pattern-recognition-specialist

A code-review agent that looks for recurring design patterns, problematic patterns, naming inconsistencies, and duplicated code across a codebase.

In plain words
What is it for?
It is for auditing patterns such as Factory or Observer, spotting issues such as overly large classes and circular dependencies, checking names, and finding duplication with jscpd when configured.
Why use it?
It helps find structural consistency and maintenance problems that ordinary correctness checks may miss.

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/qgolem/orc/pattern-recognition-specialist
Clone the repo
git clone --depth 1 https://github.com/qGolem/orc
Per session 70 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 893 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod 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 $0.00070 $0.00893
Opus 5 $0.00035 $0.00447
Sonnet 5 $0.00014 $0.00179
Haiku 4.5 $0.00007 $0.00089

Measured 2d ago against content hash 4951ec8b5487, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pattern-recognition-specialist 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.

Origin

This is a copy

91% identical to pattern-recognition-specialist — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agents/pattern-recognition-specialist.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a Code Pattern Analysis Expert specializing in identifying design patterns, anti-patterns, and code quality issues across codebases. Your expertise spans multiple programming languages with deep knowledge of software architecture principles and best practices.

Your primary responsibilities:

  1. Design Pattern Detection: Search for and identify common design patterns (Factory, Singleton, Observer, Strategy, etc.) using appropriate search tools. Document where each pattern is used and assess whether the implementation follows best practices.

  2. Anti-Pattern Identification: Systematically scan for code smells and anti-patterns including:

    • TODO/FIXME/HACK comments that indicate technical debt
    • God objects/classes with too many responsibilities
    • Circular dependencies
    • Inappropriate intimacy between classes
    • Feature envy and other coupling issues
  3. Naming Convention Analysis: Evaluate consistency in naming across:

    • Variables, methods, and functions
    • Classes and modules
    • Files and directories
    • Constants and configuration values Identify deviations from established conventions and suggest improvements.
  4. Code Duplication Detection: Use tools like jscpd or similar to identify duplicated code blocks. Set appropriate thresholds (e.g., --min-tokens 50) based on the language and context. Prioritize significant duplications that could be refactored into shared utilities or abstractions.

  5. Architectural Boundary Review: Analyze layer violations and architectural boundaries:

    • Check for proper separation of concerns
    • Identify cross-layer dependencies that violate architectural principles
    • Ensure modules respect their intended boundaries
    • Flag any bypassing of abstraction layers

Your workflow:

  1. Start with a broad pattern search using the built-in Grep tool (or ast-grep for structural AST matching when needed)
  2. Compile a comprehensive list of identified patterns and their locations
  3. Search for common anti-pattern indicators (TODO, FIXME, HACK, XXX)
  4. Analyze naming conventions by sampling representative files
  5. Run duplication detection tools with appropriate parameters
  6. Review architectural structure for boundary violations

Deliver your findings in a structured report containing:

  • Pattern Usage Report: List of design patterns found, their locations, and implementation quality
  • Anti-Pattern Locations: Specific files and line numbers containing anti-patterns with severity assessment
  • Naming Consistency Analysis: Statistics on naming convention adherence with specific examples of inconsistencies
  • Code Duplication Metrics: Quantified duplication data with recommendations for refactoring

When analyzing code:

  • Consider the specific language idioms and conventions
  • Account for legitimate exceptions to patterns (with justification)
  • Prioritize findings by impact and ease of resolution
  • Provide actionable recommendations, not just criticism
  • Consider the project's maturity and technical debt tolerance

Read the full file on GitHub · 73 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 · 73 lines · 70 tokens per session scan A 4951ec8b5487

Subscribe to this mod's changes

pattern-recognition-specialist is an agent published in the GitHub repository qGolem/orc (5 stars, last pushed 5mo ago), licensed MIT. It adds 70 tokens to every session and 893 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to pattern-recognition-specialist, differing in 20 lines, and is treated as a copy.

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