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 arc-mcp/arc-1 --skill generate-rap-service-researchedgit clone --depth 1 https://github.com/arc-mcp/arc-1Wrote 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/arc-mcp/arc-1/generate-rap-service-researched)<a href="https://agentmods.dev/skills/arc-mcp/arc-1/generate-rap-service-researched"><img src="https://agentmods.dev/badge/skills/arc-mcp/arc-1/generate-rap-service-researched/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/arc-mcp/arc-1/generate-rap-service-researched"><img src="https://agentmods.dev/badge/skills/arc-mcp/arc-1/generate-rap-service-researched.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 12 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00074 | $0.14510 |
| Opus 5 | $0.00037 | $0.07255 |
| Sonnet 5 | $0.00015 | $0.02902 |
| Haiku 4.5 | $0.00007 | $0.01451 |
Grade A, and why
generate-rap-service-researched 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 12d 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 — 1,158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate RAP OData Service — Research-First
Generate a production-quality RAP OData service through deep system research, best-practice analysis, and iterative planning before writing a single line of code.
This skill produces an implementation plan informed by the target SAP system's actual capabilities, existing code patterns, SAP documentation, and user requirements — then executes it only after explicit user approval.
Smart Defaults (apply silently, do NOT ask before research)
| Setting | Default | Rationale |
|---|---|---|
| Package | User's Z* package with transport | Production-ready; only use $TMP if user explicitly asks |
| Key strategy | UUID (sysuuid_x16), managed numbering |
Simplest, no collision risk |
| Behavior scenario | Managed | Framework handles CRUD, most common |
| OData version | V4 | Current SAP standard |
| Draft | Prefer for transactional Fiori Elements UI services; verify against system release and BO constraints | Best default for editable FE apps, but not every RAP service needs draft |
| Strict mode | strict ( 2 ) unless system patterns or SAP constraints justify otherwise |
Current RAP best practice, but not universal |
| Naming | SAP standard (see reference section) | Overridden by existing system patterns if found |
| Admin fields | System-appropriate (syuname/timestampl or abp_*) |
Detected from system type |
| Service exposure | OData V4 UI provider contract by default | Best fit for Fiori Elements unless the use case is an API-first service |
These defaults are starting points — research in Phase 1 may override them based on existing system patterns.
Input
The user provides a natural language description of the business requirement. This can range from vague ("I need something to track maintenance orders") to detailed ("REST API for plant maintenance with equipment hierarchy, work orders, and time recording").
Only the business requirement is required. Gather initial context — do NOT over-interview at this stage. Research will surface the right questions later.
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.
- 12d ago First seen · 1,158 lines · 74 tokens per session scan A b55c3a749cb8
generate-rap-service-researched is a skill published in the GitHub repository arc-mcp/arc-1 (191 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 14,510 once invoked, about $0.0004 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.
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