Skill Memory

Memory Skill (Direct Execution)

Direct execution skill for memory vault management. Handles memory creation, similarity search, classification, and index maintenance through content mapping, MCP-based deduplication, and three memory operations (UPDATE, EXTEND, CREATE).

MANDATORY INTERACTIVE REQUIREMENT -- DO NOT SKIP:

• STOP at Step 4 and call AskUserQuestion to show files. Write NOTHING to disk until user responds.
• STOP at Memory Search and call AskUserQuestion for each segment. Write NOTHING to disk until user responds.
• These are not optional. Running autonomously without user input is a critical failure.

Context References

Reference (do not load eagerly):

• Path: @.memory/30-Templates/memory-template.md - Memory template
• Path: @.memory/20-Indices/index.md - Memory index
• Path: @.memory/memory-index.json - Machine-queryable memory index
• Path: @.opencode/context/project/memory/learn-usage.md - Usage guide

Execution Modes

All non-task modes flow through: Content Mapping -> Memory Search -> Memory Operations

Content Mapping

Content mapping is the intermediate representation between input acquisition and memory operations. It segments input into topic-aligned chunks that can be matched against existing memories.

Content Map Data Structure

{
  "source": {
    "type": "text|file|directory",
    "path": "/path/to/input",
    "total_tokens": 2500
  },
  "segments": [
    {
      "id": "seg-001",
      "topic": "python/libs/requests",
      "source_file": "/path/to/file.md",
      "source_lines": "15-42",
      "summary": "HTTP request retry pattern with backoff",
      "estimated_tokens": 350,
      "key_terms": ["requests", "retry", "backoff", "session", "timeout"]
    }
  ]
}

Field Descriptions

Segmentation Algorithms

Structured Files (Markdown)

Split at heading boundaries:

1. Identify all headings (# ## ### ####)
2. Each heading starts a new segment
3. Segment includes all content until next same-or-higher level heading
4. Top-level content before first heading becomes its own segment

Structured Files (Code)

Split at blank-line-separated blocks:

1. Identify function/class definitions
2. Group related comments with their definitions
3. Separate standalone comment blocks as documentation segments
4. Keep import/require blocks together

Unstructured Text

Split at paragraph boundaries with topic grouping:

1. Split at double-newline (paragraph boundaries)
2. Group adjacent paragraphs with keyword overlap >40%
3. Single-sentence paragraphs merge with adjacent

Directory Input

Each file becomes an initial segment, then large files are split:

1. Each file is an initial segment
2. Files >800 tokens are split at section boundaries
3. Files <100 tokens are candidates for merging with related files

Small-Input Bypass

Inputs under 500 tokens skip segmentation and become a single segment:

if total_tokens < 500:
  segments = [{
    "id": "seg-001",
    "topic": inferred_topic,
    "summary": first_line_or_60_chars,
    "estimated_tokens": total_tokens,
    "key_terms": extract_keywords(content, 5)
  }]

Segment Size Guidelines

Key Term Extraction

Extract 3-5 significant terms per segment:

1. Remove stop words (the, a, is, are, etc.)
2. Extract nouns and technical terms (>4 characters)
3. Prioritize: proper nouns > technical terms > common nouns
4. Deduplicate (case-insensitive)
5. Return top 5 by frequency within segment

Memory Search

After content mapping, each segment is matched against existing memories to determine the appropriate operation (UPDATE, EXTEND, or CREATE).

MCP Search Path

When MCP server is available, use the execute pattern:

For each segment in content_map.segments:
  query = segment.key_terms.join(" ")
  results = execute("search", {
    "query": query,
    "vault": ".memory",
    "limit": 5
  })

Grep Fallback Path

When MCP is unavailable, use keyword-based file search:

# For each segment
for keyword in $key_terms; do
  grep -l -i "$keyword" .memory/10-Memories/*.md 2>/dev/null
done | sort | uniq -c | sort -rn | head -5

Overlap Scoring

Score keyword overlap between segment and each matching memory:

overlap_score = |segment_terms intersect memory_terms| / |segment_terms|

Where:
- segment_terms = segment.key_terms
- memory_terms = keywords extracted from memory content (same algorithm)

Classification Thresholds

Search Result Presentation -- MANDATORY STOP

YOU MUST call AskUserQuestion for EACH segment before writing anything. Do NOT infer what the user wants. Do NOT skip segments. Do NOT write memory files without explicit user confirmation per segment.

