/memory-recall
Search and retrieve specific memories from the semantic tree based on topics, patterns, or timeframes.
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/memory-recall
Context preview
What this command does when you run it.
Search and retrieve specific memories from the semantic tree based on topics, patterns, or timeframes.
Command definition
memory-recall.mdtools:
- read
- grep
- bash
arguments: $QUERY
Memory Recall
Search and retrieve specific memories from the semantic tree based on topics, patterns, or timeframes.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Parse Recall Query**
- Extract search terms from "$QUERY"
- Identify query type:
- Topic search: Keywords/concepts
- Pattern search: Logic patterns
- Temporal search: Time ranges
- Module search: BBMC/BBPF/BBCR/BBAM
- Metric search: ΔS ranges
2. **Search Semantic Tree**
- Load active tree and indices
- Apply search filters:
- Text matching in topics/insights
- ΔS value ranges
- λ_observe patterns
- Module attribution
- Time windows
- Rank results by relevance:
- Exact matches first
- Semantic similarity
- Recency weighting
- Importance scores
3. **Reconstruct Context**
- For each recalled node:
- Retrieve full node data
- Load surrounding context
- Find related nodes
- Rebuild reasoning chain
- Calculate recall metrics:
- Relevance score
- Context completeness
- Confidence level
4. **Synthesize Memories**
- Group related recalls
- Identify patterns
- Extract key insights
- Build narrative summary
- Highlight connections
5. **Generate Recall Report**
- Matching nodes list
- Context reconstruction
- Insight synthesis
- Pattern analysis
- Navigation suggestions
Output Format
MEMORY RECALL RESULTS
═══════════════════════════════════════
Query: "$QUERY"
Matches Found: [count]
Time Range: [earliest] to [latest]
Top Recalled Memories:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. [HIGH RELEVANCE] Node [id]
Timestamp: [ISO_8601]
Topic: "[topic matching query]"
Module: [BBMC/BBPF/BBCR/BBAM]
ΔS: [value] | λ: [direction]
Insight: "[Full insight text with **highlighted** matches]"
Context: [Previous topic] → THIS → [Next topic]
Relevance: 95%
2. [MEDIUM RELEVANCE] Node [id]
Timestamp: [ISO_8601]
Topic: "[related topic]"
Module: [module]
ΔS: [value] | λ: [direction]
Insight: "[Insight with **partial** match]"
Context: [Previous] → THIS → [Next]
Relevance: 72%
3. [PATTERN MATCH] Node [id]
Timestamp: [ISO_8601]
Topic: "[topic with similar pattern]"
Module: [module]
ΔS: [value] | λ: [direction]
Insight: "[Similar reasoning pattern]"
Context: [Previous] → THIS → [Next]
Relevance: 68%
Pattern Analysis:
────────────────────────────────────────
Common Patterns Found:
• [Pattern 1]: Appears in [N] memories
• [Pattern 2]: Recurring theme
• [Pattern 3]: Similar logic flow
Temporal Distribution:
[Earliest] ████████████ [Latest]
↑ ↑↑ ↑
Clusters of activity
Module Distribution:
• BBMC: [count] memories
• BBPF: [count] memories
• BBCR: [count] memories
• BBAM: [count] memories
Synthesized Insights:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Key Finding 1:
[Synthesis across multiple memories]
Key Finding 2:
[Common thread identified]
Key Finding 3:
[Evolution of understanding]
Related Topics Discovered:
• [Related topic 1] - [N] connections
• [Related topic 2] - [N] connections
• [Related topic 3] - [N] connections
Memory Clusters:
┌─────────────────────────────────────┐
│ Cluster │ Nodes │ Theme │
├─────────────────────────────────────┤
│ Cluster A │ 5 │ [Theme] │
│ Cluster B │ 3 │ [Theme] │
│ Cluster C │ 2 │ [Theme] │
└─────────────────────────────────────┘
Navigation Suggestions:
• Review full context: /semantic:tree-view --focus "[node_id]"
• Explore cluster: /memory:recall "[cluster_theme]"
• Build on insight: /semantic:node-build "[continuation]"
• Find bridges: /boundary:safe-bridge "[related_topic]"
Recall Confidence: [percentage]%
Coverage: [percentage]% of relevant memoriesRecall Modes
Topic Recall
/memory:recall "machine learning"
Pattern Recall
/memory:recall --pattern "divergent-convergent"
Temporal Recall
/memory:recall --from "2024-01-01" --to "2024-01-31"
Module Recall
/memory:recall --module BBPF
Metric Recall
/memory:recall --deltaS 0.7 1.0
Advanced Search
Combined Filters
/memory:recall "API" --module BBMC --deltaS 0.5 0.8
Fuzzy Matching
/memory:recall "approximate~" --fuzzy 0.8
Regex Patterns
/memory:recall --regex "auth.*token"
Semantic Search
/memory:recall --semantic "similar to authentication"
Memory Reconstruction
The system reconstructs: 1. **Direct Memories**: Exact matches 2. **Associated Memories**: Related nodes 3. **Contextual Memories**: Surrounding nodes 4. **Pattern Memories**: Similar structures 5. **Bridge Memories**: Connecting concepts
Integration
Memory recall supports:
- `/semantic:tree-view` for context
- `/semantic:node-build` to continue
- `/reasoning:chain-validate` for logic
- `/boundary:safe-bridge` for connections
Read more
tools: - read - grep - bash arguments: $QUERY
Memory Recall
