quint-code

Existing project? Run /q-onboard after init — the agent scans your codebase for existing decisions worth capturing.

First time? Ask the agent to explain how it works:

/q-reason explain how to work with quint-code effectively — what commands exist, when to use each one, and what's the recommended workflow

The agent has full knowledge of all Quint tools and will walk you through them in context of your project.

How It Works

One command: /q-reason

Describe your problem. The agent frames it, generates alternatives, compares them fairly, and records the decision — all in one command. It auto-selects the right depth.

Or drive each step manually

/q-frame  → /q-char  → /q-explore → /q-compare → /q-decide
  what's      what       genuinely     fair         engineering
  broken?     matters?   different     comparison   contract
                         options

Micro-decisions on the fly

The agent captures decisions automatically when it notices them in conversation. No rationale — no record. Conflicts with active decisions are flagged. Auto-expires in 90 days.

When decisions go stale

/q-status shows what's expired and what needs attention. /q-refresh manages the lifecycle of ALL artifact types — waive, reopen, supersede, or deprecate.

What Makes It Different

• Decisions are live — they have computed trust scores (R_eff) that degrade as evidence ages. An expired benchmark drops the whole score.
• Comparison is honest — parity enforced, dimensions cross-checked, asymmetric scoring warned. Anti-Goodhart: tag dimensions as "observation" to prevent optimizing the wrong metric.
• Memory across sessions — when you frame a problem, the tool surfaces related past decisions. When you explore, it checks for similar variants.
• The loop closes — failed measurements suggest reopening. Evidence decay triggers review. Periodic refresh prompts ensure nothing goes stale silently.
• Decisions are contracts — FPF E.9 format: Problem Frame, Decision (invariants + DO/DON'T), Rationale, Consequences. A new engineer reads it 6 months later and gets everything.

6 Tools

| Tool | What it does | |------|-------------| | quint_note | Micro-decisions with validation + auto-expiry | | quint_problem | Frame problems, define comparison dimensions with roles | | quint_solution | Explore variants with diversity check, compare with parity | | quint_decision | FPF E.9 decision contract, impact measurement, evidence | | quint_refresh | Lifecycle management for all artifacts | | quint_query | Search, status dashboard, file-to-decision lookup |

Built on First Principles Framework

FPF by Anatoly Levenchuk — a rigorous, transdisciplinary architecture for thinking.

/q-reason gives your AI agent an FPF-native operating system for engineering decisions: problem framing before solutions, characterization before comparison, parity enforcement, evidence with congruence penalties, weakest-link assurance, and the lemniscate cycle that closes itself when evidence ages or measurements fail.

quint-code fpf search gives you access to 4243 indexed sections from the FPF specification — the agent can look up any concept on demand.

Learn More

See the documentation for detailed guides on decision modes, the DRR format, computed features, and lifecycle management.

Requirements

• Go 1.24+ (for building from source)
• Any MCP-capable AI tool

License

MIT