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brianhearn

Elite To Expertpack

by Brian Hearn · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
268
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1
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5
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Install in OpenClaw
/install elite-to-expertpack
Description
Convert Elite Longterm Memory data into a structured ExpertPack. Migrates the 5-layer memory system (SESSION-STATE hot RAM, LanceDB warm store, Git-Notes col...
README (SKILL.md)

Elite Longterm Memory → ExpertPack

Converts an Elite Longterm Memory (5-layer system with 32K ClawHub downloads) into a proper structured ExpertPack.

Supported layers:

  • Hot RAM — SESSION-STATE.md (current task, context, decisions)
  • Warm Store — LanceDB vectors at ~/.openclaw/memory/lancedb/ (note: exported or skipped)
  • Cold Store — Git-Notes JSONL (decisions, learnings, preferences)
  • Curated Archive — MEMORY.md, memory/YYYY-MM-DD.md journals, memory/topics/*.md
  • Cloud — SuperMemory/Mem0 (skipped, noted in overview)

Usage

cd /root/.openclaw/workspace/ExpertPack/skills/elite-to-expertpack
python3 scripts/convert.py \
  --workspace /path/to/your/workspace \
  --output ~/expertpacks/my-agent-pack \
  [--name "My Agent's Knowledge"] \
  [--type auto|person|agent]

Flags let you override auto-detected paths for each layer.

What It Produces

A complete ExpertPack conforming to schema 2.3:

  • manifest.yaml (with context tiers, EK stub)
  • overview.md summarizing conversion (layer counts, warnings)
  • Structured directories: mind/, facts/, summaries/, operational/, relationships/, etc.
  • _index.md files, lead summaries, glossary.md (if terms found)
  • relations.yaml (if relationships detected)
  • Clean deduplication preferring curated > structured > raw sources

Secrets are automatically stripped (sk-, ghp_, tokens, passwords). Warnings emitted for any found.

Post-Conversion Steps

  1. cd ~/expertpacks/my-agent-pack
  2. Verify content files are 400–800 tokens each (Schema 2.5 — retrieval-ready by design)
  3. Measure EK ratio: python3 /path/to/expertpack/tools/eval-ek.py .
  4. Review overview.md and manifest.yaml
  5. Commit to git and publish to ClawHub

Learn more: https://expertpack.ai • ClawHub expertpack skill

See also: Elite Longterm Memory skill on ClawHub.

Usage Guidance
This skill appears coherent and runs only on your local files, but review these before using: 1) Run the script in a test environment or on a copy of your workspace first (it writes output). 2) Ensure python3 and PyYAML are installed (the script will exit with an instruction to pip install pyyaml). 3) Inspect the produced ExpertPack and overview.md to confirm secret redaction worked (the script uses regexes but regexes can miss edge cases). 4) The source is listed as unknown — if you care about provenance, verify the code yourself or obtain it from a trusted source (homepage is provided). 5) No network calls are present in the script, but if you later publish the pack (steps mention ClawHub) be mindful of any sensitive data you might be uploading. If you want higher assurance, have someone you trust review scripts/convert.py before running it on sensitive agent data.
Capability Analysis
Type: OpenClaw Skill Name: elite-to-expertpack Version: 1.0.1 The elite-to-expertpack skill is a utility designed to migrate agent memory data between different storage formats. The Python script (scripts/convert.py) performs local file operations to read session states, journals, and curated archives, and includes a proactive security feature to identify and redact sensitive API keys (OpenAI, GitHub, Slack) using regular expressions. The instructions in SKILL.md are consistent with the code's functionality, and there is no evidence of data exfiltration, unauthorized execution, or malicious intent.
Capability Assessment
✓ Purpose & Capability
The name/description promise (migrating the Elite 5-layer memory into an ExpertPack) matches what the Python script and SKILL.md do: they parse SESSION-STATE.md, MEMORY.md, daily journals, topic files, and Git-Notes JSONL and produce a structured output. There are no unrelated credential or network requirements declared.
✓ Instruction Scope
The SKILL.md instructs running the included script against a specified workspace and output path; the script only reads local files under the user's workspace (e.g., ~/.openclaw/*, memory/*.md, Git-Notes JSONL) and writes the ExpertPack to the output directory. It also performs secret-stripping by regex. The instructions do not request or read unrelated system configuration, environment variables, or external endpoints.
ℹ Install Mechanism
There is no install spec (instruction-only), which is lowest-risk. The script requires python3 (declared) and PyYAML (import yaml). PyYAML is not listed in declared requirements; the script exits with an error message instructing the user to pip install pyyaml. This is a minor operational gap but not a security red flag.
✓ Credentials
The skill requires no environment variables, credentials, or config paths beyond reading the user's local Elite memory files. The secret-stripping regexes are appropriately applied to inputs. No broad or unrelated credentials are requested.
✓ Persistence & Privilege
The skill is not marked always:true and does not attempt to modify other skills or system-wide settings. It runs on demand and writes output only to the specified output directory.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install elite-to-expertpack
  3. After installation, invoke the skill by name or use /elite-to-expertpack
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
Core 2.8: Obsidian compatibility — output packs include YAML frontmatter and can be opened as Obsidian vaults.
v2.0.0
Updated for Schema 2.7. Volatile/ directory support for time-bound EK migration.
v1.2.0
Removed chunker references. Schema 2.5 file-size verification step.
v1.1.0
Schema 2.5: Chunker step marked as legacy/optional. New packs authored to spec need no external chunker.
v1.0.0
Initial release — converts Elite Longterm Memory 5-layer system (SESSION-STATE, MEMORY.md, daily journals, Git-Notes JSONL, topic files) into structured ExpertPacks with context tiers, relations.yaml, and deduplication.
Metadata
Slug elite-to-expertpack
Version 1.0.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 5
Frequently Asked Questions

What is Elite To Expertpack?

Convert Elite Longterm Memory data into a structured ExpertPack. Migrates the 5-layer memory system (SESSION-STATE hot RAM, LanceDB warm store, Git-Notes col... It is an AI Agent Skill for Claude Code / OpenClaw, with 268 downloads so far.

How do I install Elite To Expertpack?

Run "/install elite-to-expertpack" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Elite To Expertpack free?

Yes, Elite To Expertpack is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Elite To Expertpack support?

Elite To Expertpack is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Elite To Expertpack?

It is built and maintained by Brian Hearn (@brianhearn); the current version is v1.0.1.

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