AI skills that find the design decisions you didn't make.
mindit walks any design through eight forces every decision has to balance. Each force is its own skill. Each run produces a scored, structured artifact you can commit to your repo and grep six months later.
Layers gives you a thinking framework. mindit gives you eight numbers, a CLI, and an MCP server.
01New in v2
v1 was skills + schema. v2 is everything that makes them actually usable across tools, validatable across time, and inspectable when something goes wrong.
- cli
usemindit add,init,list,lint,mcp - mcp Eleven skills exposed as MCP tools over stdio (plus a
mindit_schematool) - lint Schema validation, depends_on resolution, orphan detection
- install Multi-tool support: Claude Code, Claude Desktop, Cursor, Codex, Cline
- tests 44 tests covering schema, fixtures, CLI, lint behavior, MCP roundtrips
02The eight forces
Every design decision balances eight competing pressures. mindit names them and scores each one. A finished design has a force profile; lopsided profiles fail. A design at 90 Clarity and 20 Correctability hasn't been thought through, it's been polished.
03The eleven skills
Eight skills for the eight forces, plus three meta skills that combine them.
.mindit/decisions/ backward and forward.04What it produces
Every skill writes two files to .mindit/decisions/: a markdown artifact for humans, and a JSON artifact that conforms to the decision schema. Diff-able in git. Queryable by tools. Traceable through the graph.
05Anti-pattern registry
Each force ships with a registry of named failure modes. 64 patterns across 8 forces, each with a symptom, an example, a fix, and a severity. When a finding matches a pattern, the slug goes in the artifact — so you can grep your decision graph for any anti-pattern.
buried-primary-action· clarityone-off-component· continuityaccessibility-afterthought· constraintone-way-door· consequencedesktop-prototype· contextsnowflake-feature· costn-of-one· confidencebig-bang-launch· correctability
06Install
One command. Most AI tools that read the SKILL.md convention pick the skills up automatically once the CLI has placed them.
07The CLI
Five commands. Zero config. Everything you need to install, list, validate, and serve mindit.
.mindit/decisions/ in the current project, ready to receive artifacts.08Lint your decision graph
Skills produce artifacts. Artifacts reference each other. Over months, that graph drifts. usemindit lint catches the drift before it becomes a mystery.
Checks performed on every .mindit/decisions/ artifact:
- Every JSON file parses
- Every JSON file validates against
schemas/decision.schema.json - Every JSON has a markdown sibling (and vice versa)
- Every
idfield matches its filename - Every
depends_onreference resolves to an existing artifact
Exit code 0 means clean, 1 means errors, 2 means warnings only. Wire it into your CI.
09MCP server
Run usemindit mcp to expose all eleven skills as MCP tools over stdio. Compatible with Claude Desktop, Cursor's MCP support, and any other MCP client.
Twelve tools advertised: mindit_clarity, mindit_continuity, ..., mindit_audit, mindit_premortem, mindit_trace, plus mindit_schema which returns the JSON Schema for client-side validation.
Restart Claude Desktop. The eleven mindit tools appear in your tool list. Cursor, Cline, and other MCP clients work the same way — point them at npx usemindit mcp.
10Repository layout
11How mindit differs from Layers
Layers is a thinking framework: seven layers, three zones, nine markdown files. It's good. mindit is a different tool with four concrete additions.
Scoring per force, not just prose
Every force gets a 0–100 score per artifact. Not for ranking designs against each other. For surfacing which force is weakest in a given design. A 90/20 profile fails at the 20.
Anti-pattern registry
64 named failure modes. Findings carry slugs. Greppable across your decision graph.
Decision graph
Artifacts reference each other through depends_on. mindit-trace walks the graph backward and forward. Decisions accumulate; the team's reasoning becomes legible six months later.
Tooling
A CLI for one-command installation across five AI tools. An MCP server for tools that prefer that integration. A schema-based linter to catch graph drift over time.
Both are MIT, both work with the SKILL.md convention. You can use both at the same time.