Deep Intelligence & Causal Codebase Forensics
What this bundle does:
Combines advanced external knowledge synthesis with deterministic internal codebase debugging, bridging the gap between strategic research and deep code forensic investigation.
The Enabled Workflow:
- Deep Research Engine executes multi-agent Stanford STORM research protocols with iterative search refinement, perspective generation, and structured engineering summaries.
- Temporal Git Forensics performs zero-dirtying time-travel state reconstruction, isolating regression root causes via automated git-bisect and generating targeted remediation branches.
Who it is for:
Lead developers, architects, and technical consultants responsible for solving complex technical decisions, architecture planning, and tracking down elusive legacy code regressions.
Works with any agent that signs in to Agensi, such as Claude, ChatGPT, Claude Code, Cursor and Codex CLI.
One payment, lifetime access. 2 skills unlock instantly in your library.
What's included
2 skills
Autonomous deep research framework with Stanford STORM multi-perspective synthesis and 5-layer drill-down.
Causal Git forensics, zero-dirtying time-travel state reconstruction, regression bisect pinning, and hotfix patch generator.
See it in action
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()
);
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads these skills and uses them on your task. It always asks before any payment.
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