Agent Skillsaipoch/medical-research-skills › protocol-standardization

protocol-standardization

GitHub

将碎片化的实验笔记转化为标准化、可复现的实验协议文档。适用于实验室SOP起草、多人员操作统一及新人培训,强制标记缺失参数以确保严谨性。

scientific-skills/Protocol Design/protocol-standardization/SKILL.md aipoch/medical-research-skills

Trigger Scenarios

整理混乱的实验记录为正式协议 编写跨操作员的标准操作程序(SOP) 审计现有方法以识别缺失参数 新人入职培训时的步骤澄清

Install

npx skills add aipoch/medical-research-skills --skill protocol-standardization -g -y
More Options

Non-standard path

npx skills add https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Protocol Design/protocol-standardization -g -y

Use without installing

npx skills use aipoch/medical-research-skills@protocol-standardization

指定 Agent (Claude Code)

npx skills add aipoch/medical-research-skills --skill protocol-standardization -a claude-code -g -y

安装 repo 全部 skill

npx skills add aipoch/medical-research-skills --all -g -y

预览 repo 内 skill

npx skills add aipoch/medical-research-skills --list

SKILL.md

Frontmatter
{
    "name": "protocol-standardization",
    "author": "AIPOCH",
    "license": "MIT",
    "description": "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\"."
}

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You have messy notes (chat logs, notebook fragments, bullet points) and need a formal, reproducible experimental protocol.
  • You are preparing a lab SOP for standardization across multiple operators or sites.
  • You need to convert exploratory/iterative experimental steps into a structured method for documentation or publication support.
  • You are onboarding new team members and must clarify steps, parameters, and checkpoints without inventing missing information.
  • You are auditing an existing method to identify missing critical parameters and quality-control points.

Key Features

  • Converts fragmented experimental steps into a standardized protocol structure (prep → execution → closing).
  • Enforces parameter completeness for reproducibility (e.g., temperature, time, concentration, volume, mixing/rotation speed).
  • Prohibits fabrication: any unknown or uncertain information is explicitly labeled as "To be supplemented/Not provided".
  • Unifies units, terminology, and step formatting for consistency.
  • Adds operational checks: logical order, safety notes, and waste disposal requirements.
  • Supports structured output using reusable templates (see assets/protocol_template.md).
  • Provides format guidance and checklists via references/guide.md.

Dependencies

  • None (documentation-only skill; no runtime dependencies specified).

Example Usage

Input (raw experimental notes)

Goal: Protein precipitation and cleanup (example)

Materials/Reagents

  • Sample lysate: 200 µL
  • Acetone (pre-chilled): volume not recorded
  • Microcentrifuge tubes
  • Centrifuge

Steps (as recorded)

  1. Add acetone to lysate, mix.
  2. Put at cold temperature for a while.
  3. Spin down, remove supernatant.
  4. Dry pellet, then resuspend.

Output (standardized protocol)

Title: Protein Precipitation by Cold Acetone (Standardized Protocol)

Purpose

  • Precipitate proteins from lysate for downstream analysis.

Materials/Reagents

  • Sample lysate: 200 µL
  • Acetone (pre-chilled): To be supplemented/Not provided (final ratio/volume)
  • Microcentrifuge tubes
  • Centrifuge (model/rotor: To be supplemented/Not provided)

Equipment

  • Microcentrifuge capable of: To be supplemented/Not provided (×g or rpm)
  • Temperature control (ice bath / 4°C fridge / cold room): To be supplemented/Not provided

Procedure

A. Preparation

  1. Label microcentrifuge tubes with sample IDs.
  2. Pre-chill acetone to To be supplemented/Not provided (e.g., -20°C) and prepare a cold incubation environment (To be supplemented/Not provided).

B. Execution 3. Add pre-chilled acetone to 200 µL lysate at a ratio of To be supplemented/Not provided (e.g., 4:1 acetone:lysate). 4. Mix by To be supplemented/Not provided (vortex/inversion) for To be supplemented/Not provided (time). 5. Incubate at To be supplemented/Not provided (temperature) for To be supplemented/Not provided (time). 6. Centrifuge at To be supplemented/Not provided (×g or rpm) for To be supplemented/Not provided (time) at To be supplemented/Not provided (temperature). 7. Carefully remove and discard the supernatant without disturbing the pellet.

C. Closing 8. Air-dry pellet for To be supplemented/Not provided (time) until residual solvent is removed (do not overdry if resuspension is required). 9. Resuspend pellet in To be supplemented/Not provided (buffer type and volume) by To be supplemented/Not provided (pipetting/vortexing) for To be supplemented/Not provided (time).

Critical Parameters to Supplement

  • Acetone:lysate ratio (or acetone volume)
  • Incubation temperature and duration
  • Centrifugation force/time/temperature
  • Pellet drying time
  • Resuspension buffer and volume

Quality Control / Checkpoints

  • Pellet presence after centrifugation: Expected (yes/no criteria to be supplemented)
  • Supernatant clarity: To be supplemented/Not provided
  • Resuspension completeness: To be supplemented/Not provided

Safety & Waste Disposal

  • Acetone handling: To be supplemented/Not provided (PPE/ventilation requirements)
  • Solvent waste disposal route: To be supplemented/Not provided

Suggested Output Location

  • outputs/ProteinPrecipitation_Acetone.txt (example naming)

