/knowledge-intake

name: knowledge-intake
description: Processes external resources into stored knowledge with quality scoring and routing. Use when ingesting articles, papers, or docs into a memory palace.
alwaysApply: false
category: governance
tags:
- knowledge-management
- intake
- evaluation
- curation
- external-resources
dependencies:
- memory-palace-architect
- digital-garden-cultivator
- leyline:evaluation-framework
- leyline:storage-templates
- leyline:document-conversion
- scribe:slop-detector
scripts: []
usage_patterns:
- resource-intake
- knowledge-evaluation
- application-routing
complexity: intermediate
estimated_tokens: 950

Knowledge Intake

Process external resources into the knowledge store. When a user links an article, blog post, or paper, this skill guides evaluation, storage decisions, and application routing.

When To Use

• Capturing and organizing knowledge from sessions
• Ingesting information into structured memory palaces

When NOT To Use

• Temporary notes that do not need long-term storage
• Code-only changes without knowledge capture needs

What It Is

A knowledge governance framework that answers three questions for every external resource: 1. **Is it worth storing?** - Evaluate signal-to-noise and relevance 2. **Where does it apply?** - Route to local codebase or meta-infrastructure 3. **What does it displace?** - Identify outdated knowledge to prune

The Intake Signal

> When a user links an external resource, it is a signal of importance.

The act of sharing indicates the resource passed the user's own filter. Our job is to:

• Extract the essential patterns and insights
• Determine appropriate storage location and format
• Connect to existing knowledge structures
• Identify application opportunities

Quick Start

When a user shares a link:

1. FETCH    → Detect format, retrieve and convert content
2. EVALUATE → Apply importance criteria
3. DECIDE   → Storage location and application type
4. STORE    → Create structured knowledge entry
5. VALIDATE → Scribe verification (slop scan + doc verify)
6. CONNECT  → Link to existing palace structures
7. PROMOTE  → Offer Discussion promotion (score 80+)
8. APPLY    → Route to codebase or infrastructure updates
9. PRUNE    → Identify displaced/outdated knowledge

Step 1: FETCH with Format Detection

Before retrieving content, detect the source format from the URL or file path to choose the right retrieval method.

**Web articles and blog posts** (default path): Use WebFetch to retrieve HTML content directly. No conversion needed.

**Document URLs** (PDF, DOCX, PPTX, XLSX): Apply the `leyline:document-conversion` protocol. This tries the markitdown MCP tool first for high-quality markdown, then falls back to native Claude Code tools (Read for PDFs, etc.), then informs the user if the format is unsupported without markitdown.

**Local files** (user shares a file path): Construct a `file://` URI from the absolute path and apply the `leyline:document-conversion` protocol.

**Format detection heuristics:**

| URL Pattern | Format | Retrieval | |-------------|--------|-----------| | `*.pdf`, `arxiv.org/pdf/*` | PDF | document-conversion | | `*.docx`, `*.doc` | Word | document-conversion | | `*.pptx`, `*.ppt` | PowerPoint | document-conversion | | `*.xlsx`, `*.xls` | Excel | document-conversion | | `*.epub` | E-book | document-conversion | | `drive.google.com/*` | Various | document-conversion | | Everything else | HTML/web | WebFetch (existing) |

After retrieval (regardless of method), wrap the content in external content boundary markers per `leyline:content-sanitization` before proceeding to Step 2 (EVALUATE).

Step 5: Scribe Validation (Required)

**All knowledge corpus entries MUST pass scribe validation before finalizing.**

Run `Skill(scribe:slop-detector)` on the new entry:

• Score must be < 2.5 (Clean to Light)
• No Tier 1 markers (delve, tapestry, comprehensive, leveraging, etc.)
• Hedge word density < 15 per 1000 words

Use `Agent(scribe:doc-verifier)` to validate:

• All file paths and URLs exist
• All cross-references valid
• Source attributions accurate

# Quick validation for knowledge corpus entry
/slop-scan docs/knowledge-corpus/[entry-name].md
# Doc verification is now agent-only:
Agent(scribe:doc-verifier) "Verify docs/knowledge-corpus/[entry-name].md"

**DO NOT finalize entries with slop score > 2.5** - rewrite with concrete specifics.

Step 7: Discussion Promotion (Score 80+ Only)

When the evaluation score is 80-100 (evergreen), you MUST execute the Discussion promotion workflow. If the score is below 80, skip this step entirely.

**Execute these steps in order:**

1. Read `modules/discussion-promotion.md` for the full GraphQL workflow 2. Tell the user: "This entry has reached evergreen maturity. Publishing to GitHub Discussions. [Y/n]" 3. If the user says "n", skip to Step 8 (APPLY) 4. Run the `gh api graphql` commands from the module to create or update a Discussion in the "Knowledge" category 5. Update the local corpus entry with `discussion_url`

• If the entry already has a `discussion_url` field,

update the existing Discussion instead of creating a new one

• If `gh` is unavailable or promotion fails, warn

the user and continue to Step 8 (APPLY)

Publishing is the default for qualifying entries. It never blocks the intake workflow.

Evaluation Framework

Importance Criteria

| Criterion | Weight | Questions | |-----------|--------|-----------| | **Novelty** | 25% | Does this introduce new patterns or concepts? | | **Applicability** | 30% | Can we apply this to current work? | | **Durability** | 20% | Will this remain relevant in 6+ months? | | **Connectivity** | 15% | Does it connect to multiple existing concepts? | | **Authority** | 10% | Is the source credible and well-reasoned? |

Scoring Guide

• **80-100**: Evergreen knowledge, store prominently, apply immediately
• **60-79**: Valuable insight, store in corpus, schedule ap