X Knowledge Base (XKB)

讓知識重新浮現 | Make Knowledge Reappear

XKB is a personal knowledge lifecycle system. It turns local notes, bookmarks, videos, repositories, papers, and conversation memory into structured cards, searchable indexes, distilled wiki topics, and an interactive knowledge graph. The core idea is semantic active recall — knowledge should resurface when it becomes useful, not sit in an archive waiting to be manually found.

(Click to watch the concept presentation)

The Problem

Every day we consume notes, articles, videos, repositories, papers, conversations, and threads. We save them because they feel important. Six months later — we cannot find them, cannot recall them, and have no idea what we actually learned.

Most tools stop at capture: save a bookmark, store a note, tag a source. XKB starts after capture. It turns raw material into reusable knowledge, connects it to what you already know, and lets agents retrieve it at the moment of need. Knowledge should know when you need it.

XKB is built on a different premise: knowledge has a lifecycle. The goal is not to archive more — it is to make what you have already consumed reappear at the right moment and gradually sediment into durable understanding.

Start Simple: Three Operating Modes

XKB can run in layers. You do not need to install the full XBrain/GBrain stack on day one.

Mode	Best for	Requires	Retrieval
Lite	First run, local notes, small libraries	Python + an LLM provider	`search_index.json` keyword search
Enhanced	Better semantic recall without services	Lite + `GEMINI_API_KEY`	flat `vector_index.json` fallback
Full / XBrain	Daily use, larger libraries, agent workflows	OpenClaw + GBrain/XBrain	Postgres/pgvector hybrid RRF search

Recommended path:

Start with Lite mode and ingest a few local notes.
Add Gemini embeddings only if you need semantic fallback search.
Install XBrain/GBrain once the library is large enough to need hybrid search and durable jobs.

Your personal cards, indexes, graph data, and runtime state should stay outside git. This repo contains the tooling and templates, not your private knowledge base.

10-Minute Lite Quick Start

# 1. Clone
git clone https://github.com/Hidicence/x-knowledge-base
cd x-knowledge-base

# 2. Choose a workspace for your private data
export OPENCLAW_WORKSPACE="$HOME/.openclaw/workspace"
mkdir -p "$OPENCLAW_WORKSPACE/memory/cards" "$OPENCLAW_WORKSPACE/memory/bookmarks"

# 3. Configure an LLM provider
cp .env.example .env
# Edit .env or export LLM_MODEL / LLM_API_URL / LLM_API_KEY in your shell.

# 4. Ingest a local markdown folder
python3 scripts/local_ingest.py /path/to/notes --category notes

# 5. Build the fallback search index
bash scripts/build_search_index.sh

# 6. Ask or search
bash scripts/search_bookmarks.sh "agent memory"
python3 scripts/xkb_ask.py "What patterns are emerging in my notes?"

This mode does not require X/Twitter cookies, GBrain, Postgres, Bun, or OpenClaw cron.

Privacy & Public Repo Hygiene

XKB is designed so the public repo can stay clean while your knowledge base remains local.

Do not commit:

.env or real files under .secrets/
X/Twitter cookies such as BIRD_AUTH_TOKEN / BIRD_CT0
API keys such as LLM_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY
generated personal data: memory/cards/, memory/bookmarks/search_index.json, memory/bookmarks/vector_index.json, memory/x-knowledge-base/wiki/
generated demo output: $OPENCLAW_WORKSPACE/memory/x-knowledge-base/demo/graph-data.json
runtime queues, logs, caches, PM2 dumps, or machine-specific paths

Safe to commit:

source scripts
config templates and examples
docs
sample graph/schema files
.env.example / .secrets/*.example placeholders

Before publishing changes, run:

git status --short
git diff --check
python3 scripts/health_check_pipeline.py

For index hygiene specifically:

python3 scripts/prune_duplicate_index_rows.py --dry-run
python3 scripts/sync_enriched_index.py --dry-run  # if available in your version

