Whats your thoughts on the concept of https://github.com/Lasse-numerous/prisme
Tænkte i 46s

From what’s publicly described in the repo + docs, Prisme’s concept is strong and very “you”: spec-as-code in Python/Pydantic that generates the boring-but-fragile 80% of a product (CRUD + list endpoints + filters/sort/pagination + UI + tests), while explicitly trying to preserve downstream customization across regenerations.

Here are my main thoughts—what feels uniquely compelling, what’s risky, and what I’d focus on next.

What I think is genuinely compelling
1) “Spec as Code” in Pydantic is a great wedge

You’re not inventing a new DSL—your “spec” is just Python. That’s a big adoption win for teams already living in Pydantic/FastAPI land.

2) Full-stack generation including MCP is a timely differentiator

A lot of generators stop at OpenAPI/clients, or at backend scaffolding. You’re saying: generate REST + GraphQL + MCP + React components + tests from the same spec.
That “MCP as a first-class exposure target” is new enough that it can be a clear identity, if you lean into it.

3) The CLI workflow is crisp and demo-friendly

create → install → generate → test → dev is exactly the kind of “voila, you have it” flow you like.
Also: templates (minimal, api-only, saas) are the right move for getting to outcomes fast.

4) You picked an “industrial-grade but approachable” stack

FastAPI + SQLAlchemy (async) + Alembic + React/Vite/TS/Tailwind is a very pragmatic default that many teams will accept without bikeshedding.
And calling out Docker + Terraform + Hetzner as a target is consistent with your EU-cloud positioning.

The two big risks I see
1) The “preserve customization” promise is the whole game

You explicitly promise “Extend, Don’t Overwrite” / “without losing your customizations on regeneration.”
That’s the hardest part of codegen frameworks in practice. People will try it, tweak something, regenerate, and if it ever hurts them once they churn forever.

What I’d do: make your customization mechanism extremely legible and hard to misuse:

a very explicit “generated/” vs “user/” boundary (or partial classes / dependency injection points)

“escape hatches” that feel intentional (hooks, signals, dependency overrides)

golden tests that prove regeneration stability across versions (this becomes your credibility engine)

2) Scope creep via “selective exposure”

You support fine-grained control across REST, GraphQL, MCP, and frontend exposure.
That’s powerful—but it also multiplies permutations and “why is this not generated?” confusion.

What I’d do: choose one “happy path” you optimize for (and market), and treat the rest as advanced. For example:

Happy path: REST + frontend (or GraphQL + frontend) + MCP

Advanced: turning layers on/off, custom policies, exotic fields, etc.

One positioning thing to tighten

On GitHub the README header shows “Prism”, while the docs and package name are “Prisme / prisme” (and the tagline is consistent).
It’s minor, but early on naming consistency reduces friction (also “Prism” is a crowded name in OSS).

Where I’d focus next (highest ROI)

Nail the “regen without fear” story with one killer example
A tiny demo app where you:

generate everything

make 2–3 realistic custom changes (backend logic + UI tweak)

regenerate

show diffs are clean / changes preserved
This is the moment people decide whether Prisme is “real”.

Make the list/filter/sort/pagination ergonomics excellent
You already call it out prominently.
If your list endpoints + UI table are noticeably better than what people hack together, that becomes a repeatable reason to adopt.

Lean into MCP as a first-class product outcome
Your docs explicitly call out generating MCP tools.
I’d make a page that shows: “your spec becomes an agent-ready tool surface” with a couple of concrete examples.

Make “SaaS template with auth” a flagship path
You have --template saas.
If that template feels production-oriented (tenancy boundaries, roles, audit basics, etc.), it becomes your bridge to the “ship internal tools as products” world.

If you want, paste (or point me to) one example spec file you think represents the intended sweet spot (a real-ish model set, not the toy CRM), and I’ll give super concrete feedback on:

spec ergonomics (what’s missing / too verbose)

what the generated API/UI will feel like to an end user

where customization hooks will be needed in practice

I feel there is a confusion between whats cli cmd and flags, whats spec, what in Prism Config.

Yeah — your instinct is 100% right.
Right now Prisme is conceptually sound, but mentally expensive because users don’t have a stable mental model of where decisions live.

What you’re seeing is classic boundary confusion:

“Should I change a flag, the spec, or the config… and what happens if I regenerate?”

