pattern_discovery

A command for finding recurring structures and relationships across different subjects or problem areas. It organizes findings into categories such as hierarchies, networks, layers, modules, dependencies, and feedback loops.

In plain words
What is it for?
Use it to analyze architectures, organizational structures, dependency chains, feedback loops, and possible transfers of ideas between domains.
Why use it?
It helps turn seemingly unrelated information into reusable patterns and makes similarities between problems easier to see.

Command

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 commands/dotclaude/marketplace/pattern_discovery
Clone the repo
git clone --depth 1 https://github.com/dotclaude/marketplace
Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,001 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 $0.00009 $0.04001
Opus 5 $0.00005 $0.02001
Sonnet 5 $0.00002 $0.00800
Haiku 4.5 $0.00001 $0.00400

Measured 2d ago against content hash 315edee9e457, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pattern_discovery 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.

plugins/adaptive-learning/commands/pattern_discovery.md · 357 lines

How it starts

The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pattern Discovery Engine

Identify deep structural patterns across domains, recognize recurring frameworks, and facilitate pattern transfer for enhanced problem-solving and understanding. Transform seemingly unrelated information into coherent pattern libraries that reveal universal principles and enable innovative applications.

Pattern Category Framework

Structural Patterns (Organizational and architectural patterns)

[Extended thinking: Identify how components organize, relate, and create stable arrangements across different contexts and domains.]

Architectural Organization:

  • Hierarchical Structures: Tree-like organizations with clear parent-child relationships
  • Network Topologies: Interconnected nodes with distributed relationships and flows
  • Layered Architectures: Stratified systems with abstraction levels and interfaces
  • Modular Systems: Component-based organizations with defined boundaries and interactions
  • Fractal Patterns: Self-similar structures that repeat at different scales

Relationship Patterns:

  • Dependency Chains: Sequential relationships where elements depend on predecessors
  • Feedback Loops: Circular relationships where outputs influence inputs
  • Hub-and-Spoke: Central nodes that coordinate distributed peripheral elements
  • Mesh Networks: Distributed connectivity with multiple pathways and redundancy
  • Pipeline Flows: Sequential processing stages with defined inputs and outputs

Stability Mechanisms:

  • Balance Points: Equilibrium states that systems naturally seek
  • Tension Resolution: How opposing forces create stable dynamic states
  • Adaptation Protocols: Mechanisms that maintain structure while enabling change
  • Boundary Maintenance: How systems preserve identity while interacting with environment
  • Recovery Patterns: How systems restore stability after disruption

Behavioral Patterns (Process and interaction patterns)

[Extended thinking: Recognize recurring sequences of actions, interactions, and transformations that create predictable outcomes.]

Read the full file on GitHub · 357 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 · 357 lines · 9 tokens per session scan A 315edee9e457

Subscribe to this mod's changes

pattern_discovery is a command published in the GitHub repository dotclaude/marketplace (42 stars, last pushed 4mo ago), licensed MIT. It adds 9 tokens to every session and 4,001 once invoked, about $0.0000 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.