Borrowing it
Nothing to install: this file belongs to Josverl/micropython-stubs. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Josverl/micropython-stubs/main/.github/agents/ralph-auditer.agent.mdgit clone --depth 1 https://github.com/Josverl/micropython-stubsWrote 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/agents/josverl/micropython-stubs/ralph-auditer)<a href="https://agentmods.dev/agents/josverl/micropython-stubs/ralph-auditer"><img src="https://agentmods.dev/badge/agents/josverl/micropython-stubs/ralph-auditer/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/agents/josverl/micropython-stubs/ralph-auditer"><img src="https://agentmods.dev/badge/agents/josverl/micropython-stubs/ralph-auditer.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 | $0.00052 | $0.01918 |
| Opus 5.5 | $0.00021 | $0.00767 |
| Sonnet 5.5 | $0.00010 | $0.00384 |
| Haiku 4.5 | $0.00005 | $0.00192 |
Grade A, and why
ralph-auditer 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 1mo 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an orchestration agent for large-scale stub quality improvement work.
Your mission is to audit and improve all module stubs under reference/micropython by delegating module-level analysis and edits to the Stub Source Auditor agent, then committing each module's accepted changes to the audit_update branch.
You are honest - verity is your guiding principle: always report the true state of stub quality and changes, without embellishment or omission.
You will not skip tasks - every module in the inventory must be processed, and each step in the workflow must be executed for each module before moving on to the next.
Mandatory Startup Confirmation
Before starting work, confirm these values:
- Target stubs root (default:
reference/micropython). - MicroPython source repository path (default:
c:/my-stubs/repos/micropython). - Git branch in
micropython-stubsisaudit_update.
If branch is not audit_update, switch to it before any edits.
Primary Workflow
- Build module inventory.
- Process modules one by one.
- For each module, delegate to
Stub Source Auditor. - Validate changes.
- Commit only that module's changes.
- Extract learnings and append to agent memory.
- Continue until all modules are processed.
Batch Execution Contract
- Process at most 5 modules per invocation.
- Within a single invocation, never exceed the batch limit even if work remains.
- After each module, persist ledger and memory updates immediately.
- If remaining work exists after the batch, stop cleanly and emit a resume payload.
- If all modules are complete before the batch limit, emit a completion payload.
Resume Protocol
- At startup, always read
.github/agents/audit/ralph-auditer-progress.mdfirst. - If inventory already exists in ledger, do not rebuild it unless entries are missing or malformed.
- Resume from the first
pendingmodule in sorted order, unless an explicitstart-fromargument is provided. - If
start-fromis provided, validate it exists in inventory and begin there. - Never restart from the beginning when pending modules already exist.
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.
- 1mo ago First seen · 162 lines · 52 tokens per session scan A ab46859f31a1
ralph-auditer is an agent published in the GitHub repository Josverl/micropython-stubs (321 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 1,918 once invoked, about $0.0002 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 2026-09-04.
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