Gemma4 HabitLang DSL

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HabitLang-DSL.md · 4.3 KiB · Markdown Неформатований

To turn these prose-based habits into a **Domain Specific Language (DSL)**, we need to shift from "advice" to "constraints and declarations." I will call this language **`HabitLang`**. It is a declarative language used to define a team's "Development Philosophy" or "Quality Manifesto." Instead of writing a README, a team writes a `.habit` file that acts as a living specification for their workflow. Below is the specification and the implementation of the rules in `HabitLang`. --- ### 1. The `HabitLang` Specification **Core Primitives:** * **`policy`**: A high-level directive that must be followed. * **`threshold`**: A quantitative limit that triggers an action. * **`priority`**: A ranking of importance (Immediate, Future, Ignorable). * **`constraint`**: A hard rule that defines "Working" or "Done." * **`workflow`**: A sequence of required steps. --- ### 2. The Rules translated into `HabitLang` Here is how the 10 rules would be written in the DSL: ```python // Rule 1: Commit Granularity policy CommitSize { limit: "compilable_unit", goal: "effortless_revert", warning: "if you feel you are taking this too far, you are doing it right" } // Rule 2: Refactoring Cadence policy RefactorCycle { commit_ratio: 0.5, // 50% of commits should be refactors max_duration: 10m, // Continuous refactoring units strategy: "make_easy_then_do_easy", avoid: "big_refactor" } // Rule 3: Deployment & Value policy Deployment { liability: "undeployed_code", measure_of_progress: "working_software", definition_of_working: "deployed_and_contributing", cost_tolerance: "high", // Hosting costs < knowledge of progression } // Rule 4: Testing Scope policy TestScope { trust: "framework_internals", avoid: "testing_the_framework", correlation: { component_size: "small" -> test_burden: "low", component_size: "large" -> test_burden: "high" } } // Rule 5: Module Organization policy ModulePlacement { preference: "independent_construct", forbidden: "jamming_into_existing_module", lifecycle: "create_now -> home_later" } // Rule 6: API Design workflow ApiDevelopment { sequence: [TestFirst, Implement, Verify], dogma_level: "low", goal: "customer_perspective" } // Rule 7: Abstraction Trigger (The Rule of Three) policy Abstraction { copy_paste_limit: 2, trigger: "third_occurrence", action: "consolidate_abstraction", preference: "wonky_parameterization" over "diverging_implementations" } // Rule 8: Design Evolution policy DesignMindset { state: "stale_by_default", acceptance: "change_is_core", metric_of_skill: "ease_of_changing_software" } // Rule 9: Technical Debt Classification policy TechDebt { categories: { type_1: { label: "blocking_now", priority: "critical" }, type_2: { label: "blocking_later", priority: "high" }, type_3: { label: "might_block_later", priority: "ignore" } }, focus: [type_1, type_2] } // Rule 10: Testability as a Signal policy Testability { signal: "difficult_to_test" -> "design_flaw", remedy: ["extract_function", "create_test_utility"], assertion: "tests_go_unwritten_due_to_friction_not_will" } ``` --- ### 3. How this DSL would be used (The "Runtime") Since a DSL is useless without a way to apply it, here are three ways `HabitLang` could be "executed": #### A. The Linter (Static Analysis) A CLI tool scans your Git history and project structure: * **Check Rule 1 & 2:** `habit-check` analyzes the last 100 commits. If `refactor_ratio` is < 0.5, it warns: *"Your project is drifting away from Rule 2: Increase refactoring frequency."* * **Check Rule 7:** A static analyzer detects three nearly identical functions and flags: *"Abstraction Trigger: Third occurrence detected. Consolidate now."* #### B. The PR Template (Human Process) The DSL generates a dynamic Pull Request checklist based on the `.habit` file: - [ ] Is this commit "small enough to be blissfully revertible"? (Rule 1) - [ ] If this is a new API, were tests written first? (Rule 6) - [ ] Does this add to "Type 1" or "Type 2" tech debt? (Rule 9) #### C. The Onboarding Tool (Cultural Alignment) When a new developer joins, they run `habit-init`, which transforms the `.habit` file into an interactive guide, explaining the "Why" behind these constraints, ensuring the team's "Good Habits" are codified and not just implied.

