GB-Power-Market-JJ expertpack-export
Export an OpenClaw instance's accumulated knowledge into a structured ExpertPack composite. Use when backing up an agent's identity, exporting for migration, or creating a portable knowledge snapshot. Handles auto-discovery (scanning workspace state to identify constituent packs), distillation (compressing raw state into structured EP files), and packaging (writing EP-compliant packs + composite manifest). NOT for importing/hydrating from an existing EP.
git clone https://github.com/GeorgeDoors888/GB-Power-Market-JJ
T=$(mktemp -d) && git clone --depth=1 https://github.com/GeorgeDoors888/GB-Power-Market-JJ "$T" && mkdir -p ~/.claude/skills && cp -r "$T/openclaw-skills/skills/brianhearn/expertpack-export" ~/.claude/skills/georgedoors888-gb-power-market-jj-expertpack-export && rm -rf "$T"
T=$(mktemp -d) && git clone --depth=1 https://github.com/GeorgeDoors888/GB-Power-Market-JJ "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/openclaw-skills/skills/brianhearn/expertpack-export" ~/.openclaw/skills/georgedoors888-gb-power-market-jj-expertpack-export && rm -rf "$T"
openclaw-skills/skills/brianhearn/expertpack-export/SKILL.mdExpertPack Export
Part of the ExpertPack framework — a structured, portable knowledge format for AI agents.
Export an OpenClaw instance into a composite ExpertPack — an agent pack (subtype: agent) as the voice, plus person/product/process packs as knowledge constituents.
Learn more: expertpack.ai · GitHub · Schema docs
Prerequisites
- Read
for the EP schema rules this export must follow.references/schemas-summary.md - The export writes to a target directory (default:
). It does NOT modify the agent's live workspace files.{workspace}/export/
Export Flow
1. Scan
Run
scripts/scan.py to inventory the workspace. It outputs a JSON manifest of discovered files, their categories, and proposed pack assignments.
python3 {skill_dir}/scripts/scan.py --workspace /root/.openclaw/workspace --output /tmp/ep-scan.json
Review the scan output. It proposes:
- Which files map to which pack type (agent, person, product, process)
- Which knowledge domains were detected
- Confidence scores for ambiguous classifications
2. Propose
Present the proposed composite to the user:
- List each proposed pack with type, slug, and key content sources
- Flag ambiguous classifications for user decision
- Note any gaps (e.g., "No process packs detected — skip or create stubs?")
Wait for user confirmation before proceeding.
3. Distill
Run
scripts/distill.py for each proposed pack. It reads source files, extracts knowledge, deduplicates, and writes EP-compliant output.
python3 {skill_dir}/scripts/distill.py \ --scan /tmp/ep-scan.json \ --pack agent:easybot \ --output /root/.openclaw/workspace/export/packs/easybot/
Repeat for each pack. The script:
- Reads source files listed in the scan manifest
- Extracts and classifies knowledge assertions
- Deduplicates (prefers newest for conflicts)
- Writes structured .md files with proper headers and frontmatter
- Writes manifest.yaml per pack
- Strips secrets (API keys, tokens, passwords) automatically
4. Compose
Run
scripts/compose.py to generate the composite manifest and overview.
python3 {skill_dir}/scripts/compose.py \ --scan /tmp/ep-scan.json \ --export-dir /root/.openclaw/workspace/export/
5. Validate
Run
scripts/validate.py to check the export against schema rules.
python3 {skill_dir}/scripts/validate.py --export-dir /root/.openclaw/workspace/export/
It checks:
- All required files exist per schema
- manifest.yaml fields are valid
- No secrets leaked (scans for API key patterns)
- File sizes within guidelines
- Cross-references resolve
6. Review & Ship
Present the validation report and a summary of what was exported. The user decides whether to commit/push or adjust.
Important Rules
- Never include secrets. The scan and distill scripts strip known patterns, but always review
andoperational/tools.md
manually.operational/infrastructure.md - Distill, don't copy. Raw journal entries and session states should be compressed into structured knowledge. The export should be 10-20% the volume of raw state.
- Respect privacy. Flag personal information about the user for access tier review. Default user-specific content to
access.private - Preserve provenance. Each distilled file should note its source files in frontmatter.
- Don't modify the live workspace. All output goes to the export directory.