Codegen And Toolchain

Community

zakariaf

codegen-and-toolchain

Enforces a deterministic build_runner codegen discipline: run one pinned `dart run build_runner build --delete-conflicting-outputs` pass BEFORE `flutter analyze` (never after), fence every builder with `generate_for:` globs in a per-package `build.yaml`, make one deliberate commit-vs-gitignore decision for `*.g.dart`/`*.freezed.dart`/`*.drift.dart` and back it with the matching CI gate (freshness diff if committed, codegen-first if gitignored), mirror the generated-file globs into the analyzer AND coverage excludes, pin the SDK, and never hand-edit generated output. Use when editing build.yaml, analysis_options.yaml, pubspec.yaml, .gitignore, .gitattributes, or CI workflows; wiring drift_dev/freezed/json_serializable/riverpod_generator/ gen-l10n/mockito codegen; fixing "missing part file", "undefined class _$Foo", or "conflicting outputs" errors; deciding whether to commit generated code; or scoping builders so one edit does not regenerate everything.

Overview

Publisher	zakariaf
Repository	`Flutter-Skills`
Skill name	`codegen-and-toolchain`
Stars	64
Forks	15
Bundled files	7
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
7 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by zakariaf on GitHub. Read the source before you install it.

Installation

Install the Codegen And Toolchain AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/zakariaf/Flutter-Skills/tree/main/skills/codegen-and-toolchain
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/zakariaf/Flutter-Skills.git /tmp/Flutter-Skills
mkdir -p .claude/skills
cp -r /tmp/Flutter-Skills/skills/codegen-and-toolchain .claude/skills/codegen-and-toolchain

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codegen And Toolchain in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codegen And Toolchain on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codegen And Toolchain is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codegen and toolchain

One deterministic codegen pass is the source of truth for every generated file; run it first, scope it tightly, and decide once whether its output lives in git. Applies whenever a project uses build_runner or gen-l10n.

Read the reference for the task at hand:

references/codegen-scoping.md — the generator inventory, generate_for: scoping tables, error-to-fix recovery map.
references/commit-vs-gitignore.md — the two committed/gitignored strategies, their tradeoffs, and the exact CI gate each one requires.
references/toolchain-and-workspace.md — SDK pinning, analyzer/coverage excludes, and the multi-package workspace layering (fenced, opt-in).

Run scripts/regen.sh (codegen → format → analyze) before a PR, and scripts/check-codegen-hygiene.sh to verify excludes and commit-policy parity.

Non-negotiable rules

Codegen runs FIRST, before flutter analyze. Generated files may be absent on a fresh clone or stale after editing an annotation. Analyzing that tree emits misleading "missing part file" and "undefined class _$Foo" errors. Regenerate, then analyze — never chase the symptom at the call site.
Always pass --delete-conflicting-outputs. Without it build_runner refuses to overwrite an output from a renamed/deleted source and fails with a wall of "conflicting outputs" text. That is not a bug to investigate.
Never hand-edit a generated file. The next build silently reverts it and takes your edit with it. Change the annotated source and rerun codegen.
Scope every builder with generate_for: in a per-package build.yaml. A change in one directory must not regenerate the whole tree. Point each glob at the narrowest directory that owns the annotation.
Decide commit-vs-gitignore ONCE, back it with the matching CI gate. Gitignored → CI regenerates first, before analyze. Committed → CI regenerates then runs git diff --exit-code as a freshness gate. Either is valid; a policy without its gate is not (see references/commit-vs-gitignore.md).
Mirror generated-file globs into the analyzer AND coverage excludes. A glob that lints machine output wastes review; a glob missing from lcov inflates coverage. Exclude the emitted suffix — verify it, do not guess.
Pin the SDK; local must equal CI. The CI Flutter/Dart version must match the pinned local version (.fvmrc under FVM, or the tool the repo standardizes on). A drift here reproduces "works on my machine" format/codegen churn.
watch is a local edit-loop tool only. CI and the pre-analyze step always use the one-shot build command.

The canonical command

Run from the repository root:

bash
dart run build_runner build --delete-conflicting-outputs

This regenerates every builder — drift_dev, freezed, json_serializable, riverpod_generator, mockito — in one pass. gen-l10n runs through the Flutter tool but is treated identically (regenerated first, same commit policy):

bash
flutter gen-l10n   # if not auto-triggered by `flutter pub get` via l10n.yaml

Fresh clone / after pull / after editing any annotation, the fixed order is:


build_runner build --delete-conflicting-outputs   →   format   →   analyze   →   test

Never reorder codegen after analyze. During an active edit session only, a watcher rebuilds affected outputs on save:

bash
dart run build_runner watch --delete-conflicting-outputs

Scoping builders with build.yaml

Give every codegen-carrying package (or the single app package) its own build.yaml, and fence each builder's generate_for: at the narrowest directory that owns its annotation. An unscoped root config fans out to every annotation in the tree, so editing one Task model regenerates unrelated Order and Account outputs. Copy examples/build.yaml; references/codegen-scoping.md has the full generator-to-suffix table and per-builder globs.

