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agentic-ai-prompt-research

Leonxlnx/agentic-ai-prompt-research

Research into agentic AI coding assistants focusing on prompt patterns and security

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Decision brief

agentic-ai-prompt-research explores functionality and security aspects of agentic AI in coding assistance. This research includes reconstructed prompts for secure coordination.

Good fit when

  • If you are specifically interested in the working mechanisms of agentic AI with a focus on Claude, it is suited for your needs.
  • Consider using this tool if your project emphasizes security classification within prompt patterns.

Avoid when

  • Avoid if your primary interest lies in generic AI agent behavior without emphasis on secure coordination methods.
  • Not suitable for those whose research does not center around specific prompts and their reconstruction techniques.

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Slowing (118d since push)
As of 3w
Provenance
Not a fork · Personal account
As of 3w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/Leonxlnx/agentic-ai-prompt-research

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Investigates workings of agentic AI in code assistance with emphasis on reconstructed prompts and secure coordination methods.

Capability facts

No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).

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README

Claude Code System Prompts

A research project exploring how modern agentic AI coding assistants work under the hood. This repository contains our best understanding of the prompt architecture, agent coordination patterns, and security mechanisms that power tools like Claude Code.

Everything here is based on behavioral observation, output analysis, community discussions, and publicly shared information. These are reconstructed approximations, not verbatim copies. The actual implementation may differ significantly.

What This Project Is

This is an educational deep-dive into the design patterns behind agentic coding assistants. We analyze how these systems:

  • Assemble dynamic system prompts at runtime
  • Coordinate multiple specialized sub-agents
  • Classify and auto-approve tool calls safely
  • Manage context windows through intelligent compaction
  • Handle memory, skills, and user preferences

The goal is to help AI engineers, researchers, and builders learn from these architectural patterns and apply them in their own projects.

What This Project Is Not

This is not a leak, dump, or direct copy of any proprietary system. The prompts documented here are our best reconstructions based on observable behavior. They represent one interpretation of how these systems likely work.

Documented Patterns

Core Identity

#PatternDescription
01Main System PromptHow the master prompt is dynamically assembled from modular sections
02Simple ModeMinimal prompt variant for lightweight operation
03Default Agent PromptBase instructions inherited by all sub-agents
04Cyber Risk InstructionSecurity boundaries between authorized and prohibited actions

Orchestration

#PatternDescription
05Coordinator System PromptMulti-worker orchestration with phased workflows
06Teammate Prompt AddendumCommunication protocols for multi-agent collaboration

Specialized Agents

#PatternDescription
07Verification AgentAdversarial testing agent that validates implementations
08Explore AgentRead-only codebase exploration with no-modify constraints
09Agent Creation ArchitectGenerates new agent configurations from requirements
10Status Line Setup AgentTerminal status line configuration across shells

Security and Permissions

#PatternDescription
11Permission ExplainerRisk assessment before tool approval
12Auto Mode ClassifierMulti-stage security classifier for autonomous tool execution

Tool Descriptions

#PatternDescription
13Tool-Specific PromptsHow individual tools (Bash, Edit, Agent, etc.) describe themselves

Utility Patterns

#PatternDescription
14Tool Use SummaryGenerating concise labels for completed tool batches
15Session SearchSemantic search across past conversation sessions
16Memory SelectionSelecting relevant memory files for query context
17Auto Mode CritiqueReviewing user-written classifier rules
20Session TitleLightweight title generation for session management
2

For agents

This page has a .md twin and JSON over the API.

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