Let me try to untangle it cleanly and then give you a crisp contract you can adopt.

The root problem (in one sentence)

Prisme currently mixes execution-time decisions, generation-time structure, and product intent across CLI flags, spec, and config — without a single authoritative hierarchy.

Users don’t know:

what is ephemeral

what is regeneratable

what is stable & versioned

The model that will fix this

You want one mental sentence users can repeat:

CLI = when / how
Spec = what
Config = where / defaults

Let’s make that real.

1️⃣ CLI commands & flags

Purpose: execution, never intent

CLI answers: “What am I doing right now?”

Rules

CLI must not define product shape

CLI flags are ephemeral

Re-running a command with different flags must never invalidate a repo

Allowed responsibilities
CLI	Meaning
prisme create	scaffold repo
prisme generate	run generator
prisme test	run tests
prisme dev	start dev server
Flags should be only:

paths

verbosity

dry-run

force

watch

target env (dev/prod)

❌ CLI flags should NOT control:

API exposure (REST/GraphQL/MCP)

auth behavior

UI inclusion

model semantics

multi-tenancy

If a flag affects generated code → it’s in the wrong layer.

2️⃣ Spec (Pydantic models)

Purpose: product truth

Spec answers: “What does my product mean?”

This is your crown jewel. The spec should be:

declarative

deterministic

safe to regenerate forever

diffable in Git

portable between projects

Spec should define

domain models

relationships

field semantics

exposure intent (but not mechanics)

Example (conceptual):

class Project(Model):
    name: str
    status: Status

    __expose__ = {
        "read": True,
        "write": True,
        "list": True,
        "mcp": True,
    }


Notice:

No ports

No framework choices

No auth providers

No UI framework toggles

The spec says:

“This concept exists and should be exposed.”

Not how.

3️⃣ Prisme Config (prisme.toml / prisme.yaml)

Purpose: policy & defaults

Config answers: “How is this product realized here?”

This is where most current confusion lives.

Config should be:

environment-aware

generator-aware

infra-aware

overridable per environment

not domain-aware

Config owns:
Category	Examples
Backend	FastAPI, SQLAlchemy, async
Frontend	React, Tailwind
Exposure defaults	REST on, MCP on
Auth strategy	JWT, Keycloak
DB	Postgres
UI behavior	tables/forms enabled
Generator policies	overwrite rules

Example:

[backend]
framework = "fastapi"

[exposure]
rest = true
graphql = false
mcp = true

[frontend]
enabled = true
tables = true
forms = true

[auth]
provider = "keycloak"


This means:

Spec says what could exist

Config says what is enabled here

4️⃣ The missing piece: precedence contract

This is the part you should literally document as a box:

CLI flags > Config > Spec defaults


But with strict constraints:

CLI can only override execution behavior

Config can override generation defaults

Spec is never overridden, only interpreted

If two layers overlap → that’s a design smell.

5️⃣ A simple user rule that prevents 90% of confusion

❓ “Will this change survive regeneration?”

Yes → Spec or Config

No → CLI

❓ “Is this about meaning or infrastructure?”

Meaning → Spec

Infrastructure → Config

❓ “Should this be different in prod vs dev?”

Yes → Config

No → Spec

6️⃣ What I’d change concretely in Prisme (short list)

Remove any CLI flag that affects generated output

or deprecate them with warnings

Make spec exposure expressive but minimal

“exposed / not exposed”

“read/write/list”

no framework detail

Make prisme.toml mandatory

even for minimal

empty is fine, but visible

Add a one-page “Mental Model” doc
Title it something like:

“Where do things live in Prisme?”

Add a regen contract test

change spec → regen → no user files touched

change config → regen → only generated layer changes

7️⃣ Why this matters for you specifically

Given your background and the people you’re building for:

R&D engineers hate magic

SaaS builders fear regeneration

Platform teams need ownership boundaries

Prisme wins if it feels:

“Boring, predictable, and safe to rerun.”

That only happens when the layers are mentally unambiguous.

If you want, next we can:

rewrite your README intro around this model

design the exact schema for prisme.toml

or refactor one existing CLI command as a canonical example

This is a very solvable problem — and fixing it will noticeably increase adoption.

Please review and point out concrete differences between your model and current state
Tænkte i 50s

Here are concrete, source-backed differences between the “CLI/spec/config contract” I proposed and what Prisme is doing today, based on the current README.

