job-application-helper

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✨ Why agentic-stack?

Most "AI skills" are a single clever prompt. These are full multi-agent pipelines with the engineering that separates a demo from a tool:

• 🧪 Evidence in, judgment out. Research runs split evidence-gathering (parallel sub-agents) from judgment (a deterministic Python scoring engine for product decisions, an adversarial red-team for research). The conclusion is grounded — not vibes, not a hallucinated ranking.
• 🔁 They get smarter. ultradeep writes a transferable lesson back into its own program.md after every run. Your tooling compounds.
• 🗂️ They compound into a knowledge base. Reports file themselves into an optional Obsidian wiki, cross-linked and searchable next time.
• 🛡️ Privacy-first by design. Ships the engine, never your data. No secrets, no personal content — your wiki/memory start as empty scaffolds.
• ⚡ Zero-to-running in one command. An interactive installer wires the free MCP servers and asks what you want. Required pieces are free and key-less.

Built by @justsima — the agentic research workflow behind real deep-research, buying decisions, and high-stakes calls, packaged so you can run it too.

How it compares

	Single-prompt "skill"	Vanilla Claude Code	agentic-stack
Multi-agent fan-out	❌	⚠️ manual	✅
Adversarial red-team verification	❌	❌	✅
Deterministic scoring — no hallucinated rankings	❌	❌	✅
Self-learning across runs	❌	❌	✅
Files into a knowledge base	❌	❌	✅
One-command install + MCP wiring	—	—	✅
Privacy-first — ships the engine, not your data	—	—	✅

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🧩 The plugins