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Dash Discover

Community
kevmoo
dash-discover

Diagnoses latent architectural modernization opportunities across Dart and Flutter packages (language idioms, testing hygiene, CLI patterns, doc rot) and prescribes matching specialized skills.

Overview

Publisherkevmoo
Repositorydash_skills
Skill namedash-discover
Stars
148
Forks
16
Bundled files
1
LicenseApache-2.0
Links
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kevmoo on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kevmoo/dash_skills/tree/main/skills/dash-discover
  4. 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.
2

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/kevmoo/dash_skills.git /tmp/dash_skills
mkdir -p .claude/skills
cp -r /tmp/dash_skills/skills/dash-discover .claude/skills/dash-discover
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dash Discover 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 Dash Discover 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 Dash Discover 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.

Dash Discover (Meta-Skill)

The Meta-Skill Discovery Engine evaluates a Dart or Flutter project's architecture, language idioms, testing patterns, and documentation health to uncover latent modernization opportunities that standard static analysis passes ignore.


1. When to use this skill

Use this skill when:

  • Asked questions like: "Am I doing this right?", "Am I holding it right?", or "What skills should I use on this repository?"
  • Entering a new or unfamiliar Dart/Flutter repository and determining where to focus modernization effort.
  • dart analyze reports clean code (0 errors, 0 warnings), but the codebase may still harbor outdated pre-Dart 3 constructs, legacy matcher assertions, unstructured CLI entrypoints, or rotting doc examples.

When NOT to use (Abstention Guardrails)

Do NOT run or prescribe discovery scans when:

  • Non-Dart / Non-Flutter Projects: The repository or directory has no pubspec.yaml or Dart source files (e.g. pure documentation repositories or external language services).
  • Targeted Single-File Edits or Bug Fixes: The user is requesting a narrow, isolated bug fix or single-file adjustment. Do not derail the turn into a broad repository-wide architectural audit unless specifically solicited.
  • Active In-Flight Breaking Migrations: The repository is midway through a breaking SDK upgrade or uncommitted refactoring. Allow primary migrations to stabilize before running discovery sweeps.

2. Core Mental Model: The Analyzer Blindspot

Static analysis (dart analyze) verifies syntactic and semantic correctness, not architectural quality or modern idiomatic design. A package can pass dart analyze --fatal-infos with zero warnings while simultaneously:

  • Using 7-branch polymorphic else if (x is Y) cascades instead of concise Dart 3 switch expressions with pattern destructuring.
  • Relying on legacy package:test expect(actual, matcher) calls instead of fluent, type-safe package:checks.
  • Storing rotting, unverified code snippets in /// ``` doc comments instead of automated {@example} region testing.
  • Building ad-hoc monolithic 300+ line bin/main.dart entrypoints without package:args/command_runner.dart.

Dash Discover systematically identifies these latent gaps and points directly to the specialized skills equipped to remediate them.


3. Two-Tier Discovery Protocol

Tier 1: Fast Static Heuristics (<50ms)

Run the discovery CLI from anywhere in the workspace:

bash
dart run dash_discover <path-to-target-package>

Or for structured machine ingestion:

bash
dart run dash_discover <path-to-target-package> --json

By default, scans report Defect and Capability rules while suppressing Alternative idiom rules (checks-migration, pattern-matching, matcher-best-practices) when existing code is already valid. Pass --include-alternatives (or target a rule directly via --rule <id>) to opt into alternative-idiom checks:

bash
dart run dash_discover <path-to-target-package> --include-alternatives

The static scanner performs rapid, zero-network checks across 7 built-in rules:

  1. Testing Architecture (dart-migrate-to-checks-package) [Alternative (requires --include-alternatives)]: Detects test or flutter_test in dependencies when checks is absent.
  2. Dart 3 Language Idioms (dart-use-pattern-matching) [Alternative (requires --include-alternatives)]: Detects legacy else if (... is ...) type cascades and returning switch statements.
  3. CLI Architecture (dart-build-cli-app) [Capability]: Detects ad-hoc bin/*.dart CLI entrypoints lacking structured argument parsing.
  4. Cross-Platform Robustness (dart-use-path-package) [Defect]: Detects manual path string concatenation without package:path.
  5. Testing Architecture (dart-generate-test-mocks) [Capability]: Detects handwritten fake or mock class definitions without mockito or mocktail.
  6. Testing Architecture (dart-matcher-best-practices) [Alternative (requires --include-alternatives)]: Detects unidiomatic expect() assertions (e.g. expect(x.length, ...) or expect(x.isEmpty, true)).
  7. Dart 3 Language Idioms (dart-seal-type-hierarchies) [Capability]: Detects closed type hierarchies that are not sealed, so the compiler cannot check switch exhaustiveness over them.

Tier 2: Token-Efficient Outline Probing

To capture complex cross-cutting architectural patterns beyond regexes:

  1. Generate the condensed repository outline (~1k tokens):
    bash
    dart run dash_discover <path-to-target-package> --outline-only
  2. The outline captures:
    • pubspec.yaml (dependencies, dev-dependencies, SDK constraints).
    • Shallow directory structure (up to 3 levels deep).
    • Structural API signatures and class outlines (via sem entities lib/ --signatures).
  3. Pass the generated prompt (--prompt-only) and outline to a fast model (such as Gemini Flash) along with the active skills catalog to evaluate semantic architectural fit with concrete file evidence.

4. Remediation Workflow

When dash-discover produces recommendations:

  1. Triage by Trigger, Lifecycle & Confidence:
    • Address Defect findings first (fragile or unportable patterns), then evaluate Capability opportunities against project goals; treat Alternative idiom migrations (--include-alternatives) as opt-in.
    • Focus on finite migrations before recurring hygiene sweeps, prioritized by confidence and affected file count.
    • Throttle ubiquitous recommendations (e.g. at most one test framework migration at a time).
  2. Follow the Resolved Skill Target:
    • Each opportunity output by dash-discover includes a resolved_uri in --json and a clickable link in Markdown.
    • If the target skill is installed locally (is_local: true), inspect and follow the local SKILL.md directly (file://...).
    • If not installed locally, inspect the upstream definition at the provided github_url or install the skill into your agent environment before applying changes.
  3. Verify Empirically:
    • Ensure tests continue to pass (dart test).
    • Ensure analysis remains clean (dart analyze).

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 Dash Discover AI skill do?

Diagnoses latent architectural modernization opportunities across Dart and Flutter packages (language idioms, testing hygiene, CLI patterns, doc rot) and prescribes matching specialized skills.

Why use Dash Discover on TypingMind?

Because you install it once and use it with any model. Dash Discover 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 Dash Discover in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kevmoo/dash_skills/tree/main/skills/dash-discover. 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 Dash Discover?

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 Dash Discover?

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

Is the Dash Discover AI skill free?

Yes. It is published on GitHub by kevmoo under the Apache-2.0 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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