How It Works

The Full Pipeline

Input sources
├── Local notes / markdown     →  local_ingest.py
├── YouTube playlists          →  fetch_youtube_playlist.py
├── GitHub forks/stars         →  fetch_github_repos.py
├── PubMed / academic papers   →  fetch_pubmed.py + local_ingest.py
├── Conversation memory        →  distill_memory_to_wiki.py
└── X/Twitter bookmarks        →  xkb_minion_submit.py / xkb_minion_worker.py
        │
        ▼
  scripts/_card_prompt.py   ← shared by ALL ingest scripts
  (unified 9-section card format, same prompt regardless of source)
        │
        ▼ (all LLM calls go through scripts/_llm.py)
        │
┌─────────────────────────────────────────────────────────────┐
│  Knowledge Artifacts (permanent, gitignored)                │
│                                                             │
│  memory/cards/*.md                      structured 9-section cards      │
│  memory/x-knowledge-base/wiki/topics/   distilled long-term knowledge   │
└─────────────────────────────────────────────────────────────┘
        │
        ▼ on every card write (auto)
┌─────────────────────────────────────────────────────────────┐
│  Primary Retrieval — XBrain                                 │
│  (XKB's semantic search layer, powered by GBrain)           │
│                                                             │
│  • pgvector + Postgres-backed GBrain                        │
│  • Gemini embeddings                                        │
│  • RRF hybrid search (vector + keyword)                     │
│  • Minions job runtime for durable internal pipelines       │
│  • xbrain_recall.py  ← used automatically by all scripts   │
└─────────────────────────────────────────────────────────────┘
        │  falls back to when XBrain unavailable
        ▼
┌─────────────────────────────────────────────────────────────┐
│  Fallback Retrieval                                         │
│                                                             │
│  search_index.json          keyword + summary search        │
│  vector_index.json          flat Gemini vector index        │
│  build_vector_index.py      rebuilds flat index on demand   │
└─────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────┐
  │  Wiki Layer (memory/x-knowledge-base/wiki/topics/*.md)   │
  │                                                         │
  │  sync_cards_to_wiki.py     external bookmark knowledge  │
  │  distill_memory_to_wiki.py conversation memory insights │
  │                            (daily cron, auto-staged)    │
  └─────────────────────────────────────────────────────────┘
        │
        ▼
xkb_ask.py / Active Recall Layer
Two-layer recall: wiki topics (synthesized) → cards (XBrain hybrid search)
        │
        ▼
demo/xkb-demo-ui/  ← Interactive graph explorer (Next.js)
Knowledge Graph | Chat | Evidence Panel

Every Card Uses the Same 9-Section Structure

#	Section	Purpose
1	Core Question & Conclusion	What question does this answer? What is the conclusion?
2	Claim Level	Attested / Scholarship / Inference — how reliable?
3	Key Arguments	3–5 key arguments extracted from the source
4	False Friends	Terms with specific technical meaning in this context
5	Surprises	What might surprise a knowledgeable reader?
6	Relation to Existing Knowledge	How does this relate to existing cards?
7	Bilingual Summary	ZH + EN (used for search index)
8	Value to User	Actionable directions, relevant projects
9	Source	Source URL and related links

One format, every source. A YouTube video, a GitHub repo, and a PubMed paper all produce the same card structure.

LLM Configuration

XKB uses a single unified LLM config. All scripts share the same model — no scattered environment variables.

`config/llm.json` — change this one file to switch all scripts

{
  "model": "openai-codex/gpt-5.4"
}

Available model formats (anything supported by openclaw capability model run):

Value	Provider
`openai-codex/gpt-5.4`	ChatGPT via OpenClaw OAuth
`openai-codex/gpt-5.4-mini`	ChatGPT Mini via OpenClaw OAuth
`MiniMax-M2.7`	MiniMax via API key
`MiniMax-M2.5`	MiniMax M2.5 via API key

How it works: All scripts call scripts/_llm.py, which invokes openclaw capability model run. OpenClaw handles all auth (OAuth token refresh, API keys) automatically. Scripts no longer need to manage API keys.

Embedding is separate. Semantic vector search uses Gemini (GEMINI_API_KEY) and is not affected by config/llm.json.

Standalone / non-OpenClaw setup

If you are not using OpenClaw, override the model via environment variables:

export LLM_MODEL="MiniMax-M2.5"
export LLM_API_URL="https://api.minimax.io/anthropic"
export LLM_API_KEY="your-minimax-key"

LLM_MODEL env var takes priority over config/llm.json.

Minions Pipeline

X/Twitter bookmark enrichment now runs on a Minions-native queue pipeline by default on top of Postgres-backed GBrain. This replaces the old cron-spawn scan-worker pattern for the main bookmark enrichment path.

