Month End Narrative

    1

    The month end close story, written only from numbers that reconciled.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    Analyze our October performance using the attached ledger, P&L, and budget. Reconcile the data first and write the narrative for the board meeting.

    Your agent does

    Reconciliation Status: TIED

    All bank balances and revenue figures match the ledger.

    Variance Drivers

    1. Cloud Infrastructure: $12k over budget due to one-time migration costs (Timing).
    2. Ad Spend: $8k under budget; lower CPC than forecasted (Performance).

    Narrative

    October finished 4% above net margin targets...

    What you get

    Reconcile bank statements against ledger totals automatically.Isolate performance drivers from simple timing variances.Generate board-ready financial summaries based on hard data.Enforce accounting basis consistency across reporting periods.

    About this skill

    The problem

    Writing financial narratives is time-consuming, yet the words are worthless if the underlying numbers shift after the report is drafted. Most financial summaries fail by burying key drivers under a list of every minor line item variance.

    What it does

    • Executes a mandatory reconciliation of bank statements to ledgers and invoices to revenue before drafting.
    • Identifies unreconciled items as blockers with specific amounts rather than burying them in footnotes.
    • Distinguishes between timing differences (revenue slippage) and actual performance changes.
    • Extracts the two or three primary drivers that explain the majority of period variance.
    • Enforces a constant accounting basis to ensure period-over-period consistency.

    Why this beats prompting it yourself

    Generic prompts often hallucinate explanations for mismatched data. This skill enforces a hard "reconcile-first" logic gate that prevents the agent from narrating inaccurate figures. It focuses strictly on variance drivers, preventing the "wall of text" output typical of standard LLM financial summaries.

    Use cases

    • Month-end close reporting for internal stakeholders.
    • Monthly financial summaries for board updates or partners.
    • Explaining budget vs. actual variances to department heads.
    • Preparing timing-adjusted performance reviews for small business owners.

    Known limitations

    Requires structured inputs including period statements, ledger exports, and budget/plan documents to function effectively.

    Most premium skills are on a weekly review cycle. Fixes and improvements ship as updates, and buyers receive release notes by email. Part of the Mambabuilt catalog. Packs and the full bundle are available at mambabuilt.com.

    How to install

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

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