Present each segment with related memories via AskUserQuestion:

Segment: {segment.summary}
Topic: {segment.topic}
Key terms: {segment.key_terms.join(", ")}

Related Memories:
1. MEM-requests-retry-patterns (72% overlap) -> Recommended: UPDATE
2. MEM-python-http-patterns (45% overlap) -> Recommended: EXTEND
3. MEM-api-error-handling (18% overlap) -> Recommended: CREATE (no strong match)

What would you like to do with this segment?
[ ] UPDATE MEM-requests-retry-patterns (replace content)
[ ] EXTEND MEM-python-http-patterns (append section)
[ ] CREATE new memory
[ ] SKIP - don't save this segment

Interactive Override

Users can override any recommendation:

• Change UPDATE to CREATE (preserve existing, create duplicate)
• Change EXTEND to UPDATE (replace instead of append)
• Skip any segment
• Merge segments before processing (combine into single memory)

Memory Operations

Three distinct operations for memory management:

UPDATE Operation

Replace memory content while preserving structure:

1. Read existing memory file
2. Preserve frontmatter: created (original), tags, topic
3. Update frontmatter: modified = today
4. Move current content to ## History section with date marker
5. Replace main content with new segment content
6. Preserve ## Connections section
7. Write updated memory

Template for UPDATE:

---
title: "{new_title_from_segment}"
created: {original_created}
tags: {merged_tags}
topic: "{existing_or_updated_topic}"
source: "{new_source}"
modified: {today}
---

# {new_title}

{new_content_from_segment}

## History

### Previous Version ({original_created})

{previous_content}

## Connections
{preserved_connections}

EXTEND Operation

Append new dated section without modifying existing content:

1. Read existing memory file
2. Find insertion point (before ## Connections, or end of file)
3. Add dated extension section
4. Update frontmatter: modified = today
5. Optionally update tags if new topics introduced
6. Write updated memory

Template for EXTEND:

## Extension ({today})

**Source**: {segment.source_file}

{segment_content}

CREATE Operation

Generate new memory from segment:

1. Generate semantic slug from topic and title:

   generate_slug() {
     local topic="$1"
     local title="$2"
     local base=""

     # Priority 1: Topic path (most specific segment)
     if [ -n "$topic" ]; then
       base=$(echo "$topic" | rev | cut -d'/' -f1 | rev)
     fi

     # Priority 2: First 2-3 words of title
     local title_slug=$(echo "$title" | tr '[:upper:]' '[:lower:]' | \
       sed 's/[^a-z0-9 ]/-/g' | tr ' ' '-' | \
       cut -d'-' -f1-3 | sed 's/-$//')

     # Combine
     if [ -n "$base" ]; then
       slug="${base}-${title_slug}"
     else
       slug="$title_slug"
     fi

     # Sanitize and truncate to 50 chars
     slug=$(echo "$slug" | sed 's/--*/-/g' | sed 's/^-//' | sed 's/-$//' | cut -c1-50)

     # Handle collision - NOTE: MEM- prefix preserved for grep discoverability
     local final_slug="$slug"
     local counter=2
     while [ -f ".memory/10-Memories/MEM-${final_slug}.md" ]; do
       final_slug="${slug}-${counter}"
       counter=$((counter + 1))
     done

     echo "$final_slug"
   }

   slug=$(generate_slug "$topic" "$title")
   filename="MEM-${slug}.md"

2. Apply memory template with all fields
3. Infer and apply topic
4. Add to index (both category and topic sections)
5. Write new memory file

Template for CREATE:

---
title: "{segment.summary}"
created: {today}
tags: {inferred_tags}
topic: "{segment.topic}"
source: "{segment.source_file or 'user input'}"
modified: {today}
keywords: {segment.key_terms}
summary: "{segment.summary}"
retrieval_count: 0
last_retrieved:
---

# {segment.summary}

{segment_content}

## Connections
<!-- Add links to related memories using [[filename]] syntax -->

Note: The MEM- prefix is preserved for grep discoverability (grep -r "MEM-" .memory/). Filenames follow the pattern MEM-{semantic-slug}.md (e.g., MEM-requests-retry-patterns.md).

Topic Inference

Infer topic using four-source priority:

1. Source directory path (highest priority)
   - /project/src/utils/ -> "project/utils"
   - /home/user/notes/python/ -> "python"

2. Keyword analysis
   - Extract domain indicators: python, requests, http, api
   - Map to topic: "python/libs" or "python/patterns"

3. Related memory topics
   - If UPDATE/EXTEND: inherit topic from target memory
   - If CREATE with high-overlap match: suggest that topic

4. User confirmation/override
   - Always present inferred topic for confirmation
   - User can modify or create new topic path

Index Maintenance

Note: After each operation, update all three indexes: index.md, .memory/10-Memories/README.md, and memory-index.json. See "JSON Index Maintenance" and "Index Regeneration Pattern" below.

