Doc Consolidation

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)
• Check the audience tier before routing. A destination serves one reader, and merging across tiers is how a getting-started page acquires an audit table. Report-derived findings, metrics, and rationale are expert content: route them to docs/deep-dive/<topic>.md, an ADR, or a benchmarks page, then link from the newcomer document rather than weaving into it. Tier table and cut test: scribe:slop-detector module audience-targeting.md. When the destination's own tier is unclear, ask, do not guess.

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.