reagent-expiry-alert

Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.

53 stars

Best use case

reagent-expiry-alert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.

Teams using reagent-expiry-alert should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/reagent-expiry-alert/SKILL.md --create-dirs "https://raw.githubusercontent.com/aipoch/medical-research-skills/main/scientific-skills/Other/reagent-expiry-alert/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/reagent-expiry-alert/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How reagent-expiry-alert Compares

Feature / Agentreagent-expiry-alertStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
# Reagent Expiry Alert

Scan reagent bottle barcodes or IDs, log expiration dates, and alert before expiry to support safe laboratory inventory management.

## Quick Check

```bash
python -m py_compile scripts/main.py
python scripts/main.py --help
```

## When to Use

- Use this skill when logging a new reagent with its expiry date into the inventory.
- Use this skill when checking for reagents approaching expiration (30/60/90-day alerts).
- Do not use this skill to manage controlled substances, biological hazards requiring special disposal, or reagents subject to regulatory chain-of-custody requirements.

## Workflow

1. Confirm the reagent barcode/ID, expiry date, and action (scan/log or check alerts).
2. Validate that the request is for reagent expiry tracking, not chemical safety assessment or disposal guidance.
3. **Date validation:** If `--expiry` is provided, validate that it is a valid YYYY-MM-DD date. If the date is in the past, emit a warning: "This reagent is already expired as of [date]. It will be logged with an Expired alert status."
4. Log the reagent or run the alert check using the packaged script.
5. Return expiration status, alert level, and reorder recommendations.
6. If inputs are incomplete, state which fields are missing and request only the minimum additional information.

## Usage

```text
# Log a new reagent
python scripts/main.py --scan "REAGENT-001" --name "Tris Buffer" --expiry 2025-12-31 --location "Shelf A"

# Check for upcoming expirations
python scripts/main.py --check-alerts --alert-days 30

# Check with custom alert window
python scripts/main.py --check-alerts --alert-days 60
```

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `--scan` | string | No | — | Reagent barcode or ID |
| `--name` | string | No | — | Reagent name |
| `--expiry` | date | No | — | Expiration date (YYYY-MM-DD) |
| `--location` | string | No | — | Storage location |
| `--quantity` | string | No | — | Quantity on hand |
| `--check-alerts` | flag | No | — | Check for upcoming expirations |
| `--alert-days` | integer | No | 30 | Days before expiry to alert |

## Alert Levels

- 🔴 Expired — reagent past expiry date
- 🟠 Critical — expiring within 30 days
- 🟡 Warning — expiring within 60 days
- 🟢 OK — expiring beyond 60 days

## Output

- Expiration alert report with alert level per reagent
- Inventory summary
- Reorder recommendations for critical/expired items

## Stress-Case Rules

For complex multi-constraint requests, always include these explicit blocks:

1. Assumptions
2. Reagents Checked
3. Alert Report
4. Reorder Recommendations
5. Risks and Manual Checks

## Error Handling

- If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
- If `scripts/main.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
- Do not fabricate expiry dates, inventory counts, or reorder thresholds.

## Input Validation

This skill accepts: reagent barcode/ID and expiry date for logging, or a check-alerts request for inventory review.

If the request does not involve reagent expiry tracking — for example, asking for chemical hazard assessment, waste disposal guidance, or controlled substance management — do not proceed with the workflow. Instead respond:
> "reagent-expiry-alert is designed to log reagent expiry dates and generate alerts before expiration. Your request appears to be outside this scope. Please provide a reagent ID and expiry date, or use a more appropriate tool."

## Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

Related Skills

reagent-substitute-scout

53
from aipoch/medical-research-skills

Find validated alternative reagents based on literature citation data.

toxicity-structure-alert

53
from aipoch/medical-research-skills

Analyze data with `toxicity-structure-alert` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

skill-auditor

53
from aipoch/medical-research-skills

A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

two-sample-mr-research-planner

53
from aipoch/medical-research-skills

Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:"design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

research-proposal-generator

53
from aipoch/medical-research-skills

Generates a comprehensive research proposal design based on input literature, including hypothesis, mechanism verification, and budget. Use when the user wants to design a research project from a paper.

research-grants

53
from aipoch/medical-research-skills

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan's NSTC when you need agency-compliant narratives, budgets, and review-criteria alignment for a specific solicitation/FOA/BAA.

protocol-standardization

53
from aipoch/medical-research-skills

Standardize fragmented experimental steps into reproducible protocol documents when you need method organization, lab SOP drafting, or cross-operator reproducibility; missing parameters must be explicitly marked as "To be supplemented/Not provided".

prospero-registration-helper

53
from aipoch/medical-research-skills

Assists researchers in generating PROSPERO registration content for meta-analyses from a title and optional protocol. Use when the user wants to draft a PROSPERO registration form.

non-tumor-ml-research-planner

53
from aipoch/medical-research-skills

Generates complete non-tumor biomedical machine learning research designs from a user-provided research direction. Always use this skill when users want to plan bioinformatics + ML papers for non-cancer diseases (metabolic, cardiovascular, kidney, inflammatory, autoimmune, infectious, neurological, endocrine, wound healing, chronic multifactor), design diagnostic biomarker studies, combine GEO datasets with feature selection and ML modeling, or generate Lite/Standard/Advanced/Publication+ workload plans. Trigger for:"non-tumor ML study", "bioinformatics paper outside oncology", "key genes and diagnostic model for a disease", "pyroptosis/ferroptosis/senescence/autophagy + disease", "GEO datasets + machine learning", "RF + LASSO diagnostic model", "DEG + feature selection + validation", "immune infiltration + biomarker", "non-cancer biomarker paper". Trigger even for casual phrasings like "I want to study X using machine learning", "help me design a non-tumor bioinformatics paper", or "how do I build a diagnostic model for disease Y".

network-tox-docking-research-planner

53
from aipoch/medical-research-skills

Generates complete network toxicology + molecular docking research designs from a user-provided toxicant and disease/phenotype. Always use this skill when users want to investigate how an environmental toxicant, endocrine disruptor, heavy metal, food contaminant, pharmaceutical residue, or consumer product chemical may contribute to a disease through shared molecular targets, hub genes, pathways, and docking evidence. Trigger for:"network toxicology study", "toxicology mechanism paper", "target prediction + PPI + docking", "environmental pollutant and disease mechanism", "hub genes and docking for toxicant", "Lite/Standard/Advanced toxicology plan", "CTD + SwissTargetPrediction + GeneCards + STRING", "CB-Dock2 docking study", "triclosan/BPA/cadmium/PFAS + disease". Also triggers for Chinese phrasings:"网络毒理学研究设计"、"毒物机制论文"、"靶点预测+PPI+对接"、"环境污染物与疾病机制". Trigger even for casual phrasings like "I want to study how chemical X affects disease Y" or "help me design a toxicology paper". Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

meta-protocol-writer

53
from aipoch/medical-research-skills

Generates a PROSPERO-compliant Meta-analysis protocol based on Title and PICOS. Use when the user wants to write a protocol for a systematic review or meta-analysis.

hypothesis-generation

53
from aipoch/medical-research-skills

Structured scientific hypothesis formulation from observations; use when you have experimental observations or preliminary data and need testable hypotheses with predictions, mechanisms, and validation experiments.