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OpenClaw

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.

brianhearn0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install brianhearn/expertpack-export

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

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...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/brianhearn/expertpack-export
Version
—
Commit
10a734764631044eda889d4e4d79615800e2fe94
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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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