1) Your “Spec” is not just “what” — it also contains “how” knobs
My model

Spec = product truth (“what exists / what it means”)

Avoid framework-ish toggles and delivery targets in spec (REST/GraphQL/MCP/UI) except maybe as high-level intent.

Current state

Your spec example is a DSL object graph (StackSpec, ModelSpec, FieldSpec, etc.) and it includes delivery-surface decisions and UI mechanics inside the model definitions:

rest=RESTExposure(...)

graphql=GraphQLExposure(...)

mcp=MCPExposure(...)

frontend=FrontendExposure(...)

plus field UI details like ui_widget="email", and query behavior like filter_operators=[...]

Concrete difference: In today’s Prisme, a user must decide “is this model exposed via MCP and what prefix?” inside the spec, whereas in my model those toggles live in config defaults (with spec optionally saying “this can be exposed”).

2) Your CLI flags currently include durable “project intent” (not just execution-time choices)
My model

CLI flags = ephemeral “how do I run this command right now?”

Durable choices (db choice, package managers, templates/features) should land in config and be re-runnable consistently.

Current state

prism create includes flags that change durable aspects of the project:

--template minimal | api-only | saas

--database sqlite

--package-manager pnpm

--python-manager poetry

--docker

and more

Some of these are fine as CLI conveniences for initial scaffolding, but the confusion you feel is real because they look like “this is how my system works” rather than “this is a one-time creation choice”.

Concrete difference: In today’s Prisme, the same kind of decision (e.g., exposure/auth/stack shape) can plausibly be expressed as:

a create --template ... flag, and/or

something in the spec (exposures), and/or

something in generated code/config files
…with no single “source of truth” hierarchy stated.

3) “Config” appears to be generated Python files, not a stable Prisme config artifact
My model

A single explicit config file (e.g., prisme.toml) is the stable home for “where/defaults/policy”.

generate consumes spec + config deterministically.

Current state

The README explicitly says prism generate requires core backend files (__init__.py, main.py, config.py, database.py) that are created by prism create.

That implies your “config” concept is currently:

partly embedded in the scaffolded backend code (config.py etc.)

and not clearly separated as “Prisme configuration” vs “generated app configuration”.

Concrete difference: In my model, you can always point to one file as “Prisme config”. In the current state, “config” is split across scaffolded Python files whose role isn’t obviously “Prisme config” to the user.

4) generate is coupled to create (workflow coupling = boundary confusion)
My model

generate should work anywhere given:

a spec

a config

(optionally) a target directory

scaffolding is a convenience, not a prerequisite.

Current state

You have a prominent warning that generate depends on files created by create, and manual setup must recreate them.

Concrete difference: Today, users may interpret create as part of “generation semantics” (not just scaffolding), because without it generate fails. That blurs “CLI execution workflow” vs “project definition”.

5) There’s no clearly documented precedence contract between “flags vs spec vs config”
My model

Publish a small precedence rule-set (and enforce it):

CLI can only override runtime/execution behavior

Config selects defaults/policies/targets

Spec defines domain truth

Current state

From the README alone, a user sees:

many durable knobs in create flags

many exposure knobs in the spec

plus required backend config.py/database.py created by create
…but no explicit “this lives here” contract or precedence rule is visible.

Concrete difference: In my model, a user always knows “change X here.” In current Prisme, it’s easy to ask “should this be a CLI flag, a spec attribute, or a config.py edit?”

If you want to align to the model with minimal churn (very concrete)

You don’t have to rewrite everything. The smallest “de-confusion” move is:

Introduce prisme.toml generated by create

It records durable choices currently passed as flags (database, package-manager, python-manager, template, exposure defaults, etc.)

Keep create --database sqlite etc, but make them write into prisme.toml

Make generate read from prisme.toml and fail with a helpful message if missing (“run prism init or create one”)

Optionally let spec keep per-model exposure overrides, but move the default surfaces to config:

config: exposure.mcp=true

spec: Customer.mcp.enabled=false only when deviating

That one change makes the boundaries visibly real.

If you point me at one of your existing project templates (minimal/api-only/saas) and the files it drops (even just a tree listing), I can map exactly which options should move into prisme.toml vs stay as CLI vs stay in spec, in a concrete table.