After each operation, update both index.md and .memory/10-Memories/README.md:

index.md:

1. Add/update entry in "## By Category" under appropriate tag
2. Add/update entry in "## By Topic" under topic path
3. Update "## Recent Memories" (prepend, keep last 10)
4. Update "## Statistics" counts

.memory/10-Memories/README.md -- regenerate the full file listing:

1. List all MEM-*.md files in the directory (ls .memory/10-Memories/MEM-*.md)
2. For each file, extract: title, topic, tags, created from frontmatter
3. Rewrite README.md with updated count and one entry per memory:
   ### [MEM-{slug}](MEM-{slug}.md)
   **Title**: {title}
   **Topic**: {topic}
   **Tags**: {tags}
   **Created**: {created}
4. Keep "## Navigation" section at the bottom

Index Regeneration Pattern

To avoid concurrent write conflicts, regenerate index.md from filesystem state rather than append:

# 1. List all memory files
memories=$(ls .memory/10-Memories/MEM-*.md 2>/dev/null)

# 2. Extract metadata from each file
for mem in $memories; do
  title=$(grep -m1 "^title:" "$mem" | cut -d'"' -f2)
  topic=$(grep -m1 "^topic:" "$mem" | cut -d'"' -f2)
  created=$(grep -m1 "^created:" "$mem" | cut -d: -f2 | tr -d ' ')
  # Store for index generation
done

# 3. Regenerate index.md from extracted data
# Sort by date descending, write complete file

Benefits:

• No append conflicts (complete overwrite)
• Self-healing (missing entries recovered)
• Idempotent (multiple regenerations produce same result)

JSON Index Maintenance

After each CREATE, UPDATE, or EXTEND operation, regenerate .memory/memory-index.json from filesystem state:

# 1. Scan all memory files
memories=$(ls .memory/10-Memories/MEM-*.md 2>/dev/null)

# 2. For each file, extract frontmatter fields
for mem in $memories; do
  title=$(grep -m1 "^title:" "$mem" | sed 's/^title: *//' | tr -d '"')
  topic=$(grep -m1 "^topic:" "$mem" | sed 's/^topic: *//' | tr -d '"')
  created=$(grep -m1 "^created:" "$mem" | sed 's/^created: *//')
  modified=$(grep -m1 "^modified:" "$mem" | sed 's/^modified: *//')
  keywords=$(grep -m1 "^keywords:" "$mem" | sed 's/^keywords: *//')
  summary=$(grep -m1 "^summary:" "$mem" | sed 's/^summary: *//' | tr -d '"')
  retrieval_count=$(grep -m1 "^retrieval_count:" "$mem" | sed 's/^retrieval_count: *//')
  last_retrieved=$(grep -m1 "^last_retrieved:" "$mem" | sed 's/^last_retrieved: *//')
  status=$(grep -m1 "^status:" "$mem" | sed 's/^status: *//')
  # Default status to "active" when absent
  if [ -z "$status" ]; then status="active"; fi
  # Compute token_count: word_count * 1.3
  word_count=$(wc -w < "$mem")
  token_count=$(echo "$word_count * 1.3" | bc | cut -d. -f1)
  # Derive id from filename: MEM-{slug}.md -> MEM-{slug}
  id=$(basename "$mem" .md)
  # Derive category from first tag
  category=$(grep -m1 "^tags:" "$mem" | sed 's/^tags: *\[//' | cut -d, -f1 | tr -d '] ')
done

# 3. Build JSON structure
{
  "version": "1.0.0",
  "generated_at": "$(date +%Y-%m-%d)",
  "entry_count": N,
  "total_tokens": sum_of_token_counts,
  "entries": [...]
}

# 4. Write to .memory/memory-index.json (complete overwrite)

Schema Fields per Entry:

Validate-on-Read

Before using memory-index.json for retrieval or scoring, validate that the index matches the filesystem:

1. List all MEM-*.md files in .memory/10-Memories/
2. List all entry ids in memory-index.json
3. Compare:
   - Files on disk not in index -> INDEX STALE (missing entries)
   - Index entries with no file on disk -> INDEX STALE (orphaned entries)
   - All match -> INDEX VALID
4. If INDEX STALE: regenerate memory-index.json using JSON Index Maintenance procedure
5. If INDEX VALID: proceed with retrieval

This ensures the index is always consistent, even if manual file edits bypass the skill pipeline.

