compound-docs

Convert solved research problems into Markdown files with YAML frontmatter.

11|2|Updated Feb 26, 2026
One-click install
npx skills add https://github.com/James-Traina/compound-science --skill compound-docs-james-traina
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: compound-docs
Source: https://github.com/James-Traina/compound-science/tree/main/skills/compound-docs
Command: npx skills add https://github.com/James-Traina/compound-science --skill compound-docs-james-traina

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill captures solved research problems and converts them into structured, searchable documentation using YAML frontmatter, enabling rapid reuse in future sessions.

Core Features & Use Cases

  • Categorized documentation: each solved problem is saved as a single Markdown file under a category directory (e.g., docs/solutions/estimation-issues/...).
  • Frontmatter-driven searchability: metadata fields like component, date, category, symptoms, root_cause, and solution enable precise querying and pattern detection.
  • Future-proof knowledge base: documents can be used by agents like learnings-researcher to accelerate subsequent analyses and reproduce results.

Quick Start

Document a solved research problem by creating a categorized Markdown file under docs/solutions with YAML frontmatter.

Frequently Asked Questions about compound-docs

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

FAQPage Schema
How do I document solved research problems for future lookup?▼

Structured documentation captures solved research problems as categorized Markdown files with YAML frontmatter, using metadata fields like component, date, category, symptoms, root_cause, solution, and prevention to enable precise querying and pattern detection for future lookup.

What YAML frontmatter fields are needed for a research knowledge base?▼

Building a research knowledge base requires YAML frontmatter fields including component, date, category, symptoms, root_cause, solution, and prevention to trigger cross-references and organized search across estimation, data, numerical, methodology, derivation, and replication issues.

How does YAML frontmatter pattern detection work for research documentation?▼

Pattern detection in research documentation works by applying consistent YAML frontmatter fields like symptoms, root_cause, and category across Markdown files, enabling agents to query and identify recurring issues across estimation, data, numerical, methodology, derivation, and replication problems.

Can I use categorized Markdown files to reproduce past research results?▼

Categorized Markdown files with structured YAML frontmatter enable reproduction of past research results by saving solutions under docs/solutions with metadata fields, allowing agents like learnings-researcher to accelerate subsequent analyses and replicate findings.

What's the best way to structure a documentation workflow for research issues?▼

The best way to structure a research documentation workflow is saving each solved problem as a single Markdown file under a category directory within docs/solutions, using YAML frontmatter fields like component, symptoms, root_cause, and solution to enable future cross-referencing and organized search.

Do I need any dependencies to create searchable research documentation?▼

Creating searchable research documentation requires no dependencies, only Markdown files with YAML frontmatter under category directories, using fields like component, date, category, symptoms, root_cause, solution, and prevention to build a reusable knowledge base for future sessions.