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    Lab Result Deviation Evidence Packet

    1

    Organize laboratory-result deviation records into a traceable evidence packet with Claude.

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    Lab Result Deviation Evidence Packet

    Lab Result Deviation Evidence Packet

    Example session with this skill installed

    Organize a deviation packet for Assay L1 (98-102%). Original result L2 was 94.1%. Audit trail L3 shows reintegration. Retest L4 passed at 99.8%. Analyst suspects a pipette error.

    • Read your context and instructions
    • Compiled the lab result deviation

    1. Packet decision

    Decision: PARTIAL LABORATORY-RESULT DEVIATION EVIDENCE PACKET AVAILABLE.

    7. Gaps and conflicts

    G1 — Assignable cause: Pipette hypothesis lacks supporting evidence.
    G2 — Audit trail: Reintegration needs reconstruction and rationale.
    G4 — Retest governance: Authorization missing.

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

    About this skill

    The problem

    Laboratory deviation records are often scattered across disparate instrument logs, worksheets, and audit trails. Manually organizing this evidence into a traceable investigation packet is time-consuming and prone to overlooking critical data integrity gaps.

    What it does

    • Builds a structured source register from specifications, raw instrument data, and analyst notes.
    • Maps sample identities across chain-of-custody, receipt, and preparation records to identify discrepancies.
    • Separates original observations from recalculated results, retests, and resamples to preserve data integrity.
    • Chronologically links analytical controls, system-suitability results, and equipment alarms to specific results.
    • Flags gaps such as unsupported hypotheses, missing audit trails, and unauthorized retests for technical review.

    Why this beats prompting it yourself

    This skill enforces strict regulatory-style boundaries, ensuring that hypotheses are never conflated with confirmed causes. It maintains a neutral stance that prevents the agent from making unauthorized validity decisions or disposition claims that could compromise a quality investigation.

    Use cases

    • Preparing a source-indexed packet for internal quality assurance or technical review.
    • Reconstructing a data trail when audit logs show reintegration or reprocessing.
    • Organizing evidence when a laboratory hypothesis is presented as a confirmed root cause.
    • Mapping batch impact across shared instruments, materials, and analysts.

    Known limitations

    Requires all records to be supplied via file or Read tool. Cannot determine result validity, assign root cause, or assess public health risks.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Agents & Orchestration.

    Requires all records to be supplied via file or Read tool. Cannot determine result validity, assign root cause, or assess public health risks.

    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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    Trust & safety

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    Security scan passed 3 months ago

    Permissions

    Read Files

    Permissions are limited to the active local workspace and are used to inspect user-supplied or local workspace files. The skill package does not require bundled credentials, unrestricted external network access, or automatic production changes.

    Listed3 months ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    The skill automates the labor-intensive process of gathering and linking scattered laboratory data, ensuring that audit trails, instrument logs, and analyst notes are organized into a single, chronologically indexed packet for faster quality reviews.

    This skill is designed to work with most advanced LLM-based agents that support document processing and tool-use; however, you must provide the specific files (logs, worksheets, etc.) via your agent's file upload or data retrieval tool.

    Upon purchase, you receive the production-ready agent instructions (system prompt) and a structured data schema for organizing the evidence packets, which can be imported into your preferred AI orchestration environment.

    No, the skill is strictly an evidence-organizing tool; it identifies gaps and maps data trails to preserve integrity, but it will not make final validity determinations or assign root causes, as these require human quality signatures.

    By strictly separating original observations from retests and flagging unsupported hypotheses, the skill ensures that the investigation packet meets high regulatory standards for data traceability and prevents 'cherry-picking' of results.

    New in Agents & Orchestration