file-save-protocol

Name: file-save-protocol
Availability: InStock
Author: orientpine

Write analysis results as JSON and Markdown to a specified output_path.

33|10|Updated Jan 6, 2026

Search Tags:#markdown #audit #analysis #json #save #output #write

Ready to run

npx skills add https://github.com/orientpine/honeypot --skill file-save-protocol

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: file-save-protocol
Source: https://github.com/orientpine/honeypot/tree/main/plugins/investments-portfolio/skills/file-save-protocol
Command: npx skills add https://github.com/orientpine/honeypot --skill file-save-protocol

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analysis workflows risk data loss and hallucination when results are not persisted. This skill defines a strict file-save protocol that ensures every analysis result is written to disk using the Write tool, recording both JSON and markdown summaries for auditing and reproducibility.

Core Features & Use Cases

Enforces mandatory persistence of analysis outputs to a coordinator-provided output_path.
Generates JSON data alongside a human-readable Markdown summary for quick reviews and audits.
Use Case: In multi-agent analysis, each step writes its outputs and validation artifacts to a shared folder, enabling session resumption and traceability.

Quick Start

Save the latest analysis result to the specified output_path and verify the write operation succeeds.

Frequently Asked Questions about file-save-protocol

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I ensure analysis results are saved reliably to disk?▼

To ensure analysis results are saved reliably, apply a strict file-save protocol that writes outputs to a specified path using the Write tool, verifying success and returning a structured FAIL response with details if the operation fails.

Why save analysis outputs in both JSON and Markdown formats?▼

Saving analysis outputs in both JSON and Markdown generates structured data alongside a human-readable summary, enabling quick reviews and auditing while ensuring reproducibility across end-to-end workflows.

How to prevent data loss in multi-agent analysis workflows?▼

Prevent data loss in multi-agent analysis workflows by enforcing mandatory persistence, where each step writes validation artifacts and outputs to a shared folder, enabling session resumption and traceability.

What happens when writing analysis results to an output_path fails?▼

When writing analysis results to an output_path fails, the file-save protocol returns a structured FAIL response containing specific failure details, ensuring errors are captured during the disk write operation.

Do I need a coordinator to specify the output_path for saving analysis data?▼

Yes, the file-save protocol persists outputs to a coordinator-provided output_path, ensuring the analysis results are written to the correct designated location for auditing and reproducibility.