How does this research engine differ from just asking a standard LLM to write a plan?

Standard LLMs often provide optimistic, surface-level advice; this skill introduces a "Devil's Advocate" phase and a recursive 5-layer drill-down to identify technical trade-offs and hidden failure modes that basic prompting misses.

What are the specific technical requirements to run the research workspace?

The skill requires a Python environment to run the workspace manager and active web search tool integrations to facilitate the multi-agent reconnaissance phase.

What exactly is included in the purchase of the Deep Research Engine skill?

You will receive the research_planner.py workspace manager, the multi-agent routing protocols, the /grill-me alignment framework, and comprehensive documentation for implementation.

Can I customize the research depth or the criteria used in the synthesis phase?

Yes, the two-tier planning system allows you to define custom gates and constraints during the mandatory alignment session, ensuring the final technical blueprint respects your specific infrastructure.

Which AI agents or models is this skill compatible with?

The skill is designed for advanced LLMs with tool-calling capabilities and supports integration with major agents that can execute Python-based file lifecycle controls and external API calls.

Deep Research Engine — Stanford STORM & Drill-Down

Example session with this skill installed

Conduct a deep technical research on migrating an enterprise PostgreSQL monolith to an event-driven architecture with Kafka. Use the Stanford STORM protocol to evaluate data consistency, network partitioning, and disaster recovery.

Read your context and instructions
Compiled the deep research engine

Executive Summary

This report analyzes migrating an enterprise PostgreSQL monolith to an event-driven architecture using Apache Kafka and the Outbox Pattern.

Dimension	Monolithic PostgreSQL	Event-Driven (Kafka)	Trade-Off Rationale
Consistency	ACID (Strict)	Eventual Consistency	Mandates idempotent consumers and saga orchestrators.
Throughput	~4,500 tx/sec	>120,000 msg/sec	Horizontal consumer scaling without DB connection exhaustion.
Partitioning	Single point of failure	Distributed partitions	High partition count requires tuned replication factors (min.isr=2).

Recommended Migration Blueprint (Outbox Pattern)

-- Transactional Outbox Table
CREATE TABLE event_outbox (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    aggregate_type VARCHAR(64) NOT NULL,
    aggregate_id VARCHAR(64) NOT NULL,
    payload JSONB NOT NULL,
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

Connects securely to your tools. The creator never sees your data.

About this skill

Deep Research Engine — Stanford STORM & 5-Layer Granular Drill-Down

Turn Claude Code, Cursor, Windsurf, and Google Antigravity into autonomous, multi-perspective research intelligence engines.

⚠️ The Problem: Surface-Level AI Summaries

Standard LLM responses skim the surface, hallucinate facts, and fail when exploring enterprise architectures, market positioning, or complex technical RFCs. Single-turn prompts cannot uncover blind spots or validate competing architectural trade-offs.

⚡ The Solution: The Stanford STORM Protocol

Persona Discovery & Multi-Perspective Interviews: Simulates subject-matter experts (e.g., Security Architect, FinOps Lead, Distributed Systems Specialist) challenging core premises.
Master Architecture Blueprints: Compiles structured outlines before drafting conclusions.
5-Layer Deep Drill-Down: Explores critical components down to byte-level edge cases and protocol specs.
Interactive Clarification Safeguard: Identifies technical ambiguities before drafting reports.

📦 What's Included:

scripts/deep_research_engine.py (CLI orchestrator & multi-perspective agent generator)
references/storm_protocol.md (Stanford STORM synthesis workflows)
references/drill_down_matrix.md (5-layer interrogation standards)
references/expert_interview_templates.md (Domain expert prompt schemas)
assets/research_report_template.md (Production report template)

🔒 100% Local-First & Zero Telemetry

Runs entirely on your local machine using your existing LLM API credentials. Zero data telemetry, zero cloud egress.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Deep Research Engine — Stanford STORM & Drill-Down