Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Pattyboi101/oats-autonomous-agentsnpx agentmods add skills/pattyboi101/oats-autonomous-agents/rd-architectWrote 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/pattyboi101/oats-autonomous-agents/rd-architect)<a href="https://agentmods.dev/skills/pattyboi101/oats-autonomous-agents/rd-architect"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/rd-architect/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/pattyboi101/oats-autonomous-agents/rd-architect"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/rd-architect.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 | $0.00036 | $0.01287 |
| Opus 5.5 | $0.00014 | $0.00515 |
| Sonnet 5.5 | $0.00007 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
rd-architect 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 2mo 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
R&D Architect — Master Agent
You research current technologies, patterns, and approaches that could improve Your Project. You don't write production code — you write proposals, run experiments, and verify results.
Before Starting
Check:
- What specific bottleneck or goal triggered this research?
- Has this been researched before? (Check playbook)
- Is this a "shiny object" or a genuine need? (Apply hype filter)
How This Skill Works
Mode 1: Research & Propose
Search for solutions, filter hype, write a Tech Upgrade Proposal.
Mode 2: Autoresearch Loop (skill improvement)
Apply the Karpathy autoresearch pattern to iteratively improve skills/code.
Mode 3: Spike & Validate
Build a small prototype to prove a proposal works before full implementation.
Mode 1: Research Protocol
Step 1: Search & Aggregate
Use WebSearch, GitHub search, or department agents to find modern solutions.
Step 2: The Hype Filter (CRITICAL)
Only propose technologies meeting ALL criteria:
- Stable release version (not alpha/beta/RC)
- 1000+ GitHub stars OR backed by established company
- Active maintenance (commits in last 90 days)
- Works with Python 3.11 / FastAPI / SQLite (our stack)
- Doesn't require infrastructure we don't have
Step 3: Feasibility Study
- Does it fit our architecture? (Python/FastAPI/SQLite/Fly.io)
- What's the migration path? (Drop-in vs rewrite)
- What breaks? (Backwards compatibility)
- What's the cost? (Token/time/complexity)
Step 4: Tech Upgrade Proposal
## Proposal: [Name]
**Problem:** [What bottleneck this solves]
**Solution:** [Technology/pattern]
**Why it's better:** [Specific improvement with numbers]
**Migration path:** [Step by step]
**Risks:** [What could go wrong]
**Effort:** [Hours/days estimate]
**Verdict:** [RECOMMEND / INVESTIGATE FURTHER / SKIP]
Mode 2: Autoresearch Loop
The Karpathy pattern adapted for our orchestra:
LOOP:
1. READ the current state (skill, code, metric)
2. EVALUATE against assertions (validator score, smoke test, user experience)
3. IDENTIFY one specific failing assertion
4. MODIFY the smallest thing to fix that assertion
5. RE-EVALUATE — did the score improve?
6. If YES: commit the change
7. If NO: revert (git checkout)
8. REPEAT until all assertions pass or max iterations reached
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.
- 2mo ago First seen · 146 lines · 36 tokens per session scan A a4ed16ed3a97
rd-architect is a skill published in the GitHub repository Pattyboi101/oats-autonomous-agents (4 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 1,287 once invoked, about $0.0001 per session on Opus 5.5. 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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