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Nimi Coding ​

Nimi Coding is a vendor-neutral AI-native methodology product for governing high-risk AI-assisted software work. It ships as a standalone npm package (@nimiplatform/nimi-coding), bootstraps a project-local .nimi/** truth surface into any repository, and turns "AI plausibly finished this" into "the four closure dimensions are evidenced."

Nimi Coding is one of the products inside the Nimi platform — the AI development methodology that ships with everything else. It can also be adopted on its own: the package is host-agnostic and works in any repository, regardless of whether the rest of the Nimi platform is in use.

Nimi Coding and the rest of the platform stress-test each other. Nimi Coding is what makes a system as ambitious as Nimi buildable by a small team using AI; the platform's actual scale is what makes Nimi Coding's claims falsifiable in practice.

Why This Section Exists ​

Most AI products solve "AI in the editor." Nimi Coding solves "how does anyone trust the work AI did?" The answer is not better prompts and not better tests. It is methodology — explicit machinery for declaring closure conditions before work begins, and verifying them as evidence after work ends.

If you have ever watched an AI-assisted change look complete to every available signal — type checker green, tests green, code review approved — and turn out to be wrong about authority, scope, or product meaning, this section is for you.

Start Here If You Are New ​

The first successful Nimi Coding path is intentionally bounded:

  1. Install the package in an existing repository. See Installation.
  2. Bootstrap .nimi/. Use nimicoding start, then check the result with nimicoding doctor --json.
  3. Hand off spec reconstruction with nimicoding handoff --skill spec_reconstruction --json.
  4. Let the admitted AI host reconstruct authority into .nimi/spec/**, recording source basis and unresolved gaps instead of inventing clean rules.
  5. Validate the result with nimicoding validate-spec-tree .nimi/spec and nimicoding validate-spec-audit.

That path gives the project a local truth surface and mechanical validators. It does not require topics, waves, packets, or high-risk gates. Those come later, when the work is authority-bearing, cross-module, multi-wave, or audit-sensitive.

When work becomes high-risk, the escalation path is explicit: create a topic, split it into waves, freeze a packet before worker execution, run or hand off work through an admitted host, and close only when authority, semantic, consumer, and drift-resistance closure all hold.

What's In This Section ​

The Paradigm ​

  • The Paradigm — what's actually new about AI-coding governance and why this is a paradigm rather than a checklist.
  • Four Closures — authority, semantic, consumer, and drift-resistance closure as a thinking framework.
  • False Closure Typology — the named failure shapes the methodology catches.
  • Forbidden Shortcuts — the catalog of refused anti-patterns.

Roles And Convergence ​

Lifecycle ​

  • Topic Lifecycle — proposal, ongoing, pending, closed; wave fine-grained states; true close.
  • Whitepaper — the conceptual case argument for treating AI-assisted implementation as authority-bearing work.
  • Topic Workflow — the operational topic / wave / packet / preflight / audit / closeout flow.
  • Walkthrough — a synthetic example end-to-end.

The Package ​

  • The Package — what @nimiplatform/nimi-coding ships, what it does not ship.
  • Host-Agnostic Boundary — why switching AI hosts does not change the methodology.
  • Skills — the four declared skills (spec_reconstruction, doc_spec_audit, audit_sweep, high_risk_execution).
  • CLI Surface — concept-level overview of the command surface.
  • Installation — current installation posture.

Comparison And Adoption ​

  • Comparison — vs vanilla AI coding, code review, DevOps governance, DDD, agile.
  • Adoption Path — who would adopt this and why.

Practical Sub-Trees ​

  • Tutorials — learning-oriented step-by-step lessons, including the full path from install to .nimi/spec/**, topic execution, sweep audit, sweep design, and long-running host work.
  • How-to — problem-shaped recipes.
  • Reference — schema-level dictionary.

Appendix ​

Source Basis ​

Nimi AI open world platform documentation.

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