Please create a Review document with all this content
Prisme – Cli / Spec / Config Boundary Review
Prisme Review: CLI vs Spec vs Config Boundaries
Purpose of this document

This document reviews the current state of Prisme and contrasts it with a clear conceptual model for separating responsibilities between:

CLI commands & flags

Spec (Pydantic-based domain specification)

Prisme configuration

The goal is to:

Reduce user confusion

Establish a stable mental model

Make regeneration safe, predictable, and boring

This is not a criticism of the concept (which is strong), but a concrete boundary review with actionable alignment suggestions.

Target Mental Model (Reference)

One-sentence rule:

CLI = when / how
Spec = what
Config = where / defaults / policy

Layer responsibilities
Layer	Answers	Persistence	Regen-safe
CLI	What am I doing right now?	No	N/A
Spec	What does my product mean?	Yes (Git)	Yes
Config	How is it realized here?	Yes (Git)	Yes
Precedence contract
CLI flags  >  Config  >  Spec defaults

With strict constraints:

CLI may only affect execution/runtime behavior

Config selects generators, defaults, policies

Spec is never overridden — only interpreted

Current State Review (Concrete Differences)
1. Spec currently mixes "what" and "how"
Intended (target model)

Spec defines:

Domain models

Fields & relationships

High-level exposure intent

Spec avoids:

Framework mechanics

Delivery targets (REST/GraphQL/MCP/UI) as detailed configs

Current Prisme state

The spec DSL (StackSpec, ModelSpec, FieldSpec, etc.) contains delivery-surface mechanics and UI behavior, e.g.:

rest=RESTExposure(...)

graphql=GraphQLExposure(...)

mcp=MCPExposure(...)

frontend=FrontendExposure(...)

UI-level details such as ui_widget, filter operators, sorting behavior

Concrete difference:

Today, a user decides "is this model exposed via MCP and with what prefix" inside the spec.

In the target model, the spec would say "this model can be exposed", while config decides where it is actually enabled.

2. CLI flags encode durable project intent
Intended

CLI flags are ephemeral

They control execution (paths, verbosity, watch, force)

Re-running a command with different flags should not redefine the project

Current Prisme state

prism create includes flags that encode durable architectural intent:

--template minimal | api-only | saas

--database sqlite

--package-manager pnpm

--python-manager poetry

--docker

These choices materially affect the long-term structure of the project.

Concrete difference:

The same category of decision (stack shape, exposure, infra choices) can currently appear as:

a CLI flag

a spec attribute

or a generated Python config file

There is no single visible source of truth.

3. No explicit Prisme config artifact exists
Intended

A single, explicit Prisme config file (e.g. prisme.toml)

Contains:

generator defaults

exposure defaults

backend/frontend choices

auth strategy

database policy

Current Prisme state

Configuration is implicit and scattered:

config.py, database.py, etc. generated by create

These files mix app runtime configuration with generator assumptions

Concrete difference:

Users cannot point to one file and say: "this is my Prisme configuration".

The boundary between generated app code and Prisme policy is blurred.

4. generate is coupled to create
Intended

generate should operate on:

a spec

a config

a target directory

Scaffolding should be optional

Current Prisme state

prism generate depends on backend files created by prism create

Manual setup must recreate those files

Concrete difference:

Users may interpret create as part of generation semantics, not just scaffolding

This blurs workflow vs definition

5. No documented precedence contract
Intended

Users should always know:

"This belongs in the spec"

"This belongs in config"

"This is a CLI-only concern"

Current Prisme state

Multiple overlapping control surfaces exist

No explicit hierarchy or override rules are stated

Concrete difference:

Users must guess where a change should live

This directly causes regeneration anxiety

Minimal Alignment Strategy (Low Churn)

The following steps align Prisme with the target model without rewriting core concepts:

1. Introduce prisme.toml

Generated by prism create, versioned in Git.

Owns durable choices currently passed as flags:

template

database

package manager

python manager

exposure defaults

backend/frontend enablement

auth strategy

CLI flags become writers to config, not parallel truth.

2. Narrow the spec’s responsibility

Keep spec expressive for domain meaning

Reduce exposure details to:

enabled / disabled

read / write / list intent

Defaults live in config; spec only overrides when deviating.

3. Make config mandatory

Even an empty prisme.toml is valid

generate fails with a helpful error if missing

4. Document the mental model explicitly

Add a short doc titled:

Where does this live in Prisme?