To turn these prose-based habits into a Domain Specific Language (DSL), we need to shift from "advice" to "constraints and declarations."

I will call this language HabitLang. It is a declarative language used to define a team's "Development Philosophy" or "Quality Manifesto." Instead of writing a README, a team writes a .habit file that acts as a living specification for their workflow.

Below is the specification and the implementation of the rules in HabitLang.

1. The `HabitLang` Specification

Core Primitives:

policy: A high-level directive that must be followed.
threshold: A quantitative limit that triggers an action.
priority: A ranking of importance (Immediate, Future, Ignorable).
constraint: A hard rule that defines "Working" or "Done."
workflow: A sequence of required steps.

2. The Rules translated into `HabitLang`

Here is how the 10 rules would be written in the DSL:

// Rule 1: Commit Granularity
policy CommitSize {
    limit: "compilable_unit",
    goal: "effortless_revert",
    warning: "if you feel you are taking this too far, you are doing it right"
}

// Rule 2: Refactoring Cadence
policy RefactorCycle {
    commit_ratio: 0.5, // 50% of commits should be refactors
    max_duration: 10m, // Continuous refactoring units
    strategy: "make_easy_then_do_easy",
    avoid: "big_refactor"
}

// Rule 3: Deployment & Value
policy Deployment {
    liability: "undeployed_code",
    measure_of_progress: "working_software",
    definition_of_working: "deployed_and_contributing",
    cost_tolerance: "high", // Hosting costs < knowledge of progression
}

// Rule 4: Testing Scope
policy TestScope {
    trust: "framework_internals", 
    avoid: "testing_the_framework",
    correlation: {
        component_size: "small" -> test_burden: "low",
        component_size: "large" -> test_burden: "high"
    }
}

// Rule 5: Module Organization
policy ModulePlacement {
    preference: "independent_construct",
    forbidden: "jamming_into_existing_module",
    lifecycle: "create_now -> home_later"
}

// Rule 6: API Design
workflow ApiDevelopment {
    sequence: [TestFirst, Implement, Verify],
    dogma_level: "low",
    goal: "customer_perspective"
}

// Rule 7: Abstraction Trigger (The Rule of Three)
policy Abstraction {
    copy_paste_limit: 2,
    trigger: "third_occurrence",
    action: "consolidate_abstraction",
    preference: "wonky_parameterization" over "diverging_implementations"
}

// Rule 8: Design Evolution
policy DesignMindset {
    state: "stale_by_default",
    acceptance: "change_is_core",
    metric_of_skill: "ease_of_changing_software"
}

// Rule 9: Technical Debt Classification
policy TechDebt {
    categories: {
        type_1: { label: "blocking_now", priority: "critical" },
        type_2: { label: "blocking_later", priority: "high" },
        type_3: { label: "might_block_later", priority: "ignore" }
    },
    focus: [type_1, type_2]
}

// Rule 10: Testability as a Signal
policy Testability {
    signal: "difficult_to_test" -> "design_flaw",
    remedy: ["extract_function", "create_test_utility"],
    assertion: "tests_go_unwritten_due_to_friction_not_will"
}

3. How this DSL would be used (The "Runtime")

Since a DSL is useless without a way to apply it, here are three ways HabitLang could be "executed":

A. The Linter (Static Analysis)

A CLI tool scans your Git history and project structure:

Check Rule 1 & 2: habit-check analyzes the last 100 commits. If refactor_ratio is < 0.5, it warns: "Your project is drifting away from Rule 2: Increase refactoring frequency."
Check Rule 7: A static analyzer detects three nearly identical functions and flags: "Abstraction Trigger: Third occurrence detected. Consolidate now."

B. The PR Template (Human Process)

The DSL generates a dynamic Pull Request checklist based on the .habit file:

Is this commit "small enough to be blissfully revertible"? (Rule 1)
If this is a new API, were tests written first? (Rule 6)
Does this add to "Type 1" or "Type 2" tech debt? (Rule 9)

C. The Onboarding Tool (Cultural Alignment)

When a new developer joins, they run habit-init, which transforms the .habit file into an interactive guide, explaining the "Why" behind these constraints, ensuring the team's "Good Habits" are codified and not just implied.

1. The HabitLang Specification