I built Alpha Insights: AI business research with validators, not just prompts

Source: Dev.to

Most AI research tools can summarize. That is not the hard part.
The hard part is making the model behave like a serious analyst when the context gets long, the evidence is messy, and the answer needs to support a real decision.

That is why I built Alpha Insights.

GitHub:

The problem

When you ask a raw AI model to do business research, the failure mode is usually subtle:

• It gives a clean answer before the research is actually done.
• It cites weak evidence with too much confidence.
• It skips framework steps when the context gets crowded.
• It mixes facts, assumptions, and recommendations into one fluent paragraph.
• It produces a report that looks finished, but is hard to audit.

In business analysis, that is dangerous. A polished answer is not the same thing as a decision‑ready answer.

What Alpha Insights does differently

Alpha Insights is an open‑source business analysis SKILL for Claude Code‑compatible runtimes and Codex Desktop. It is not a prompt pack; it is a research workflow with external constraints:

• 19 business frameworks – e.g., Porter’s Five Forces, Value Chain, SWOT, PESTEL, BCG Matrix, TAM/SAM/SOM, JTBD, Blue Ocean, Three Horizons, Flywheel, SCP, and more.
• 9 analyst methodologies – MECE, Issue Tree, Hypothesis‑Driven, Pyramid Principle, Triangulation, Pre‑Mortem, First Principles, ACH, Expert Interview.
• 10 research scenarios – industry research, competitive analysis, product analysis, business model teardown, opportunity discovery, market entry, investment decision, strategic planning, due diligence, ad‑hoc advisory.
• Evidence chain – conclusions are tied to source quality and confidence, instead of floating as polished prose.
• Multi‑track research – public sources, optional knowledge bases, optional internal data, and expert‑interview workflows.

The goal is simple: make AI stop acting like a generic summarizer and start following an analyst‑grade research process.

The technical idea: harness over prompt

The most important design decision in Alpha Insights V4 is this:

Prompt instructions are probabilistic. Harness checks are deterministic.

Alpha Insights adds a runtime harness around the AI workflow:

• A state machine tracks the research stage, tier, loaded frameworks, and deliverables.
• Stage‑gate validators check whether each step has actually produced the required artifacts.
• Hooks guard report generation, trigger gate checks, and persist progress incrementally.
• HTML write guards prevent the model from jumping straight to a final report before the evidence and insight stages are validated.
• Dual‑platform adapters support both Claude Code‑compatible runtimes and Codex Desktop.

This matters because agent quality problems are often execution problems, not wording problems. If the model can silently skip a stage, it eventually will. Without artifact boundaries, the report becomes unauditable. If evidence quality isn’t checked before recommendations, the output can look smart while resting on sand.

Why this may be useful beyond business research

Alpha Insights is a business analysis tool, but the engineering lesson is broader: for serious AI workflows we should stop relying only on better prompts.

A good agent should have:

• Explicit stages.
• Persistent intermediate artifacts.
• Validators before transitions.
• Source and confidence tracking.
• Hooks that enforce the boring‑but‑important parts.

That is the difference between “the model probably followed the instruction” and “the workflow can prove what happened.”

Install

For Codex Desktop

git clone https://github.com/Ericyoung-183/alpha-insights.git
cd alpha-insights
python3 scripts/install_codex.py --verify

For Claude Code‑compatible runtimes

Install the folder as a skill package, keep the root SKILL.md frontmatter hooks intact, then run:

python3 scripts/verify_cloudcode.py

Agent‑first installation

See INSTALL_FOR_AGENTS.md in the repository for the full guide.

Feedback welcome

Alpha Insights is open source and MIT licensed. If you are building AI agents, research workflows, or business‑analysis tools, I would love feedback on the harness design, the validator layer, and the dual‑platform installation path.

GitHub:

Stars are appreciated, but serious critique is even more useful.