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    Python DictLinter — Static i18n & AST Dictionary Auditor

    1

    Detects mismatched i18n translation keys, structural syntax faults, and brace imbalances in Python dicts.

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    Python DictLinter — Static i18n & AST Dictionary Auditor

    Python DictLinter — Static i18n & AST Dictionary Auditor

    Example session with this skill installed

    Intended modification to a Python translation dictionary with structural anomalies

    t = {
    "RU": {"greeting": "Привет", "farewell": "Пока"},
    "EN": {"greeting": "Hello"}
}
    • Read your context and instructions
    • Compiled the python dictlinter
    {
  "audit_status": "CRITICAL",
  "findings": [
    {
      "id": "f1",
      "file": "locales/translation.py",
      "line": 3,
      "severity": "HIGH",
      "signature": "ERR_MISSING_KEY",

      "message": "Key 'farewell' is present in 'RU' node but missing from 'EN' node. Triggers runtime KeyError.",
  "snippet": "\"EN\": {\"greeting\": \"Hello\"}"

        }
  ],
  "suggested_remediation": {
    "t": {
      "RU": {"greeting": "Привет", "farewell": "Пока"},
      "EN": {"greeting": "Hello", "farewell": "Goodbye"}
    }
  },
  "metadata": {
    "version": "1.0.0",
    "timestamp": "2026-10-15T15:00:00Z",
    "files_scanned": 1,
    "key_consistency": "FAILED"
  }
}

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Stop tolerating silent backend crashes, production localization failures, and unhandled KeyError exceptions caused by broken translation structures. Python DictLinter is a static AST-driven architectural auditor that deep-scans Python dictionary literals and localization schemas to guarantee complete structural symmetry across multi-language applications before deployment.

    AI development agents frequently introduce subtle syntax regressions—such as unclosed quote literals, skipped delimiters, or asymmetric key mapping profiles—when generating or scaling deep, nested data sets within source files. This engine acts as a strict validation gate, verifying syntactic and logical integrity inside local memory blocks.

    What it does

    • Cross-Language Key Synchronization: Deep-scans nested native structures to guarantee that all defined language segments (e.g., t["RU"], t["EN"]) enforce completely identical key maps, neutralizing runtime KeyError vectors.
    • AST Brace Balance Verification: Programmatically tracks, validates, and balances matching syntax delimiters ({}, [], ()) within complex Python data arrays.
    • Delimiter Integrity Guard: Detects and flags missing trailing commas, unclosed string literals, and broken notation tokens that escape standard lightweight linters.
    • AST Blueprint Analysis: Parses data syntax layouts across native Python dict structures completely offline within code session buffers.
    • Machine-Readable Telemetry: Outputs a strict, schema-validated JSON payload optimized for local pre-commit hooks, Git workflow gates, or AI agent context buffers.

    Detected Structural Vulnerabilities

    1. ERR_MISSING_KEY: Asymmetric schema mapping where an active translation key exists in one language node but is missing from another, triggering inevitable application crashes.
    2. BRACE_IMBALANCE: Structural delimiter mismatch where opening and closing brackets do not balance out, indicating broken nested payloads.
    3. UNCLOSED_LITERAL: String declarations failing to close their initial quote scopes, corrupting the down-stream parser pipeline context.
    4. SYNTAX_DISRUPTION: Omitted element separation commas or structural delimiter breaks within the target translation array.

    When to use

    • Scaling microservices and user-facing web boundaries across complex multi-language frameworks (FastAPI, Django, Flask).
    • Auditing high-density localization repositories containing extensive nested dictionary matrices or localization script assets.
    • Reviewing automated AI-generated dictionary modifications inside Cursor or Claude Code environments to prevent architectural regressions.

    When NOT to use

    • Auditing spelling accuracy, semantic context quality, or text translation values (this engine is strictly a structural layout validator).
    • Processing flat, non-nested string arrays lacking complex structural hierarchy contracts.
    • Executing dynamic runtime database mutations or operational workspace changes (this tool runs strictly in a passive, read-only configuration).

    Under the Hood & Security

    • 100% Local & Air-Gapped: Operates entirely inside secure memory allocations (file_system_write: false). Zero outbound cloud lookups ensure internal dictionary structures, infrastructure pathways, and vendor strings never leak.
    • Contract-Tested Stability: Backed by a rigorous 12-point testing harness validating schema compliance boundaries, edge cases, and absolute zero-hallucination execution.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Code Quality & Review.

    Static validation constraints only. The tool measures architectural formatting parameters and code layout metrics within native Python files without evaluating semantic context, dictionary translation quality, or linguistic properties. Flat data collections lacking nested target nodes are systematically bypassed by the structural evaluator. Requires direct file read permissions within the targeted workspace setup boundaries. Scans are bypassed on lightweight scripts under 300 total lines of code.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Software
    Python

    Permissions

    Read Files

    Allowed Hosts

    docs.python.org

    File Scopes

    **/*.py

    Read-only access to localized script files and active dictionary structures. Validates formatting integrity and cross-language key consistency fully offline within sandboxed memory allocations, completely free of codebase write actions, shell execution, or outbound network telemetry.

    Listed1 month ago
    Updated29 days ago

    What's inside

    Frequently Asked Questions

    The skill functions as a static validation guardrail that checks native Python multi-language dictionaries before deployment. It processes code blocks via Abstract Syntax Tree (AST) mapping to intercept unbalanced braces, missing item delimiters, and asymmetric translation keys that cause application termination.

    Fully compatible with Claude Code, Cursor, GitHub Copilot, and open-source agent setups supporting standard Markdown specs. The execution engine is strictly optimized for native Python dictionary structures (.py) using offline standard library parsing hooks.

    If a structural syntax fault or missing localization key is isolated within the targeted Python module, the engine triggers an automatic programmatic remediation cycle. The compromised payload is blocked from staging, ensuring only syntactically valid structures are delivered.

    Yes. The cross-language synchronization engine evaluates nested dictionary attributes to enforce absolute key map symmetry. If a key is present in your primary locale but omitted from an alternative language assignment block, the tool flags an explicit ERR_MISSING_KEY signature to prevent runtime KeyError crashes.

    The deployment package delivers the core deterministic validation engine, strict AST rule evaluation schemas, a flat JSON telemetry output architecture, and a contract-tested verification suite optimized for local git hooks and automated pipeline pre-delivery gates.

    New in Code Quality & Review