Agent skill

research-processing

Use when adding external sources to research library - normalizes source, extracts insights, assigns topic, validates integrity, and writes to datasets/research/{topic}/

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/research-processing

SKILL.md

Research Processing

Purpose

Add external sources to organized research library:

  • Normalize source format (file, URL, or note)
  • Extract key insights and strategic applications
  • Assign topic category
  • Validate source integrity
  • Write to datasets/research/{topic}/

When to Use

Activate when:

  • User invokes /research:process-source
  • Adding external article, report, or framework
  • Building research context for strategy sessions

Workflow

1. Accept Source Input

Input formats:

  • --url='https://...': Fetch external URL
  • --file='/path/to/file.pdf': Local file
  • --from-chat: Use conversation context as note

2. Normalize Source

Invoke: source-normalization skill

Outputs:

  • Stable ID (src_{hash})
  • Checksum calculation
  • Metadata extraction (title, author, date)
  • Saved to temp location

3. Extract Content

For URLs:

  • Fetch with WebFetch
  • Extract title from or H1
  • Convert HTML to markdown
  • Save cleaned content

For files:

  • Read content
  • Extract metadata from frontmatter if present
  • Copy to research library

For notes:

  • Use conversation text
  • Generate title from first sentence
  • Save as markdown

4. Extract Insights

Interactive or auto:

Ask user (or analyze):

  • What are the key insights? (3-5 bullets)
  • How does this apply strategically? (use cases)
  • Notable quotes to preserve?
  • Related internal context? (meetings, epics)

Structure:

markdown
## Key Insights
- Insight 1
- Insight 2
- Insight 3

## Strategic Applications
- How this informs decisions
- Relevant use cases

## Citations / Quotes
> "Verbatim quote"
> — Author, Source (Date)

## Related Internal Links
- [Meeting](datasets/meetings/...)
- [Epic](datasets/product/epics/...)

5. Assign Topic

Topics:

  • competitive-analysis
  • pricing-strategy
  • market-positioning
  • product-strategy
  • customer-segmentation
  • growth-strategy

Ask user or auto-assign based on content.

6. Set Expiry Date

Apply guidelines:

  • Frameworks: 365-730 days (1-2 years)
  • Market data: 90-180 days (3-6 months)
  • Competitor intel: 180-365 days (6-12 months)
  • Customer insights: 180-365 days (6-12 months)

7. Validate Source Integrity

Invoke: source-integrity skill

  • Verify checksum calculated
  • Verify all required metadata present
  • Verify expiry_date set appropriately
  • Validate schema compliance

8. Write to Research Library

Output: datasets/research/{topic}/{filename}.md

Filename: {YYYYMMDD}_{slug_from_title}.md

Full YAML frontmatter:

yaml
---
title: "Source Title"
kind: "url" | "file" | "note"
topic: "{topic}"
url: "https://..." (if URL)
checksum: "sha256:..."
added_utc: "2025-10-21T..."
expiry_date: "YYYY-MM-DD"
author: "..." (if available)
published_date: "..." (if available)
tags: ["keyword1", "keyword2"]
---

Success Criteria

  • Source normalized with stable ID
  • Insights extracted (key insights, strategic applications)
  • Topic assigned
  • Expiry date set
  • Source integrity validated
  • Written to datasets/research/{topic}/

Related Skills

  • source-normalization: Normalizes input format
  • source-integrity: Validates metadata and checksum
  • research-gathering: Uses processed sources for strategy sessions

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