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S02 — GitHub System Prompts Search Log

Research R0049 — Landscape Scan
Run 2026-03-31-02
Query Q001
Search S02

Tool and Description

Field Value
Tool WebSearch
Query GitHub published AI system prompt structured analytical framework research agent competing hypotheses evidence scoring
Date 2026-03-31
Results returned 10
Selected 4
Rejected 6

Additional searches: awesome-system-prompts research methodology, OpenAI deep research system prompt leaked, Perplexity system prompt leaked

Summary

Searched GitHub repositories and prompt collections for published system prompts implementing analytical frameworks. Found extensive collections of leaked/published commercial prompts (Perplexity, OpenAI, Claude, etc.) but none implementing comprehensive research methodology. Found partial implementations of SATs via LLMs.

Selected Results

Result Title Rationale
S02-R01 sroberts LLM-SATs-FTW Implements 3 structured analytic techniques via LLM
S02-R02 Perplexity AI system prompt (leaked) Commercial research tool — no analytical framework
S02-R03 OpenAI Deep Research system prompt (leaked) Commercial deep research tool — no analytical framework
S02-R04 awesome-ai-system-prompts collection Comprehensive prompt collection — no research methodology prompts

Rejected Results

Result Title Rationale for Rejection
x1xhlol/system-prompts-and-models-of-ai-tools Collection of coding/chat prompts, no research methodology
HKUDS/AI-Researcher Covered under S03 preprint search
assafelovic/gpt-researcher Tool, not published prompt with analytical framework
tmgthb/Autonomous-Agents Paper collection, not prompts
promptslab/Awesome-Prompt-Engineering General prompt engineering, no research methodology
elder-plinius/CL4R1T4S Leaked prompts collection — examined, no research frameworks found

Notes

The gap between commercial system prompts (which contain no analytical methodology) and academic research (which discusses methodology but does not publish complete prompts) is the central finding. Commercial prompts delegate analytical rigor to model training and retrieval architecture, not to prompt design.