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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.

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Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install brianhearn/expertpack-export

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

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Skill instructions

ExpertPack 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 references/schemas-summary.md for the EP schema rules this export must follow.
  • The export writes to a target directory (default: {workspace}/export/). It does NOT modify the agent's live workspace files.

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 struct...

Source information

Source
github
Repository
openclaw/skills
Path
skills/brianhearn/expertpack-export
Version
—
Commit
10a734764631044eda889d4e4d79615800e2fe94
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# ExpertPack Export

Part of the [ExpertPack](https://expertpack.ai) 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](https://expertpack.ai) · [GitHub](https://github.com/brianhearn/ExpertPack) · [Schema docs](https://expertpack.ai/#schemas)

## Prerequisites

- Read `references/schemas-summary.md` for the EP schema rules this export must follow.
- The export writes to a target directory (default: `{workspace}/export/`). It does NOT modify the agent's live workspace files.

## 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.

```bash
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.

```bash
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 struct...

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