Include:

Layer definitions

Precedence rules

Regeneration guarantees

5. Add a regeneration contract test

Automated proof that:

Spec changes regenerate generated layers

User code is preserved

Config changes only affect generated layers

This becomes a trust anchor.

Why this matters

Prisme’s core promise is:

"Generate fast — extend safely — regenerate without fear."

That promise only holds if:

boundaries are explicit

ownership is obvious

regeneration is predictable

Clarifying CLI vs Spec vs Config is not cosmetic — it is foundational to adoption, especially for:

R&D engineers

platform teams

internal SaaS builders

Summary

The concept is strong and timely

Confusion comes from boundary overlap, not feature complexity

Introducing an explicit config layer and precedence contract resolves most friction

This can be done incrementally with minimal churn

This document should be treated as a design contract, not just documentation.

Done. I’ve created a full Review document in the canvas titled:

“Prisme – CLI / Spec / Config Boundary Review”

It:

Captures the target mental model

Documents concrete differences vs current Prisme

Explains why the confusion exists (with examples)

Proposes a low-churn alignment strategy

Reads like a design contract, not a chat summary

Next good steps (pick one):

Convert this into an ADR (Architecture Decision Record)

Use it to refactor the README intro

Derive a concrete prisme.toml schema from it

Map one existing template (e.g. saas) line-by-line to Spec vs Config vs CLI

If you want, say “turn this into an ADR” or “derive prisme.toml from this” and I’ll do it cleanly and mechanically.

Could a refactor go together with introducing a versioning of the spec and config files?

Yes — and it’s one of the best ways to make the refactor safe.

If you introduce explicit versioning for both spec + config, you get three big wins during (and after) the boundary refactor:

you can evolve the schema without breaking old projects

you can auto-migrate (or at least detect + explain)

you can make regeneration deterministic (“this project is on spec v2 / config v1”)

Here’s a concrete way to do it that fits Prisme’s shape.

The core idea
Version both artifacts independently

Spec schema version (what the spec means)

Config schema version (how generation is configured)

They won’t always change together.

What it looks like
Config: version header inside prisme.toml

Simple and obvious:

prisme_version = "0.4.0"
config_version = 1

[backend]
framework = "fastapi"

[exposure]
rest = true
graphql = false
mcp = true


config_version is the schema version for this file

prisme_version is the generator version used last (optional but very helpful for debugging)

Spec: version as a constant / attribute

Because your spec is Python, you can keep it lightweight:

PRISME_SPEC_VERSION = 2

stack = StackSpec(
    name="acme",
    # ...
)


Or if you prefer everything “inside the model”:

stack = StackSpec(
    meta=SpecMeta(version=2),
    # ...
)


Either is fine; the constant is simplest.

How versioning helps the refactor specifically

You’re about to move “knobs” across layers (CLI → config, spec exposures → config defaults, etc.).
That’s exactly when you want:

hard detection: “This is an old spec shape”

guided migration: “Run prisme migrate”

safe failure: “Can’t migrate automatically, here’s the diff”

So you can do the refactor in phases without breaking existing users.

A very workable migration strategy
1) Add versions first (no behavior change)

Parse spec/config versions

Print warnings only:

“No config_version found → assuming v0”

“No spec version found → assuming v0”

This alone reduces fear: users see you have a plan.

2) Add a prisme migrate command

Even if it only supports “migrate v0→v1” initially.

Examples:

prisme migrate --dry-run

prisme migrate --write

3) Make generate enforce compatibility

Rules like:

If config_version is too old → fail with “run migrate”

If spec version is unknown → fail with explanation

This is how you avoid silent footguns.

What “migrations” actually do (concretely)
Config migrations

This is the easiest and highest ROI.

Example v0 → v1:

take what used to be implicit in scaffolded config.py / create flags

write it into prisme.toml

So migration might:

detect poetry.lock → set python_manager="poetry"

detect pnpm-lock.yaml → set package_manager="pnpm"

detect docker-compose.yml → set docker=true

detect SQLite URL in app config → set database="sqlite"

Even if you can’t detect everything, you can:

fill what you can

emit TODOs for the rest

Spec migrations

Harder, but you can still do useful things.

