Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    sprint mechanic persona

    1

    A delivery-focused lead persona that turns vague requests into honest estimates and testable acceptance criteria.

    Secure checkout via Stripe

    0 installsSecurity scanned

    See it in action

    You say

    Refine this ticket: 'Add a search bar to the dashboard so users can find orders faster.' We did a similar filter for the users table last month in 3 days.

    Your agent does

    What does done look like, exactly? Based on the user filter, I estimate 2-4 days (80% confidence). Acceptance Criteria: 1. Input accepts alphanumeric order IDs. 2. Results update via AJAX without page reload. 3. 'No results' state displays for invalid IDs. If we add fuzzy search, that's scope growth.

    What you get

    Convert vague stakeholder requests into verifiable acceptance criteria.Generate honest estimate ranges anchored to comparable past tasks.Audit incoming sprint items for missing information or technical holes.Quantify and flag scope creep against original ticket definitions.

    About this skill

    The problem

    Vague tickets and overly optimistic estimates lead to missed deadlines and eroded client trust. Engineering teams often spend more time deciphering requirements than writing code, resulting in scope creep that kills sprint velocity.

    What it does

    • Converts ambiguous requests into concrete, testable acceptance criteria.
    • Generates estimate ranges anchored to historical work rather than single-number guesses.
    • Identifies and flags scope growth against original ticket definitions in real-time.
    • Forces clarity by identifying missing information before drafting documentation.
    • Produces concise, one-page artifacts focused on delivery rather than process theater.

    Why this beats prompting it yourself

    Generic LLMs tend to be overly agreeable and prone to "quick win" fallacies. The Sprint Mechanic is programmed to be skeptical, refusing to mark tickets as ready until they are verifiable and anchoring forecasts to comparable past performance to prevent developer bias.

    Use cases

    • Refining raw client emails or chat logs into actionable engineering tickets.
    • Sanity-checking sprint estimates against historical complexity.
    • Defining clear "definition of done" markers for non-technical stakeholders.
    • Flagging hidden scope creep during mid-sprint requirement changes.

    Known limitations

    Does not provide sales advice, pricing strategies, or legal contract terms. Requires user confirmation before updating external ticket systems or committing code.

    How to install

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

    Reviews

    No reviews yet

    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

    Trust & safety

    Security scanned

    Verified clean today

    Listedtoday

    Frequently Asked Questions