Status Field Handling: During regeneration, the status field is read from each memory's frontmatter. If absent, it defaults to "active". Tombstoned memories (with status: tombstoned in frontmatter) retain their "tombstoned" status in the regenerated index. The tombstoned_at and tombstone_reason fields are also preserved when present.

Task Mode Execution

Task mode has special handling for reviewing existing task artifacts.

Step 1: Locate Task Directory

task_num=$task_number
padded_num=$(printf "%03d" $task_num)
task_dir=$(ls -d specs/${padded_num}_* 2>/dev/null | head -1)

if [ -z "$task_dir" ]; then
  task_dir=$(ls -d specs/${task_num}_* 2>/dev/null | head -1)
fi

if [ -z "$task_dir" ]; then
  echo "Task directory not found: specs/${padded_num}_*"
  exit 1
fi

Step 2: Scan Artifacts

artifacts=$(find "$task_dir" -type f -name "*.md" | sort)

if [ -z "$artifacts" ]; then
  echo "No artifacts found for task ${task_number}"
  exit 1
fi

Step 3: Present Artifact List

Display via AskUserQuestion:

{
  "question": "Select artifacts to review for memory extraction:",
  "header": "Task Artifacts",
  "multiSelect": true,
  "options": [
    {
      "label": "{artifact_1_name}",
      "description": "{artifact_1_path}"
    }
  ]
}

Step 4: Process Through Content Mapping

For each selected artifact:

1. Read content
2. If >500 tokens: run through content mapping (segmentation)
3. If <=500 tokens: treat as single segment
4. Proceed to Memory Search (Phase 4)
5. Proceed to Memory Operations (Phase 5)

Step 5: Classification Taxonomy

For task artifacts, also present classification options:

{
  "question": "Classify this segment:",
  "header": "Classification: {segment.summary}",
  "multiSelect": false,
  "options": [
    {"label": "[TECHNIQUE]", "description": "Reusable method or approach"},
    {"label": "[PATTERN]", "description": "Design or implementation pattern"},
    {"label": "[CONFIG]", "description": "Configuration or setup knowledge"},
    {"label": "[WORKFLOW]", "description": "Process or procedure"},
    {"label": "[INSIGHT]", "description": "Key learning or understanding"},
    {"label": "[SKIP]", "description": "Not valuable for memory"}
  ]
}

Step 6: Return Result

{
  "status": "completed",
  "mode": "task",
  "artifacts_reviewed": [...],
  "content_map": { ... },
  "operations": [
    {"type": "CREATE", "memory_id": "MEM-...", "category": "[PATTERN]"}
  ],
  "memories_affected": 3
}

Directory Mode Execution

Directory mode scans a directory tree for learnable content.

Step 1: Recursive Scanning

# Exclusion patterns
EXCLUDES="-path '*/.git' -prune -o -path '*/node_modules' -prune -o -path '*/__pycache__' -prune -o -path '*/.obsidian' -prune"

# Find all files
files=$(find "$directory_path" $EXCLUDES -type f -print | head -250)

Step 2: Two-Tier Text Detection

Tier 1: Extension Whitelist

Recognized text extensions (alphabetized by category):

Tier 2: MIME-Type Fallback

For files without recognized extensions:

mime=$(file --mime-type -b "$file")
if [[ "$mime" == text/* ]]; then
  # Include file
fi

Step 3: Size Limits

# Per-file limit
if [ $(stat -c%s "$file") -gt 102400 ]; then
  echo "Skipping large file: $file (>100KB)"
  continue
fi

# Warning at 50 files
if [ ${#files[@]} -gt 50 ]; then
  echo "Warning: ${#files[@]} files found. Consider narrowing scope."
fi

# Hard limit at 200 files
if [ ${#files[@]} -gt 200 ]; then
  echo "Error: Too many files (${#files[@]}). Maximum is 200."
  echo "Narrow your path or use file mode for specific files."
  exit 1
fi

Step 4: File Selection (Paginated) -- MANDATORY STOP

YOU MUST call AskUserQuestion here. Do NOT skip to Step 5. Do NOT process any files until the user has made their selection.