Typical v0 → v1 move:

rename fields

change exposure model shape

move defaults out of spec into config

You can support:

automatic transformations where obvious

semi-automatic ones: write a new spec file next to the old one and comment “manual step here”

manual with a precise error message

Strong recommendation: separate “schema version” from “generator version”

Schema version: integer, stable, low-frequency (config_version = 1)

Generator version: SemVer of Prisme (prisme_version = "0.4.0")

Why:

You can release bugfixes without schema bumps

You can bump schema only when needed

How this ties into “regen without fear”

Once versions exist, you can guarantee:

generate is deterministic for (spec_version, config_version, prisme_version)

you can store those three in generated metadata (e.g., generated/.prisme-manifest.json)

you can support “this project was generated by 0.3.2 with spec v1”

That’s the kind of boring traceability that platform engineers love.

Minimal contract you can publish

Every project has:

prisme.toml with config_version

spec file with PRISME_SPEC_VERSION

prisme migrate upgrades them

prisme generate refuses unknown/unsupported versions

That’s it. Simple.

If you want, I can also update the Review document in the canvas with a new section:
“Versioning & Migration Plan (Spec + Config)”
…and propose concrete version numbers for your likely next refactor (e.g., config v1 + spec v2).

Any other concepts to include in this refactor? Please include in a new document
Prisme Refactor Additions – Versioning, Boundaries, And Stability Concepts
Prisme Refactor Additions: Concepts Worth Including
Purpose

This document proposes additional concepts to include alongside the CLI/spec/config boundary refactor and the introduction of spec/config versioning.

The intent is to make Prisme:

easier to learn

safer to regenerate

easier to evolve without breaking projects

more trustworthy for platform/R&D teams

1) Project Manifest and Provenance
Concept

Generate a small machine-readable manifest that records what Prisme used when generating code.

Example: generated/.prisme-manifest.json

Store:

prisme_version

config_version

spec_version

hash of spec file(s)

hash of config file

generation timestamp

enabled generators (rest/graphql/mcp/frontend)

Why include it

Debugging: "why did this change?"

Reproducibility: deterministic generation

Support: easy issue triage

2) Explicit Ownership Boundaries in the Repo
Concept

Make the boundary between generated and user-owned code impossible to misunderstand.

Common patterns:

generated/ vs app/ (user code)

or src/generated/ + src/custom/

plus a clear policy: generated is replaceable, custom is never touched

Enforce it

Generators should only write inside generated/

Create-time scaffolding for user code should be separate and stable

Why include it

This is the main trust lever behind “regen without fear.”

3) Stable “Extension Points” (Hooks) as a First-Class Feature
Concept

Instead of allowing edits "anywhere", provide supported hooks that are preserved across regeneration.

Examples:

Backend:

dependencies.py overrides (FastAPI DI)

routers_override.py for adding endpoints

events.py for startup/shutdown

policies.py for auth/authorization rules

Frontend:

components_override/

per-page extension zones

table cell renderers / form field wrappers

MCP:

tool pre/post hooks

policy hooks (who can call what)

Why include it

Users will customize. If you don’t define where, they will customize everywhere, and regeneration becomes scary.

4) “Strict Mode” vs “Lenient Mode” for Generation
Concept

Add a policy switch in config to control how strict generation should be.

Strict: fail if unknown keys / unsupported versions / ambiguous paths

Lenient: warn and proceed with safe defaults

Why include it

Strict mode is excellent for teams and CI

Lenient mode is excellent for onboarding and experimentation

5) Compatibility Matrix and Capability Flags
Concept

Add an explicit compatibility layer:

which generators support which spec/config versions

optional “capability flags” that let Prisme say: "this project uses feature X"

Examples:

capabilities = ["rbac", "tenancy", "mcp-tools", "frontend-forms"]

Why include it

Prevents “silent downgrade” when users switch templates or upgrade Prisme.

6) Deterministic Output and Formatting Canonicalization
Concept

Ensure generation results are stable across runs:

deterministic ordering (models, fields, endpoints)

deterministic file layout

canonical formatting

Optionally:

run formatters automatically as part of generate (configurable)

generate “stable IDs” for models/fields used in UI keys

Why include it

Determinism reduces noisy diffs and increases trust.

7) Two-Phase Generation: Plan + Apply
Concept

Introduce a plan/apply workflow similar to Terraform.

prisme plan produces:

list of files to create/modify

a diff summary

a risk classification (safe vs risky)

prisme apply executes

Also enable:

--dry-run

--diff

Why include it

This directly addresses regeneration fear, especially in teams.

