sjtu-sai-agents/datamaster/memory-tools

Claude Code and agent skill by sjtu-sai-agents/datamaster

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Tree-structured memory management for node-based exploration. Manages global memory, data link records, and per-node manifest (TL;DR + recordings). Use when agents need to persist, share, or retrieve exploration knowledge.

Install

npx vskill@latest install sjtu-sai-agents/datamaster/memory-tools

SKILL.md

Memory Tools

Tree-structured memory system for managing exploration history across nodes. Three memory types:

  1. Global Memory (global_memory.md) - Cross-node knowledge and experience
  2. Data Link (data_link.json) - External dataset records and comments
  3. Memory Tree (manifest.md per node) - Per-node exploration history (TL;DR + recordings)

File Structure

memory_tree/
├── global_memory.md
├── data_link.json
└── {node_id}/
├── manifest.md # TL;DR + recordings (written via tools)
└── storage/
├── trajectory.json # Conversation history (auto-saved)
├── code.py # Code backup
├── stdout.txt # Execution output
└── submission.csv # Submission backup

Manifest Format

markdown
# Manifest
## TL;DR
{Overall summary of this node's exploration}
## Recordings
1.Recording 1: {Brief title}
{Detailed content}
2.Recording 2: {Brief title}
{Detailed content}

CLI Usage

All commands require -w /path/to/workspace. Node-specific commands also require -n NODE_ID.

Update Own Manifest
ToolCLI
update_current_summarypython scripts/memory_cli.py -w WORKSPACE update-summary -n NODE_ID --summary "..."
append_current_recordingspython scripts/memory_cli.py -w WORKSPACE append-recording -n NODE_ID --summary "title" --content "detail"
modify_current_recordingspython scripts/memory_cli.py -w WORKSPACE modify-recording -n NODE_ID --rid 1 --summary "new" --content "new"
delete_current_recordingspython scripts/memory_cli.py -w WORKSPACE delete-recording -n NODE_ID --rid 1
Read Other Nodes' Memory
ToolCLI
get_current_treepython scripts/memory_cli.py -w WORKSPACE tree
get_all_manifestpython scripts/memory_cli.py -w WORKSPACE all-manifest
get_parent_manifestpython scripts/memory_cli.py -w WORKSPACE parent-manifest -n NODE_ID
get_manifest_summarypython scripts/memory_cli.py -w WORKSPACE manifest-summary -n NODE_ID
get_manifest_allpython scripts/memory_cli.py -w WORKSPACE manifest-all -n NODE_ID
list_childrenpython scripts/memory_cli.py -w WORKSPACE list-children -n NODE_ID
Access Storage Files
ToolCLI
get_node_codepython scripts/memory_cli.py -w WORKSPACE node-code -n NODE_ID
get_node_outputpython scripts/memory_cli.py -w WORKSPACE node-output -n NODE_ID
get_node_trajectorypython scripts/memory_cli.py -w WORKSPACE node-trajectory -n NODE_ID
Global Memory
ToolCLI
read_global_memorypython scripts/memory_cli.py -w WORKSPACE read-global
add_global_memorypython scripts/memory_cli.py -w WORKSPACE add-global --summary "title" --content "detail"
Data Link
ToolCLI
show_all_datapython scripts/memory_cli.py -w WORKSPACE show-all-data
show_detailed_datapython scripts/memory_cli.py -w WORKSPACE show-data --dataset-id 1
add_new_datapython scripts/memory_cli.py -w WORKSPACE add-data --path /abs/path --desc "description"
add_data_recordpython scripts/memory_cli.py -w WORKSPACE add-data-record --dataset-id 1 -n NODE_ID --comment "..."

Usage Guidelines

  1. Before exploration: Read global memory (read-global) and parent manifest (parent-manifest)
  2. During exploration: Append recordings as findings emerge (append-recording)
  3. On important findings: Update global memory (add-global)
  4. On node completion: Update TL;DR summary (update-summary)
  5. For external data: RED nodes create data link entries (add-data), BLACK nodes add score feedback (add-data-record)

Scripts Structure

scripts/
├── memory_tree.py # Core implementation (from playground/search_dataset_tools/)
├── memory_tree_interface.py # MCP interface (from playground/search_dataset_tools/)
└── memory_cli.py # CLI wrapper

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Source

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