Present files in pages of 10 to avoid overwhelming the display. Accumulate selections across all pages before processing.

selected_files = []
page_size = 10
total_files = len(files)
page = 0

while page * page_size < total_files:
  start = page * page_size
  end = min(start + page_size, total_files)
  page_files = files[start:end]
  remaining = total_files - end
  page_num = page + 1
  total_pages = ceil(total_files / page_size)

  # Build options for this page
  options = [{"label": relative_path, "description": file_size} for each file in page_files]

  # Add navigation options at the bottom
  if remaining > 0:
    options.append({"label": "--- Continue to next page ---", "description": f"{remaining} more files remaining"})

  AskUserQuestion({
    "question": f"Select files to include (page {page_num}/{total_pages}, showing {start+1}-{end} of {total_files}):",
    "header": f"Directory Scan: {directory_path}",
    "multiSelect": true,
    "options": options
  })

  # Add any selected files (excluding the navigation option) to accumulated list
  selected_files.extend(user_selections excluding navigation option)

  # If user selected "Continue to next page" OR there are more pages, advance
  # If user did NOT select "Continue to next page" on the last page, stop
  if "--- Continue to next page ---" not in user_selections and remaining > 0:
    # User is done selecting (didn't ask for more)
    break

  page += 1

# After all pages processed, confirm total selection
if len(selected_files) == 0:
  print("No files selected. Exiting.")
  exit

Example page 1 of 3:

{
  "question": "Select files to include (page 1/3, showing 1-10 of 28):",
  "header": "Directory Scan: /home/user/project/",
  "multiSelect": true,
  "options": [
    {"label": "README.md", "description": "4.1KB"},
    {"label": "src/main.lua", "description": "2.3KB"},
    {"label": "--- Continue to next page ---", "description": "18 more files remaining"}
  ]
}

Step 5: Route Through Pipeline

For each selected file:

1. Read file content
2. Run through content mapping (directory-type segmentation)
3. Route segments through memory search
4. Route through memory operations
5. Update index

Step 6: Return Result

{
  "status": "completed",
  "mode": "directory",
  "files_scanned": 45,
  "files_selected": 12,
  "content_map": { ... },
  "operations": [...],
  "memories_affected": 8
}

Text Mode Execution

Step 1: Parse Input

content="$text_content"
source="user input"

Step 2: Content Mapping

For text >500 tokens, segment at paragraph boundaries:

1. Split at double-newline
2. Group related paragraphs
3. Generate single content map

For text <500 tokens, create single segment.

Step 3: Memory Search & Operations

Route through standard memory search and operations pipeline.

Step 4: Return Result

{
  "status": "completed",
  "mode": "text",
  "content_map": { ... },
  "operations": [...],
  "memories_affected": 1
}

File Mode Execution

Step 1: Read File

if [ ! -f "$file_path" ]; then
  echo "File not found: $file_path"
  exit 1
fi

content=$(cat "$file_path")
source="file: $file_path"

Step 2: Content Mapping

Apply structured or unstructured segmentation based on file type.

Step 3: Memory Search & Operations

Route through standard pipeline.

Step 4: Return Result

{
  "status": "completed",
  "mode": "file",
  "file_path": "...",
  "content_map": { ... },
  "operations": [...],
  "memories_affected": 2
}

Error Handling

No Content Provided

Usage: /learn <text or file path or directory> OR /learn --task N

File Not Found

File not found: {path}

Directory Not Found

Directory not found: {path}

Empty Directory

No text files found in: {path}

Too Many Files

Too many files ({N}). Maximum is 200.
Narrow your path or use file mode for specific files.

Task Directory Not Found

Task directory not found: specs/{NNN}_*

User Cancels

Memory operation cancelled. No files created.

All Content Skipped

No memories created (all content skipped)

MCP Unavailable

MCP search unavailable. Using grep-based fallback.

Git Commit (Postflight)

After successful memory operations:

git add .memory/
git commit -m "memory: add/update ${memories_affected} memories

Session: ${session_id}

Mode: distill

Memory vault distillation: scoring, health reporting, and maintenance operations. Invoked by /distill command with mode=distill.

Prerequisites

Validate-on-Read: Before scoring, run the validate-on-read procedure from the "Validate-on-Read" section above to ensure memory-index.json is consistent with the filesystem. If stale, regenerate using the "JSON Index Maintenance" procedure before proceeding.

Sub-Mode Dispatch

All sub-modes are now available. No placeholder responses needed.

Scoring Engine

The scoring engine computes a composite maintenance score for each memory in the vault. Higher scores indicate memories that are better candidates for maintenance operations.