Implementation Details

  • Workflow Structure

    1. Step Review: Collect all steps/materials; classify into preparation, execution, and closing phases.
    2. Parameter Completion: Identify required parameters (time, temperature, concentration, volume, mixing/rotation speed, centrifugation force, etc.).
      • If missing/uncertain, do not infer; mark as "To be supplemented/Not provided" and list fields requiring supplementation.
    3. Standardization and Organization: Rewrite into a consistent protocol format; unify units and terminology.
    4. Output Check: Validate logical sequence and operability; add safety and waste disposal notes.
  • Parameter Rules

    • Never fabricate values.
    • Use consistent units (e.g., °C, min, mL/µL, mM, ×g or rpm).
    • Explicitly surface “critical control points” (steps where parameter deviations affect outcomes).
  • Templates and References

    • Protocol template: assets/protocol_template.md
    • Output formats, checklists, and key checkpoints: references/guide.md
  • Output Path and Naming

    • Default output directory: outputs/
    • Naming convention: {Experiment_Info_Abbreviation}.txt

When Not to Use

  • Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
  • Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
  • Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

Field Required Format/Source Example If Missing
User task description Yes Text Research question, writing goal, analysis objective Stop and ask user to provide
Primary input material Depends on task Text, file path, ID, table, or literature PMID, PDF, CSV, DOCX, keywords, etc. Specify which material type is missing
Output preference No Text Language, format, target journal, template Use skill default format

Output Contract

  • Primary output: Structured result or target file aligned with this skill's objective.
  • Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
  • Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
  • If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

  • Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
  • Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
  • Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

  • Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
  • Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Input Validation

This skill accepts requests that match the documented purpose of protocol-standardization and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

protocol-standardization only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Quick Validation

  • Check that key scripts, templates, or reference file paths this skill depends on exist.
  • Check that the final output contains the core fields, sections, or files specified for this task.
  • Check that results clearly mark assumptions, limitations, and incomplete items.

Version History

  • f5ef65b Current 2026-07-24 17:31

Same Skill Collection

awesome-med-research-skills/Academic Writing/author-response-builder/SKILL.md
awesome-med-research-skills/Academic Writing/claim-strength-calibrator/SKILL.md
awesome-med-research-skills/Academic Writing/conference-abstract-writer/SKILL.md
awesome-med-research-skills/Academic Writing/consistency-checker-across-manuscript/SKILL.md
awesome-med-research-skills/Academic Writing/cover-letter-drafter/SKILL.md
awesome-med-research-skills/Academic Writing/discussion-composer/SKILL.md
awesome-med-research-skills/Academic Writing/figure-legend-writer/SKILL.md
awesome-med-research-skills/Academic Writing/grant-specific-aims-writer/SKILL.md
awesome-med-research-skills/Academic Writing/graphical-abstract-generator/SKILL.md
awesome-med-research-skills/Academic Writing/introduction-logic-builder/SKILL.md
awesome-med-research-skills/Academic Writing/introduction-section-writer/SKILL.md
awesome-med-research-skills/Academic Writing/latex-manuscript-format-converter/SKILL.md
awesome-med-research-skills/Academic Writing/medical-english-precision-editor/SKILL.md
awesome-med-research-skills/Academic Writing/paper-sprint-review/SKILL.md
awesome-med-research-skills/Academic Writing/poster-storyline-builder/SKILL.md
awesome-med-research-skills/Academic Writing/reference-integrity-checker/SKILL.md
awesome-med-research-skills/Academic Writing/reporting-guideline-compliance-checker/SKILL.md
awesome-med-research-skills/Academic Writing/results-section-structurer/SKILL.md
awesome-med-research-skills/Academic Writing/results-section-writer/SKILL.md
awesome-med-research-skills/Academic Writing/revision-strategy-planner/SKILL.md
awesome-med-research-skills/Academic Writing/slide-deck-for-lab-meeting/SKILL.md
awesome-med-research-skills/Academic Writing/table-narrative-writer/SKILL.md
awesome-med-research-skills/Academic Writing/target-journal-matcher/SKILL.md
awesome-med-research-skills/Academic Writing/title-and-abstract-optimizer/SKILL.md
awesome-med-research-skills/Data Analysis/batch-effect-correction/SKILL.md
awesome-med-research-skills/Data Analysis/cerna-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/cibersort-immune-infiltration-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/consensus-clustering-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/decision-curve-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/decision-tree-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/deg-screening-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/differential-expression-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/elastic-net-feature-selection/SKILL.md
awesome-med-research-skills/Data Analysis/estimate-immune-score-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/external-model-validation/SKILL.md
awesome-med-research-skills/Data Analysis/gene-protein-expression-matrix-normalization/SKILL.md
awesome-med-research-skills/Data Analysis/gokegg/SKILL.md
awesome-med-research-skills/Data Analysis/gsea/SKILL.md
awesome-med-research-skills/Data Analysis/gsva-analysis-and-visualization/SKILL.md
awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot/SKILL.md
awesome-med-research-skills/Data Analysis/immune-pathway-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/km-survival-curve/SKILL.md
awesome-med-research-skills/Data Analysis/knn-imputation/SKILL.md
awesome-med-research-skills/Data Analysis/lasso-logistics-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/LightGBM-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/lncrna-regulatory-network-construction-analysis/SKILL.md
awesome-med-research-skills/Data Analysis/model-calibration-curve/SKILL.md
awesome-med-research-skills/Data Analysis/nomogram-construction/SKILL.md
awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction/SKILL.md
awesome-med-research-skills/Data Analysis/ppi-network-analysis/SKILL.md

Metadata

Files
0
Version
f5ef65b
Hash
d8610071
Indexed
2026-07-24 17:31

inicio - Wiki
Copyright © 2011-2026 iteam. Current version is 2.155.2. UTC+08:00, 2026-08-16 20:31
浙ICP备14020137号-1 $mapa de visitantes$