# Leonxlnx/agentic-ai-prompt-research

Research into how agentic AI coding assistants work. Reconstructed prompt patterns, agent coordination, and security classification

Repository: https://github.com/Leonxlnx/agentic-ai-prompt-research
Canonical: https://ross.abutalabs.com/products/agentic-ai-prompt-research
License Family: other
Topics: agentic-ai, ai-research, claude, prompt-engineering, system-prompts
Last push: 2026-03-31T19:04:33+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 75, release rhythm 35, longevity 11
- inputs: {"age_days": 155, "days_push": 155, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2519, forks 1061 (observed 2026-08-28T04:06:58.232331+00:00)

## What it is
A research repository documenting reconstructed system prompt patterns, agent coordination, and security classification mechanisms behind agentic AI coding assistants like Claude Code. It is an educational reference based on behavioral observation rather than leaked or verbatim proprietary prompts.

## Use cases
- understand how agentic coding assistants assemble system prompts
- learn multi-agent coordination and orchestration patterns
- study how AI agents classify and auto-approve tool calls safely
- design sub-agent architectures for my own AI coding tool
- learn about context window compaction strategies
- research security boundaries and permission models in AI agents
- find examples of specialized agent prompts like verification and exploration agents

## When to choose
- you are building an agentic AI coding assistant and want architectural reference patterns
- you are an AI engineer or researcher studying prompt design and agent orchestration
- you want to learn how tool permission and security classification might be structured
- you need educational material on multi-agent collaboration protocols

## When to avoid
- you need verbatim official system prompts from a proprietary product
- you want production-ready code or a runnable framework rather than documentation
- you require a licensed, legally-cleared source since the repo has no license
- you need guaranteed accuracy, since all content is reconstructed approximation

## Facets
- artifact type: learning-resource
- maturity: active
- function: prompt-engineering, agent-framework, security, documentation
- domain: artificial-intelligence, large-language-models, developer-tools, tutorials
- platform: cross-platform
- tags: agentic-ai, system-prompts, claude-code, ai-research, prompt-architecture, multi-agent-orchestration, reconstructed-prompts, ai-agents

## Member repositories
- Leonxlnx/agentic-ai-prompt-research (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:58.232331+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:26:06.443762+00:00, confidence not recorded.
  - readme: https://github.com/Leonxlnx/agentic-ai-prompt-research (fetched 2026-08-28T04:06:58.232331+00:00, sha 1c352ba27ae9)
- Data as of 2026-08-30T08:39:29.467469+00:00.