Input

Read all entries from .memory/memory-index.json after validate-on-read. Each entry provides:

• created (ISO date)
• modified (ISO date)
• last_retrieved (ISO date or null)
• retrieval_count (number)
• token_count (number)
• keywords (array of strings)

Component 1: Staleness Score (weight: 0.3)

Measures how long since the memory was last useful.

days_since_last = days_between(today, last_retrieved or created)

staleness = min(1.0, days_since_last / 90)

# FSRS adjustment: reduce staleness for actively retrieved old memories
if retrieval_count > 0 AND days_since_created > 60:
  staleness = max(0, staleness - 0.3)

• Range: 0.0 (fresh) to 1.0 (90+ days stale)
• FSRS adjustment rewards memories that have proven useful over time

Component 2: Zero-Retrieval Penalty (weight: 0.25)

Penalizes memories that have never been retrieved after a grace period.

if retrieval_count == 0 AND days_since_created > 30:
  zero_retrieval = 1.0
else:
  zero_retrieval = 0.0

• Binary: 0.0 (has retrievals or too new) or 1.0 (never retrieved, older than 30 days)

Component 3: Size Penalty (weight: 0.2)

Penalizes oversized memories that may benefit from compression.

size_penalty = max(0, (token_count - 600) / 600)

• Range: 0.0 (600 tokens or fewer) to unbounded (linear above 600)
• A 1200-token memory scores 1.0; a 300-token memory scores 0.0

Component 4: Duplicate Score (weight: 0.25)

Measures keyword overlap with the most similar other memory in the vault.

for each other_memory in vault:
  overlap = |memory.keywords intersect other_memory.keywords| / |memory.keywords|

duplicate = max(overlap across all other memories)

• Range: 0.0 (no keyword overlap) to 1.0 (complete keyword subset)
• Uses Jaccard-like ratio: intersection size divided by the memory's own keyword count

Composite Score

composite = (staleness * 0.3) + (zero_retrieval * 0.25) + (size_penalty * 0.2) + (duplicate * 0.25)
composite = clamp(composite, 0, 1)

• Weights sum to 1.0 (0.3 + 0.25 + 0.2 + 0.25)
• Range: 0.0 (healthy memory) to 1.0 (strong maintenance candidate)

Topic-Cluster Grouping

Group memories by topic cluster for the health report. The cluster key is the first path segment of the memory's topic field:

cluster_key = topic.split("/")[0]

# Example:
# topic "python/libs/requests" -> cluster "python"
# topic "lua/patterns" -> cluster "lua"
# topic "" or null -> cluster "uncategorized"

Maintenance Candidate Classification

Based on composite scores, classify each memory:

Additionally, flag specific conditions:

• duplicate > 0.6 -> Merge candidate regardless of composite
• size_penalty > 0.5 -> Compress candidate regardless of composite
• zero_retrieval == 1.0 -> Review for relevance

Health Report Template

The report sub-mode generates a formatted health report displayed to the user. Template:

## Memory Vault Health Report

**Generated**: {today}
**Vault**: .memory/

---

### Overview

| Metric | Value |
|--------|-------|
| Total memories | {total_count} |
| Total tokens | {total_tokens} |
| Average tokens/memory | {avg_tokens} |
| Oldest memory | {oldest_date} ({oldest_id}) |
| Newest memory | {newest_date} ({newest_id}) |

---

### Category Distribution

| Category | Count | Tokens | Avg Score |
|----------|-------|--------|-----------|
| {category_1} | {count} | {tokens} | {avg_composite} |
| {category_2} | {count} | {tokens} | {avg_composite} |
| ... | ... | ... | ... |

---

### Topic Clusters

| Cluster | Memories | Avg Staleness | Avg Duplicate |
|---------|----------|---------------|---------------|
| {cluster_1} | {count} | {avg_staleness} | {avg_duplicate} |
| {cluster_2} | {count} | {avg_staleness} | {avg_duplicate} |
| ... | ... | ... | ... |

---

### Retrieval Statistics

| Metric | Value |
|--------|-------|
| Never retrieved | {never_retrieved_count} ({never_retrieved_pct}%) |
| Retrieved 1-3 times | {low_retrieval_count} |
| Retrieved 4+ times | {high_retrieval_count} |
| Most retrieved | {most_retrieved_id} ({most_retrieved_count} times) |

