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    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    📝 Prompt Template Linter

    by JustHandled Labs

    3

    The Prompt Template Linter is a specialized diagnostic tool designed to sanitize and optimize LLM prompt templates before they reach production.

    $12

    30-day refund guarantee

    Secure checkout via Stripe

    0 installsSecurity scanned

    See it in action

    You say

    Lint the prompt templates in the /prompts directory and check for injection risks and missing variables.

    Your agent does

    Findings:

    • [PTL001] SEVERE: /prompts/user_gen.j2 (Line 12) - Untrusted variable 'user_input' interpolated into instruction region.
    • [PTL002] WARNING: /prompts/summary.md (Line 4) - Placeholder 'date_range' is defined but never used in the template.

    What you get

    Identify prompt injection surfaces in dynamic Jinja2 templates.Fix broken variable references and leftover placeholders before deployment.Detect contradictory model instructions that lead to hallucinations.Enforce output schema requirements for reliable downstream parsing.

    About this skill

    What it does

    The Prompt Template Linter is a specialized diagnostic tool designed to sanitize and optimize LLM prompt templates before they reach production. It scans your template files to identify structural flaws, security risks, and logical inconsistencies that often lead to model hallucinations or prompt injection vulnerabilities.

    Why use this skill

    Manually auditing prompts for "jailbreak" surfaces or missing variables is error-prone. This skill automates the detection of critical issues like unescaped user input in instruction blocks, contradictory constraints, and orphaned placeholders. It ensures your prompts are robust, type-safe (where variables are concerned), and formatted for reliable parsing.

    Supported tools

    • File Formats: Supports .prompt, .txt, .md, .jinja, and .j2 files.
    • Integration: Works via CLI or stdin, making it ideal for pre-commit hooks or CI/CD pipelines.
    • Remediation: Includes a library of safe-snippet references to fix flagged issues instantly.

    Output format

    The skill produces a structured report categorized by rule ID (PTL001-PTL006) and severity, pinpointing the exact line and file where the issue resides.

    ai-agents
    prompt-engineering
    llm-ops
    jinja2

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

    Reviews

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    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    30-day refund

    Not a fit? Get your money back

    More reviews for JustHandled Labs5.0(2)

    Feedback buyers left on other skills by the same creator.

    about 1 month ago
    Samuel Rose·📊 Pipeline Reporter
    It was easy to install as a new skill
    about 1 month ago

    I already had a forecasting tool but this gave us more detail that I had Claude take the best parts and merge it into my current agent to make it more robust.

    Robert Murphy·📊 Pipeline Reporter

    Trust & safety

    Security scanned

    Verified clean 2 months ago

    Listed2 months ago

    Creator

    JustHandled Labs

    JustHandled Labs

    53+ total installs · 185 skills on Agensi

    5.0(2)

    JustHandled Labs builds focused agent skills for the work nobody wants to do by hand. Each one is a single repeatable job done well: catching the security and data mistakes that quietly ship, keeping docs and tests honest, gating the commands an agent is about to run, sharpening writing, and handling the founder chores around launches, outreach, and brand setup. Not generic AI productivity. Specific workflows that are easy to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. Tools for people who want useful automation without theatrical complexity.

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