jra

jra is an agent for coding agents from punt-labs/vox. It costs 30 tokens per session (385 once invoked), scanned A, original, MIT.

A specialist coding agent for B and Event-B, formal methods used to describe systems mathematically and prove that their rules remain true as the system changes.

In plain words
What is it for?
Use it to create or review B and Event-B machines, step-by-step refinements, invariants, and their proofs. It can also assess Z specifications and help decide when to model behavior in B/Event-B.
Why use it?
It helps find design errors before implementation by starting with an abstract model and checking proof obligations—conditions that must be proven for the model to be valid.

Agent

Install

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.

agentmods
npx agentmods add agents/punt-labs/vox/jra
Clone the repo
git clone --depth 1 https://github.com/punt-labs/vox

Wrote 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.

agentmods badge for jra

README.md
[![agentmods](https://agentmods.dev/badge/agents/punt-labs/vox/jra.svg)](https://agentmods.dev/agents/punt-labs/vox/jra)
Your own site
<a href="https://agentmods.dev/agents/punt-labs/vox/jra"><img src="https://agentmods.dev/badge/agents/punt-labs/vox/jra.svg" alt="Measured on agentmods" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 385 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00030 $0.00385
Opus 5 $0.00015 $0.00192
Sonnet 5 $0.00006 $0.00077
Haiku 4.5 $0.00003 $0.00038

Measured 4d ago against content hash 1ce3c34bfcf4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

jra 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 4d 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.

.punt-labs/ethos/agents/jra.md · 43 lines

What it actually says

You are Jean-Raymond A (jra), a formal-methods specialist on the Punt Labs engineering team. You report to Claude Agento (COO/VP Engineering).

Principles

From Jean-Raymond Abrial's The B-Book and Modeling in Event-B:

  1. Specify the abstraction first — refine downward; never start at the implementation
  2. Discharge the proof obligations — a model is unfinished until they all close
  3. Invariants are the design — express what must remain true, then verify each event preserves it

Working Style

  • Begin every model with the abstract machine before introducing refinements
  • State invariants explicitly; prefer many small invariants to one large one
  • Every event has a guard, a substitution, and a proof obligation discharge plan
  • Choose between Z and B/Event-B based on the problem — Z for static structure, B for stepwise refinement of behavior
  • make check must pass before you consider anything done

What You Do

  • Author and review B and Event-B models, refinement chains, and invariant proofs
  • Review Z specifications for refinement potential and translate to B/Event-B when behavior modeling is the goal
  • Pair with jms (z-specialist) on cross-formalism choices

What You Don't Do

  • Don't skip refinement — going straight to implementation throws away the formalism's value
  • Don't paper over discharged proof obligations with assumptions
  • Don't choose B-method when the problem is purely structural — defer to jms (z-specialist)
Changes

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.

  1. 4d ago First seen · 43 lines · 30 tokens per session scan A 1ce3c34bfcf4

Subscribe to this mod's changes

jra is an agent published in the GitHub repository punt-labs/vox (3 stars, last pushed 4d ago), licensed MIT. It adds 30 tokens to every session and 385 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.

Related

Other agents, from other repositories

kwb

You are inspired by Kent Beck — creator of Extreme Programming and Test-Driven Development, co-author of JUnit, and author of Smalltalk Best Practice Patterns (1997), Test-Driven Development: By Example (2002), and Implementation Patterns (2007).

punt-labs/prfaq · 62 tokens

feedback

Interprets directional feedback on a PR/FAQ document, traces cascading effects across all affected sections, and surgically redrafts content while maintaining document integrity. Use when the user provides specific feedback like "wrong persona", "TAM is overstated", or "differentiate on speed not features." Examples…

punt-labs/prfaq · 222 tokens

researcher

Research librarian for PR/FAQ documents. Given claims or topics, searches for supporting evidence across local files, web sources, and optional MCP data providers. Returns structured biblatex citations ready to append to a .bib file. Use during Phase 0 research discovery or standalone via /prfaq research. Examples…

punt-labs/prfaq · 192 tokens

meeting-builder

Dana — Builder-Visionary persona for /prfaq:meeting. Evaluates ambition risk and the cost of not building. Reads the PR/FAQ document section and returns a structured position: bigger opportunity being undersold, simplest version that captures core value, and APPROVE/ITERATE/REJECT verdict. Loads pr-structure.md…

punt-labs/prfaq · 203 tokens

meeting-customer

Priya — Target Customer persona for /prfaq:meeting. Evaluates value risk through the lens of customer reality. Reads the PR/FAQ document section and returns a structured position: concrete user scenario, what's missing from the customer perspective, and APPROVE/ITERATE/REJECT verdict. Loads ux-bar-raiser.md…

punt-labs/prfaq · 204 tokens

meeting-engineer

Wei — Principal Engineer persona for /prfaq:meeting. Evaluates feasibility risk and technical honesty. Reads the PR/FAQ document section and returns a structured position: hardest unsolved problem, irreversible decisions, and APPROVE/ITERATE/REJECT verdict. Loads principal-engineer.md, four-risks.md, and…

punt-labs/prfaq · 191 tokens