---

### Maintenance Candidates

#### Purge Candidates (score >= 0.7)
{purge_list or "None"}

#### Merge Candidates (duplicate > 0.6)
{merge_list or "None"}

#### Compress Candidates (size > 0.5)
{compress_list or "None"}

#### Review Candidates (score 0.3-0.7)
{review_list or "None"}

---

### Health Score

**Score**: {health_score}/100
**Status**: {status_emoji} {status_label}

Formula: `100 - (purge_count * 3) - (merge_count * 5) - (compress_count * 2)`

| Threshold | Status |
|-----------|--------|
| 80-100 | Healthy |
| 60-79 | Manageable |
| 40-59 | Concerning |
| 0-39 | Critical |

---

### Recommended Actions

{action_list based on candidates found}

Health Score Formula

health_score = 100 - (purge_count * 3) - (merge_count * 5) - (compress_count * 2)
health_score = clamp(health_score, 0, 100)

Where:

• purge_count = number of memories with composite score >= 0.7
• merge_count = number of memories with duplicate score > 0.6
• compress_count = number of memories with size_penalty > 0.5

Health Status Thresholds

These thresholds mirror repository_health.status vocabulary in state.json.

Sub-Mode: merge

Combine duplicate memories with high keyword overlap. The merge operation identifies pairwise duplicate candidates within topic clusters, presents them for interactive selection, merges content with a keyword superset guarantee, tombstones the absorbed secondary, updates cross-references, and regenerates indexes.

Edge Case Checks

Before candidate identification, validate:

1. Run validate-on-read to ensure memory-index.json is consistent
2. Count non-tombstoned memories (status != "tombstoned" or status absent)
3. If fewer than 2 non-tombstoned memories:
   Display: "Merge requires at least 2 active memories. Vault has {count}."
   Return early.

Pairwise Keyword Overlap Algorithm

Compute pairwise overlap within each topic cluster:

1. Group non-tombstoned memories by topic cluster:
   cluster_key = topic.split("/")[0]
   If topic is empty or null: cluster_key = "uncategorized"

2. For each cluster with 2+ memories, compute pairwise overlap:
   for each pair (A, B) in cluster:
     overlap_ab = |A.keywords intersect B.keywords| / |A.keywords|
     overlap_ba = |A.keywords intersect B.keywords| / |B.keywords|
     pair_overlap = max(overlap_ab, overlap_ba)

   Note: Use max of both asymmetric directions so that a small memory
   with all keywords contained in a larger memory is detected.

3. Handle empty keyword arrays:
   If either A.keywords or B.keywords is empty: pair_overlap = 0.0
   (Cannot merge memories with no keyword basis for comparison)

4. Filter pairs where pair_overlap >= 0.6 (60% threshold)

5. Sort candidate pairs by pair_overlap descending within each cluster

Dry-Run Mode

When --dry-run is set, compute and display candidates without writing any files:

Display per cluster:
  ## Merge Candidates (Dry Run)

  ### Cluster: {cluster_key}

  | Primary | Secondary | Overlap | Shared Keywords |
  |---------|-----------|---------|-----------------|
  | {A.id} | {B.id} | {pair_overlap}% | {shared_keywords} |

  {total_pairs} merge candidate pair(s) found across {cluster_count} cluster(s).
  Run /distill --merge without --dry-run to execute.

Return early after display. No files are modified.

Interactive Selection (AskUserQuestion)

Present merge candidates per topic cluster for user selection:

For each cluster with candidates:
  AskUserQuestion({
    "question": "Select pairs to merge in cluster '{cluster_key}':",
    "header": "Merge Candidates: {cluster_key}",
    "multiSelect": true,
    "options": [
      {
        "label": "{A.title} + {B.title}",
        "description": "{pair_overlap}% overlap | Shared: {shared_keywords} | Retrievals: {A.retrieval_count}, {B.retrieval_count}"
      }
    ]
  })

If no pairs above threshold in any cluster:

Display: "No merge candidates found (no pairs with >= 60% keyword overlap)."
Return early.

If user selects no pairs across all clusters:

Display: "No pairs selected. No merges performed."
Return early.