Commit vs gitignore: pick one, wire its gate

Both strategies are legitimate; they trade different risks (see rule 5). Gitignore keeps the tree clean but needs a working toolchain to build. Committing survives a broken future toolchain and makes schema diffs reviewable, but needs a .gitattributes diff-collapse (examples/gitattributes.example) AND a mandatory freshness gate — regenerate, then git diff --exit-code -- '*.g.dart' '*.freezed.dart' '*.drift.dart'. A policy without its gate is not a policy. Both strategies, their tradeoffs, and the exact gate each requires: references/commit-vs-gitignore.md.

Analyzer and coverage excludes

Committed OR gitignored, every emitted generated suffix must be excluded from BOTH the analyzer and the lcov coverage filter — verify the suffix the config actually emits first, do not guess. A missed analyzer glob wastes review on machine output; a missed (or mis-anchored) lcov glob inflates coverage with untested code. A migration-test baseline dir that holds generated era-correct data classes needs the analyzer exclude too — an autofix inside an exported schema snapshot would corrupt migration verification (see run-migration). The canonical exclude blocks and the correctly anchored lcov patterns live in references/toolchain-and-workspace.md.

When multi-package (workspace)

Only for a repo that has genuinely outgrown one package. A single-app project needs none of this.

Link members with a native Dart pub workspace: resolution: workspace in every member pubspec plus a workspace: list at the root, producing ONE root pubspec.lock. Never add a per-package lockfile.
If you layer Melos on top, use it for script orchestration only (melos run gen/format/analyze/test), never for path linking — pub does the linking. Filter the gen script to packages that depend on build_runner.
Run build_runner once at the workspace root so every member regenerates in the same pass; running inside one package dir leaves the others stale.

Anti-patterns

Analyzing before regenerating — every downstream error is a phantom; fix the order, not the code.
Omitting --delete-conflicting-outputs — turns a routine rename into a build wall.
Hand-editing .g.dart/.drift.dart to silence an error — reverted on the next build; the real fix is in the annotated source.
Committing generated code with no freshness gate — output silently drifts from source and reviewers trust a lie.
One unscoped root build.yaml — every trivial edit regenerates the whole tree and slows every build.
Coverage/analyzer glob that matches the wrong suffix — fails silently in the analyzer, loudly in lcov 2.x, and quietly inflates coverage.
CI Flutter version ≠ pinned local version — reproducible format/codegen churn nobody can explain.
A generated migration test left with empty data lists — passes vacuously ([] == []) while proving nothing; fill it or delete it (see run-migration).

Definition of done

dart run build_runner build --delete-conflicting-outputs succeeds from a clean tree, then flutter analyze is clean.
Every codegen-carrying package has a build.yaml with scoped generate_for: globs.
The commit-vs-gitignore decision is made and its CI gate exists (freshness diff, or codegen-first).
The emitted generated suffixes are excluded from BOTH analysis_options.yaml and the lcov coverage filter.
SDK is pinned and the CI toolchain version matches it.
No generated file is hand-edited; no watch in CI.

Related skills

See run-codegen for the operational, low-freedom "run this exact command" loop and its troubleshooting table.
See run-migration for the Drift schema-snapshot and migration-test ritual the codegen pass produces.
See persistence-drift for the Drift/DAO data layer these generators feed.
See lint-and-style-config for the analyzer ruleset and the excludes mirrored here.
See ci-pipeline-and-gates for where codegen and the freshness gate sit in the pipeline.
See dependency-hygiene for the lockfile and SDK-pinning mechanics.

References

build_runner: https://dart.dev/tools/build_runner
Drift codegen & build config: https://drift.simonbinder.eu/docs/advanced-features/builder_options/
freezed: https://pub.dev/packages/freezed
json_serializable: https://pub.dev/packages/json_serializable
riverpod codegen: https://riverpod.dev/docs/concepts/about_code_generation
Flutter gen-l10n: https://docs.flutter.dev/ui/accessibility-and-internationalization/internationalization
Dart pub workspaces: https://dart.dev/tools/pub/workspaces

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codegen And Toolchain AI skill do?

Why use Codegen And Toolchain on TypingMind?

Because you install it once and use it with any model. Codegen And Toolchain is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codegen And Toolchain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zakariaf/Flutter-Skills/tree/main/skills/codegen-and-toolchain. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codegen And Toolchain?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codegen And Toolchain?

As many as you like. As long as a model supports skills, you can use Codegen And Toolchain with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codegen And Toolchain AI skill free?

Yes. It is published on GitHub by zakariaf under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Codegen and toolchain

Non-negotiable rules

The canonical command

Scoping builders with build.yaml

Commit vs gitignore: pick one, wire its gate

Analyzer and coverage excludes

When multi-package (workspace)

Anti-patterns

Definition of done

Related skills

References

Bundled files

Frequently asked questions

Related AI Skills

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