8) Schema Migration Story: Spec/Config + Database
Concept

You’ll likely have three “schema layers” over time:

Spec schema version

Config schema version

Database schema migrations (Alembic)

Align them intentionally:

spec/config migration should not silently mutate the DB

DB migrations should be explicit and reviewable

Optionally:

generate alembic migrations, but require a plan/apply step

Why include it

It prevents unintended data-loss or schema drift.

9) Multi-File Spec Support (Modularity)
Concept

Allow spec composition without one massive file.

Examples:

spec/stack.py

spec/models/customer.py

spec/models/invoice.py

Then load via config:

spec_paths = ["spec"]

Why include it

scales with real projects

enables ownership split (teams can own subdomains)

10) Clear Error Taxonomy and Fix-It Messages
Concept

Error messages should answer:

what happened

where it lives (CLI vs config vs spec)

what the user should do next

Introduce explicit error classes:

ConfigVersionTooOld

SpecVersionUnsupported

SpecValidationError

UnsafeWriteOutsideGenerated

Why include it

This is the fastest route to reducing support burden and user churn.

11) “Project Doctor” Command
Concept

Add prisme doctor to diagnose:

missing config/spec versions

mismatched package managers

wrong folder layout

missing migrations

broken invariants (generated files edited)

Why include it

It improves onboarding and is an anchor for CI checks.

12) Golden Examples and Regression Tests for Regen Guarantees
Concept

Keep 1–3 canonical example projects in-repo or in CI fixtures.

Test scenarios:

generate → edit user files → regenerate → user files unchanged

change spec → only generated layer changes

change config → only generated layer changes

migrate v0→v1 → generate succeeds

Why include it

These tests become your credibility engine.

13) Lockfile / Dependency Policy
Concept

Decide where dependency management lives:

config declares chosen manager (poetry/uv/pip)

generators produce lockfile policy accordingly

Add policy:

either generate lockfiles, or treat them as user-owned

Why include it

This reduces churn and confusion in real team workflows.

14) Security and Auth as Policy Modules
Concept

Treat auth/authorization as policy modules rather than scattered toggles.

config selects provider/integration (e.g., keycloak, local dev)

spec may annotate resources with required permissions

generated code uses stable policy hooks

Why include it

Security concerns can easily become cross-cutting chaos without a policy spine.

Suggested Minimal “Additions Bundle” (If You Want Low Churn)

If you want the highest ROI with minimal complexity, consider bundling these with the boundary + versioning refactor:

Explicit repo ownership boundary (generated vs user)

Manifest/provenance (.prisme-manifest.json)

Plan/apply (or at least plan + dry-run diff)

Doctor command

Golden regen tests

These five together materially increase trust and reduce confusion.

Closing note

The central theme across all concepts is the same:

Make Prisme predictable. Make boundaries explicit. Make changes reviewable.

That is how you turn an impressive generator into a platform people bet real work on.

Done — I created a new document in the canvas:

“Prisme Refactor Additions – Versioning, Boundaries, and Stability Concepts”

It includes a curated set of additional refactor concepts, with rationale and concrete examples, including:

project manifest/provenance

explicit generated vs user-owned boundaries

first-class extension hooks

strict vs lenient generation modes

deterministic output + canonicalization

plan/apply workflow (diff-first)

migration alignment (spec/config vs DB)

multi-file spec modularity

error taxonomy + fix-it messages

prisme doctor

golden regen tests

dependency/lockfile policy

security/auth as policy modules

If you want, I can also produce a proposed phased roadmap (v0.1 / v0.2 / v0.3) based on that document, optimized for “max trust per unit of work.”

Please make one combine refactor spec suggestion I can copy paste

Perfect — below is a single, cohesive “Refactor Spec” you can copy-paste as-is into a PR, issue, ADR, or planning doc.

It intentionally reads like a design contract, not a brainstorm.

Prisme Refactor Spec

CLI / Spec / Config Boundaries + Versioning + Regeneration Safety

Goal

Refactor Prisme to establish clear ownership boundaries, enable safe regeneration, and allow future evolution without breaking existing projects, while keeping churn low.