Search and retrieve specific memories from the semantic tree based on topics, patterns, or timeframes.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Parse Recall Query**
- Extract search terms from "$QUERY"
- Identify query type:
- Topic search: Keywords/concepts
- Pattern search: Logic patterns
- Temporal search: Time ranges
- Module search: BBMC/BBPF/BBCR/BBAM
- Metric search: ΔS ranges
2. **Search Semantic Tree**
- Load active tree and indices
- Apply search filters:
- Text matching in topics/insights
- ΔS value ranges
- λ_observe patterns
- Module attribution
- Time windows
- Rank results by relevance:
- Exact matches first
- Semantic similarity
- Recency weighting
- Importance scores
3. **Reconstruct Context**
- For each recalled node:
- Retrieve full node data
- Load surrounding context
- Find related nodes
- Rebuild reasoning chain
- Calculate recall metrics:
- Relevance score
- Context completeness
- Confidence level
4. **Synthesize Memories**
- Group related recalls
- Identify patterns
- Extract key insights
- Build narrative summary
- Highlight connections
5. **Generate Recall Report**
- Matching nodes list
- Context reconstruction
- Insight synthesis
- Pattern analysis
- Navigation suggestions
Output Format
MEMORY RECALL RESULTS
═══════════════════════════════════════
Query: "$QUERY"
Matches Found: [count]
Time Range: [earliest] to [latest]
Top Recalled Memories:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. [HIGH RELEVANCE] Node [id]
Timestamp: [ISO_8601]
Topic: "[topic matching query]"
Module: [BBMC/BBPF/BBCR/BBAM]
ΔS: [value] | λ: [direction]
Insight: "[Full insight text with **highlighted** matches]"
Context: [Previous topic] → THIS → [Next topic]
Relevance: 95%
2. [MEDIUM RELEVANCE] Node [id]
Timestamp: [ISO_8601]
Topic: "[related topic]"
Module: [module]
ΔS: [value] | λ: [direction]
Insight: "[Insight with **partial** match]"
Context: [Previous] → THIS → [Next]
Relevance: 72%
3. [PATTERN MATCH] Node [id]
Timestamp: [ISO_8601]
Topic: "[topic with similar pattern]"
Module: [module]
ΔS: [value] | λ: [direction]
Insight: "[Similar reasoning pattern]"
Context: [Previous] → THIS → [Next]
Relevance: 68%
Pattern Analysis:
────────────────────────────────────────
Common Patterns Found:
• [Pattern 1]: Appears in [N] memories
• [Pattern 2]: Recurring theme
• [Pattern 3]: Similar logic flow
Temporal Distribution:
[Earliest] ████████████ [Latest]
↑ ↑↑ ↑
Clusters of activity
Module Distribution:
• BBMC: [count] memories
• BBPF: [count] memories
• BBCR: [count] memories
• BBAM: [count] memories
Synthesized Insights:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Key Finding 1:
[Synthesis across multiple memories]
Key Finding 2:
[Common thread identified]
Key Finding 3:
[Evolution of understanding]
Related Topics Discovered:
• [Related topic 1] - [N] connections
• [Related topic 2] - [N] connections
• [Related topic 3] - [N] connections
Memory Clusters:
┌─────────────────────────────────────┐
│ Cluster │ Nodes │ Theme │
├─────────────────────────────────────┤
│ Cluster A │ 5 │ [Theme] │
│ Cluster B │ 3 │ [Theme] │
│ Cluster C │ 2 │ [Theme] │
└─────────────────────────────────────┘
Navigation Suggestions:
• Review full context: /semantic:tree-view --focus "[node_id]"
• Explore cluster: /memory:recall "[cluster_theme]"
• Build on insight: /semantic:node-build "[continuation]"
• Find bridges: /boundary:safe-bridge "[related_topic]"
Recall Confidence: [percentage]%
Coverage: [percentage]% of relevant memoriesRecall Modes
Topic Recall
/memory:recall "machine learning"
Pattern Recall
/memory:recall --pattern "divergent-convergent"
Temporal Recall
/memory:recall --from "2024-01-01" --to "2024-01-31"
Module Recall
/memory:recall --module BBPF
Metric Recall
/memory:recall --deltaS 0.7 1.0
Advanced Search
Combined Filters
/memory:recall "API" --module BBMC --deltaS 0.5 0.8
Fuzzy Matching
/memory:recall "approximate~" --fuzzy 0.8
Regex Patterns
/memory:recall --regex "auth.*token"
Semantic Search
/memory:recall --semantic "similar to authentication"
Memory Reconstruction
The system reconstructs: 1. **Direct Memories**: Exact matches 2. **Associated Memories**: Related nodes 3. **Contextual Memories**: Surrounding nodes 4. **Pattern Memories**: Similar structures 5. **Bridge Memories**: Connecting concepts
Integration
Memory recall supports:
- `/semantic:tree-view` for context
- `/semantic:node-build` to continue
- `/reasoning:chain-validate` for logic
- `/boundary:safe-bridge` for connections
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Other commands on claude-command-suite.
- /boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Open command - /boundary-detect
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Open command - /boundary-heatmap
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Open command - /boundary-risk-assess
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Open command - /boundary-safe-bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Open command - /optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.
Open command

