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    tender compliance copilot

    1

    Analyze RFP packs, build cited compliance matrices, and manage bid/no-bid workflows with local-first Python tools.

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    tender compliance copilot

    Example session with this skill installed

    Run an analysis of the new RFP pack in /docs/tender-2024. Extract all requirements and output a compliance matrix to my working directory.

    • Read your context and instructions
    • Compiled the tender compliance copilot
    • Generated the data export

    Analysis complete. Found 24 mandatory, 12 scored, and 8 informational requirements. Compliance matrix generated at /work/compliance_matrix.csv. 3 deadline candidates flagged for verification: Closing Date (2024-08-15), Clarification Deadline (2024-07-30), and Contract Start (2024-10-01).

    compliance_matrix.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    About this skill

    The problem

    Tender packs and RFPs are high-stakes, document-dense projects where a single missed mandatory requirement or deadline results in immediate disqualification. Manual extraction of requirements into compliance matrices is slow, prone to oversight, and often lacks a clear audit trail back to source text.

    What it does

    • Extracts mandatory, scored, and informational requirements from PDF, DOCX, and XLSX files with direct source citations.
    • Builds an editable compliance matrix mapping requirements to specific owners and evidence candidates.
    • Identifies deadlines and submission instructions for human verification to prevent late entries.
    • Facilitates a data-driven bid/no-bid process using weighted scorecards and hard-gate logic.
    • Performs pre-submission QA to flag missing files, unresolved statuses, or duplicate documents in the final package.

    Frameworks & tools

    Python 3.x, pdftotext (Poppler), and standard library modules for ZIP/XML parsing of Office formats.

    Why this beats prompting it yourself

    General-purpose prompts often hallucinate certifications or lose track of document locators. This skill enforces a local-first, English-first workflow that treats every requirement as a cited object, ensuring you can always trace a claim back to a specific line in the original tender pack.

    Use cases

    • Generating a compliance matrix for an ITT or RFQ within minutes of receiving the bid pack.
    • Matching company evidence folders against tender requirements to identify capability gaps early.
    • Running a formal bid/no-bid workshop with structured weighted scoring.
    • Automating final folder QA to ensure all mandatory submission attachments are present before upload.

    Known limitations

    Scanned PDFs require external OCR before processing. The CLI does not execute macros or calculate Excel formulas. All dates are marked as unverified and require human confirmation.

    Scanned PDFs require external OCR. Excel formulas are not calculated. All deadlines must be human-verified. Lexical evidence matching is for discovery only, not legal proof.

    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 any payment.

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    Recently published to Agensi

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    Trust & safety

    Security scan

    Security scan passed 3 months ago

    Permissions

    Terminal / Shell
    Listed3 months ago

    What's inside

    Frequently Asked Questions

    The copilot extracts requirements from PDF, DOCX, and XLSX files, generating a cited compliance matrix and a structured bid/no-bid scorecard to ensure no mandatory criteria are overlooked.

    You will receive the Python-based logic modules, CLI tools for requirement extraction, and pre-configured templates for compliance matrices and bid/no-bid scoring.

    This skill is designed for local-first execution using Python 3.x and is compatible with AI agents that support local code execution or file-system access for document processing.

    The skill uses a citation-first approach that maps every extracted requirement back to its specific source line or paragraph, allowing for instant human verification and eliminating hallucinated requirements.

    The tool flags all extracted dates and deadlines as 'unverified' in the output, prompting a human user to confirm them, which prevents errors caused by complex tender schedules.

    While the skill excels at native text and Office document parsing, any scanned or non-searchable PDF files must be processed through your preferred OCR tool before being analyzed by the copilot.

    New in Finance & Accounting