/doc-consolidation

name: doc-consolidation
description: Merges ephemeral report and analysis artifacts into permanent documentation. Use when LLM-generated markdown files have accumulated and need consolidation.
globs: "**/*.md"
alwaysApply: false
category: documentation
tags:
- docs
- consolidation
- cleanup
- git-hygiene
- knowledge-management
status: stable
created: 2025-12-06
updated: 2025-12-06
modules:
- modules/candidate-detection.md
- modules/content-analysis.md
- modules/destination-routing.md
- modules/merge-execution.md
model_hint: standard

Table of Contents

• [When to Use](#when-to-use)
• [Quick Start](#quick-start)
• [Two-Phase Workflow](#two-phase-workflow)
• [Phase 1: Triage (Fast Model)](#phase-1:-triage-(fast-model))
• [Phase 2: Execute (Main Model)](#phase-2:-execute-(main-model))
• [Workflow Details](#workflow-details)
• [Step 1: Candidate Detection](#step-1:-candidate-detection)
• [Step 2: Content Analysis](#step-2:-content-analysis)
• [Step 3: Destination Routing](#step-3:-destination-routing)
• [Step 4: Generate Plan](#step-4:-generate-plan)
• [Source: API_REVIEW_REPORT.md](#source:-api_review_reportmd)
• [Post-Consolidation](#post-consolidation)
• [Step 5: Execute Merges](#step-5:-execute-merges)
• [Fast Model Delegation](#fast-model-delegation)
• [Content Categories](#content-categories)
• [Merge Strategies](#merge-strategies)
• [Intelligent Weave](#intelligent-weave)
• [Replace Section](#replace-section)
• [Append with Context](#append-with-context)
• [Create New File](#create-new-file)
• [Integration](#integration)
• [Example Session](#example-session)
• [Troubleshooting](#troubleshooting)
• [No candidates found](#no-candidates-found)
• [Low-quality extractions](#low-quality-extractions)
• [Merge conflicts](#merge-conflicts)
• [Related Skills](#related-skills)

Doc Consolidation

Extracts valuable knowledge from ephemeral LLM outputs and merges it into permanent documentation.

When To Use

Use this skill when:

• You have untracked `*_REPORT.md` or `*_ANALYSIS.md` files from Claude sessions
• Git status shows markdown files that shouldn't be committed but contain useful content
• You want to preserve insights from code reviews, refactoring reports, or API audits
• Preparing a PR and need to clean up working artifacts

Do NOT use when:

• Files are already in proper documentation locations

(`docs/`, `skills/`)

• Files are intentionally temporary scratch notes
• User explicitly wants to preserve the original report format
• Source files have no extractable value (pure log output)

When NOT To Use

• Updating docs to match a code change (use `sanctum:doc-updates`)
• Detecting AI patterns in prose (use `scribe:slop-detector`)

Formatting

When merging content into permanent documentation, follow `Skill(leyline:markdown-formatting)` conventions: wrap prose at 80 chars (prefer sentence/clause boundaries), blank lines around headings, ATX headings only, blank line before lists, and reference-style links for long URLs.

Quick Start

/consolidate-docs

Or invoke directly:

I have some report files that need consolidating into permanent docs.

Two-Phase Workflow

Phase 1: Triage (Fast Model)

Read-only analysis to generate a consolidation plan:

1. **Detect candidates** - Find untracked markdown files with LLM output markers 2. **Analyze content** - Extract and categorize valuable sections 3. **Route destinations** - Match content to existing docs or propose new files 4. **Present plan** - Show user what will be consolidated and where

**Checkpoint**: User reviews and approves plan before execution.

Phase 2: Execute (Main Model)

After approval, performs the consolidation:

1. **Merge content** - Weave into existing docs or create new files 2. **Delete sources** - Remove ephemeral files after successful merge 3. **Generate summary** - Report what was created/updated/deleted

Workflow Details

Step 1: Candidate Detection

Load: `@modules/candidate-detection.md`

Identifies files using:

• Git status (untracked `.md` files)
• Location (not in standard doc directories)
• Naming (ALL_CAPS non-standard names)
• Content markers (Executive Summary, Findings, Action Items)

Step 2: Content Analysis

Load: `@modules/content-analysis.md`

For each candidate:

• Extract sections as content chunks
• Categorize: Actionable Items, Decisions, Findings, Metrics, Migration Guides, API Changes
• Score value: high/medium/low

Step 3: Destination Routing

Load: `@modules/destination-routing.md`

For each valuable chunk:

• Semantic match against existing documentation
• Apply default mappings if no good match
• Determine merge strategy (weave, replace, append, create)

Step 4: Generate Plan

Present consolidation plan to user:

# Consolidation Plan

## Source: API_REVIEW_REPORT.md

| Content | Category | Value | Destination | Action |
|---------|----------|-------|-------------|--------|
| API inventory | Findings | High | docs/api-overview.md | Create |
| Action items | Actionable | High | docs/plans/2025-12-06-api.md | Create |

### Post-Consolidation
- Delete: API_REVIEW_REPORT.md

Proceed with consolidation? [Y/n]

Step 5: Execute Merges

Load: `@modules/merge-execution.md`

After user approval:

• Group operations by destination file
• Apply merge strategies
• Validate results (frontmatter intact, structure preserved)
• Delete source files
• Generate execution summary

Fast Model Delegation

Phase 1 tasks are delegated to haiku-class models for efficiency:

# plugins/sanctum/scripts/consolidation_planner.py handles:
-scan_for_candidates()
-extract_content_chunks()
-categorize_chunks()
-score_value()
-find_semantic_matches()

Phase 2 stays on the main model for careful merge execution.

Content Categories

| Category | Description | Default Destination | |----------|-------------|---------------------| | Actionable Items | Tasks, TODOs, next steps | `docs/plans/YYYY-MM-DD-{topic}.md` | | Decisions