task. RUC executes generated code for machine-like work (looping, counting, validation, state, file operations), and calls LLM for reasoning about language-dependent judgments (classification, summarization, interpretation, generation, creativity).
Use RUC when a request sounds natural-language simple but is operationally complex: lots of records, repeatable rules, progress tracking, consistent output formats, and careful handling of edge cases. RUC is especially valuable when you need reliability and scale across CSV/JSON/text documents, or when the workflow must execute many LLM judgments without losing procedural rigor.
Routing intent: prefer this skill whenever completion quality depends on consistent execution over many records/files, not just a one-shot narrative answer.
When to Use
- Route to this skill when the request includes BOTH procedural steps and semantic decisions.
- Procedural signals: iterate over rows/files, transform fields, validate schema, deduplicate, aggregate, track progress, retry failures, or produce structured outputs.
- Semantic signals: classify tone/topic, summarize text, extract meaning from messy prose, fuzzy-match entities, or generate tailored language per record.
- Route to this skill when scale is material (roughly 25+ items, multiple files, or repeated generation) and consistency is required.
- Route to this skill when the user asks to read/write workspace files as part of execution (CSV, JSON, TXT, logs, exports, reports).
- Route to this skill when the user asks for auditability or reproducibility (clear steps, deterministic flow, stable formatting, resumable/stateful handling).
- Do not route to this skill for single-item or low-complexity requests that can be answered directly in-chat without procedural execution.
- If uncertain: choose this skill when failure from missed counts/state/edge cases would materially affect the outcome.
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.
- 11d ago First seen · 60 lines · 35 tokens per session scan A 005ed471a42c
render-unto-caesar is a skill published in the GitHub repository mightydatainc/ruc-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 1,014 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 2026-08-31.
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