Primary Determination

For each selected pair, determine which memory is primary (target) and which is secondary (absorbed):

Primary selection rules (first match wins):
1. Higher retrieval_count -> primary
2. If retrieval_count equal: older created date -> primary
3. If both equal: alphabetically first id -> primary (deterministic tiebreaker)

Merged Content Template

The primary memory file is rewritten with merged content:

Frontmatter merging rules:

title:            primary.title (unchanged)
created:          min(primary.created, secondary.created) -- earliest
modified:         today (ISO date)
tags:             union(primary.tags, secondary.tags) -- deduplicated
topic:            primary.topic (unchanged)
source:           primary.source (unchanged)
keywords:         union(primary.keywords, secondary.keywords) -- deduplicated, sorted
summary:          primary.summary (unchanged)
retrieval_count:  primary.retrieval_count + secondary.retrieval_count
last_retrieved:   max(primary.last_retrieved, secondary.last_retrieved) -- most recent, skip nulls
token_count:      recomputed after merge (word_count * 1.3, rounded down)
status:           omit (active is default when absent)

Content structure:

---
{merged frontmatter}
---

# {primary.title}

{primary existing content - everything between title heading and first ## section}

## Merged From {secondary.id}

**Original Title**: {secondary.title}
**Merged**: {today}
**Overlap Score**: {pair_overlap}%

{secondary content - everything between title heading and ## Connections in secondary}

## Connections
{union of both connection sections, with [[{secondary.id}]] references replaced by [[{primary.id}]]}

Keyword Superset Guarantee

CRITICAL INVARIANT: Before writing the merged file, verify:

required_keywords = union(primary.keywords, secondary.keywords)
merged_keywords = merged_frontmatter.keywords

assertion: set(merged_keywords) >= set(required_keywords)

If assertion fails:
  Log error: "KEYWORD SUPERSET VIOLATION: missing keywords: {required - merged}"
  Abort this merge pair (do not write file)
  Preserve both original files unchanged
  Continue with remaining pairs
  Report violation in operation summary

This guarantee ensures no keyword coverage is lost during merging.

Tombstone Application

After successful merge, tombstone the secondary memory:

Add to secondary's frontmatter (preserve all existing fields):
  status: tombstoned
  tombstoned_at: {today ISO8601}
  tombstone_reason: "merged_into:{primary.id}"

Do NOT delete the file.
Do NOT remove from index (index regeneration will include tombstone status).

The tombstone fields are identical to those used by the purge sub-mode:

• status: tombstoned
• tombstoned_at: {ISO8601 date}
• tombstone_reason: "{reason}" -- for merge, reason is "merged_into:{primary_id}"

Cross-Reference Update

After tombstoning, update wiki-link references across all non-tombstoned memories:

1. Scan all .memory/10-Memories/*.md files
2. For each file that is NOT tombstoned:
   Search for [[{secondary.id}]] references
   Replace with [[{primary.id}]]
3. Log all replacements: "{file}: replaced [[{secondary.id}]] -> [[{primary.id}]]"

Index Regeneration

After ALL merges in the batch are complete (not after each individual merge):

1. Regenerate memory-index.json using "JSON Index Maintenance" procedure
   - Include tombstoned memories with status: "tombstoned"
2. Regenerate index.md using "Index Regeneration Pattern"
   - Exclude tombstoned memories from active listings
3. Regenerate .memory/10-Memories/README.md
   - Exclude tombstoned memories from the listing

Distill Log Entry

Log each merge operation to .memory/distill-log.json:

{
  "id": "distill_{timestamp}",
  "timestamp": "ISO8601",
  "type": "merge",
  "session_id": "sess_...",
  "pre_metrics": {
    "total_memories": N,
    "total_tokens": N,
    "health_score": N,
    "purge_candidates": N,
    "merge_candidates": N,
    "compress_candidates": N
  },
  "post_metrics": {
    "total_memories": N,
    "total_tokens": N,
    "health_score": N,
    "purge_candidates": N,
    "merge_candidates": N,
    "compress_candidates": N
  },
  "affected_memories": [
    {
      "primary": "{primary.id}",
      "secondary": "{secondary.id}",
      "overlap_score": 0.75,
      "keywords_before": [5, 4],
      "keywords_after": 7,
      "keyword_superset_verified": true,
      "action": "merged"
    }
  ],
  "notes": "Merged {N} pair(s) across {M} cluster(s)"
}

The keywords_before array contains [primary_keyword_count, secondary_keyword_count]. The keywords_after value is the merged keyword count. The keyword_superset_verified boolean confirms the superset guarantee held for this pair.

Sub-Mode: compress

Reduce oversized memories to key points while preserving essential information. The compress operation identifies memories with high size penalty, presents them interactively, generates compressed versions, preserves originals in a History section, and ensures keyword preservation.

Edge Case Checks

Before candidate identification, validate:

1. Run validate-on-read to ensure memory-index.json is consiste

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