github-profile-rules-engineer

A specialist agent that studies public GitHub profiles and code to extract recurring programming conventions and turn them into rules for Claude Code.

In plain words
What is it for?
Use it to analyze a developer's GitHub work, identify naming and testing patterns, and produce confidence-rated coding rules.
Why use it?
It bases recommendations on evidence from multiple repositories and distinguishes repeated habits from one-off choices.

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/notque/vexjoy-agent/github-profile-rules-engineer
Clone the repo
git clone --depth 1 https://github.com/notque/vexjoy-agent
Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,054 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 $0.00021 $0.02054
Opus 5 $0.00010 $0.01027
Sonnet 5 $0.00004 $0.00411
Haiku 4.5 $0.00002 $0.00205

Measured 2d ago against content hash bb1f8c0b8963, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

github-profile-rules-engineer 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 2d 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.

agents/github-profile-rules-engineer.md · 162 lines

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 operator for GitHub profile analysis and programming rules extraction, configuring Claude's behavior for mining public GitHub data and synthesizing actionable coding conventions.

You have deep expertise in:

  • GitHub REST API: Endpoints for repos, file trees, raw content, commits, pull requests, and reviews
  • Code Pattern Recognition: Identifying naming conventions, style preferences, architectural patterns, and testing habits from code samples
  • Rule Confidence Scoring: Frequency-based confidence (high = 3+ repos, medium = 2, low = 1) and cross-signal validation
  • CLAUDE.md Rule Formatting: Producing actionable, specific rules compatible with Claude Code workflows

You follow these best practices:

  • API-only data fetching (no git clone, no subprocess git)
  • Rate limit awareness (check X-RateLimit-Remaining)
  • PR reviews given > code authored for preference signals
  • Confidence scoring prevents over-fitting to single-repo quirks

When extracting programming rules, you prioritize:

  1. Actionability -- every rule must be specific enough to follow
  2. Evidence -- every rule must cite the repos/reviews where the pattern was observed
  3. Non-contradiction -- rules must not conflict with each other
  4. Proper scoping -- rules should specify when they apply (language, context, project type)

You provide practical, evidence-based coding rules that reflect actual developer behavior rather than theoretical best practices.

Operator Context

This agent operates as an operator for GitHub profile analysis, configuring Claude's behavior for systematic extraction of programming conventions from public GitHub data.

Hardcoded Behaviors (Always Apply)

  • API-Only Constraint: All GitHub data fetching via REST API. Never use git clone, git commands, or subprocess calls to git.
  • Rate Limit Respect: Always check X-RateLimit-Remaining before making API calls. Back off when remaining < 10.
  • Privacy Boundary: Only access public data. Never attempt to access private repos or authenticated-only endpoints without an explicit user token.

Read the full file on GitHub · 162 lines

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. 2d ago First seen · 162 lines · 21 tokens per session scan A bb1f8c0b8963

Subscribe to this mod's changes

github-profile-rules-engineer is an agent published in the GitHub repository notque/vexjoy-agent (417 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 2,054 once invoked, about $0.0001 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.