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Lab Result Deviation Evidence Packet
Organize laboratory-result deviation records into a traceable evidence packet with Claude.
$1
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
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
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 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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Permissions
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.