This refactor formalizes:

separation of concerns between CLI, Spec, and Config

explicit versioning of spec and config

regeneration guarantees

migration and provenance mechanisms

Core Mental Model (Non-Negotiable)

CLI = when / how
Spec = what
Config = where / defaults / policy

Layer responsibilities
Layer	Owns	Persistence	Regen-safe
CLI	Execution behavior	No	N/A
Spec	Domain meaning	Yes (Git)	Yes
Config	Generator & policy defaults	Yes (Git)	Yes
Precedence contract
CLI flags  >  Config  >  Spec defaults


Constraints:

CLI flags may not affect generated output shape

Config selects generators, defaults, policies

Spec is never overridden — only interpreted

1. CLI Contract
Allowed responsibilities

CLI commands and flags may only control:

paths

verbosity

watch / dry-run

force

environment selection (dev/prod)

plan vs apply

Disallowed responsibilities

CLI must not control:

API exposure (REST / GraphQL / MCP)

UI inclusion

auth strategy

database choice

project architecture

Implication

All durable project decisions currently passed via prisme create flags must be written into config, not retained as parallel truth.

2. Spec Contract (Pydantic)
Purpose

The spec defines domain truth.

It answers:

“What exists, and what does it mean?”

Spec may define

models and fields

relationships

validation

high-level exposure intent (enabled / disabled)

read / write / list semantics

Spec must not define

framework details

delivery mechanics

infra or auth providers

UI framework behavior

Versioning

Each spec declares a schema version:

PRISME_SPEC_VERSION = 2


or equivalently via metadata.

3. Config Contract (prisme.toml)
Purpose

Config defines how the spec is realized in this project.

It answers:

“Where, how, and with which defaults is this product generated?”

Config owns

backend/frontend enablement

exposure defaults (REST / GraphQL / MCP)

auth strategy

database policy

generator behavior

strict vs lenient mode

dependency manager choice

Example
prisme_version = "0.4.0"
config_version = 1

[backend]
framework = "fastapi"

[exposure]
rest = true
graphql = false
mcp = true

[frontend]
enabled = true

[auth]
provider = "keycloak"

Versioning

config_version = schema version

prisme_version = generator version last used

Config is mandatory (empty is valid).

4. Versioning & Migration
Independent versioning

Spec schema version

Config schema version

They evolve independently.

Migration command
prisme migrate
prisme migrate --dry-run


Responsibilities:

detect outdated versions

auto-migrate when safe

fail loudly with guidance when not

Generate compatibility rules

generate fails on unsupported versions

warnings for deprecated versions

no silent behavior changes

5. Ownership Boundaries in the Repo
Required structure

generated/ → fully replaceable

app/ (or equivalent) → user-owned

generators may only write to generated/

Editing generated files is unsupported and detectable.

6. Extension Points (First-Class)

Customization must happen via explicit hooks, not ad-hoc edits.

Examples:

backend dependency overrides

router extension files

policy hooks (auth/authorization)

frontend component override folders

MCP tool pre/post hooks

These hooks are stable across regeneration.

7. Deterministic Generation

Generation must be:

order-stable

formatting-stable

path-stable

Optional:

auto-format generated code

stable IDs for UI keys

This minimizes diff noise and increases trust.

8. Plan / Apply Workflow

Introduce a two-phase model:

prisme plan
prisme apply


Plan outputs:

files to be created/modified

diff summary

risk classification

Apply performs the write.

This directly addresses regeneration fear.

9. Project Manifest (Provenance)

Each generation writes:

generated/.prisme-manifest.json

Containing:

prisme_version

spec_version

config_version

hashes of spec/config

enabled generators

timestamp

Used for debugging, CI, and support.

10. Doctor Command
prisme doctor


Checks:

missing or outdated versions

broken invariants

edited generated files

mismatched tooling

Usable locally and in CI.

11. Regeneration Guarantees (Tested)

Golden tests must prove:

regenerate preserves user code

spec changes only affect generated layer

config changes only affect generated layer

migrations are safe and explicit

These tests are part of CI and treated as a contract.

Non-Goals (Explicit)

This refactor does not aim to:

redesign the DSL

remove existing generators

force new frameworks

add new product features

The goal is clarity, safety, and evolvability.

Success Criteria

This refactor is successful when:

users know where a change belongs

regeneration is boring and safe

upgrades are explicit and guided

Prisme feels predictable and trustworthy

If you want next, I can:

turn this into an ADR

split it into implementation phases

or map it directly to GitHub issues / milestones
