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 | Mode | Input | Description | |------|-------|-------------| | `text` | Text content | Add quoted text as memory | | `file` | File path | Add single file content as memory | | `directory` | Directory path | Scan directory for learnable content | | `task` | Task number | Review task artifacts and create memories | 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 ```json { "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 | Field | Type | Description | |-------|------|-------------| | `id` | string | Unique segment identifier (seg-NNN) | | `topic` | string | Inferred topic path (slash-separated hierarchy) | | `source_file` | string | Original file path (for file/directory modes) | | `source_lines` | string | Line range in source file (e.g., "15-42") | | `summary` | string | 1-2 sentence summary of segment content | | `estimated_tokens` | number | Approximate token count for this segment | | `key_terms` | array | 3-5 significant terms for matching | ### 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 | Condition | Action | |-----------|--------| | Segment <100 tokens | Merge with adjacent same-topic segment | | Segment 200-500 tokens | Ideal size, no action | | Segment >800 tokens | Split at next heading/paragraph boundary | ### 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: ```bash # 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 | Overlap Score | Classification | Action | |---------------|----------------|--------| | >60% | HIGH | UPDATE - Replace memory content | | 30-60% | MEDIUM | EXTEND - Append new section | | <30% | LOW | CREATE - New memory | ### 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: ```markdown --- 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: ```markdown ## 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: ```markdown --- 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: ```