Deployed components

scripts/xkb_minion_submit.py
- scans unenriched bookmarks
- submits one idempotent Minion job per bookmark
- intended to run from cron (for example, hourly)
scripts/xkb_minion_worker.py
- long-lived worker daemon
- claims jobs from minion_jobs
- runs LLM enrichment
- writes final cards and updates job state

Why this replaced the old cron scan-worker pattern

Old pattern:

spawned a fresh Python process every 10 minutes
produced zombie openclaw-infer subprocesses under load
was harder to observe, retry, and recover safely

Current Minions-native pattern:

one long-lived worker daemon
sequential processing by default (one job at a time)
built-in timeout
retry + exponential backoff
idempotent submissions keyed by bookmark/card id
observable via gbrain jobs list

Smoke-tested behavior

Validated in this environment:

worker successfully claims jobs
LLM inference starts correctly
jobs move through expected states
active → dead flow was observed under intentionally short timeout in test mode
production timeout can be set to 300s per job

Monitoring

# see job states
gbrain jobs list

# verify Minions health
gbrain jobs smoke

Wiki Layer

The wiki is the distilled output layer — a readable, long-term knowledge base built from two sources:

Source	Script	What it adds
External bookmarks	`sync_cards_to_wiki.py`	Synthesized insights from cards via absorb gate
Conversation memory	`distill_memory_to_wiki.py`	Decisions, workflows, principles from daily memory logs

Single Canonical Source

The wiki lives at wiki/ inside the skill directory. The workspace symlinks to it:

~/.openclaw/workspace/wiki/  →  ~/.openclaw/workspace/memory/x-knowledge-base/wiki/  (symlink)

This prevents dual-wiki drift: every tool reads from one place.

Memory → Wiki Distillation

distill_memory_to_wiki.py reads recent memory/YYYY-MM-DD.md logs, uses LLM to extract insights worth long-term preservation, and either stages them for review or applies them to wiki topic pages.

# Preview what would be extracted from the last 3 days
python3 scripts/distill_memory_to_wiki.py --dry-run --days 3

# Stage candidates for review
python3 scripts/distill_memory_to_wiki.py --stage --days 2

# Apply all staged candidates (auto-approve)
python3 scripts/distill_memory_to_wiki.py --apply \
  --staging-file $OPENCLAW_WORKSPACE/memory/x-knowledge-base/wiki/_staging/YYYY-MM-DD-candidates.md \
  --approve-all

Cron jobs run this automatically at 15:30 and 21:30 UTC+8 daily.

Health Check

python3 scripts/health_check_pipeline.py

Checks three things:

workspace/wiki is a symlink to the canonical wiki (not a duplicate)
Recall reads from the correct wiki path
search_index.json summary coverage ≥ 70%, age < 26h; vector index freshness

Active Recall Layer

When a user sends a message, XKB uses two-layer recall:

Layer 1 — Wiki topics (memory/x-knowledge-base/wiki/topics/*.md): synthesized, durable knowledge. Answers conceptual questions.
Layer 2 — Cards (XBrain hybrid search, falls back to search_index.json): raw evidence. Provides specific citations and sources.

# Ask a question over your knowledge base
python3 scripts/xkb_ask.py "What are alternatives to RAG?"
python3 scripts/xkb_ask.py "What is the absorb gate?" --format chat
python3 scripts/xkb_ask.py "agent memory design" --json

As an MCP Tool (Claude Code / any MCP client)

Add to .claude/settings.json:

{
  "mcpServers": {
    "xkb-recall": {
      "command": "python3",
      "args": ["/path/to/x-knowledge-base/scripts/xkb_recall_server.py"],
      "env": { "OPENCLAW_WORKSPACE": "/path/to/workspace" }
    }
  }
}

vNext Direction

A draft roadmap for the next phase lives here:

docs/xkb-vnext-roadmap-draft.md

Short version:

keep wiki as the human-readable product layer
place graph/relations in the structured knowledge layer below wiki
treat Minions as the default execution substrate for large-scale internal workflows
now that bookmark enrichment is already Minions-native, focus next on knowledge governance: confidence, staleness, supersession, typed relationships

Upgrade Path: OpenClaw + XBrain

Once Lite mode works, you can enable the full stack.

With OpenClaw

# 1. Clone into your OpenClaw skills directory
git clone https://github.com/Hidicence/x-knowledge-base \
  ~/.openclaw/workspace/skills/x-knowledge-base
cd ~/.openclaw/workspace/skills/x-knowledge-base

# 2. Configure model/auth in OpenClaw
# Add env keys to ~/.openclaw/openclaw.json, for example:
# { "env": { "GEMINI_API_KEY": "...", "LLM_API_KEY": "..." } }

# 3. Optional: install XBrain/GBrain hybrid search
bash scripts/setup_xbrain.sh

# 4. Run health check
python3 scripts/health_check_pipeline.py

# 5. Run the demo
bash scripts/xkb_demo.sh

Standalone with XBrain

export LLM_API_KEY="your-minimax-or-openai-key"
export LLM_API_URL="https://api.minimax.io/anthropic/v1"
export LLM_MODEL="MiniMax-M2.7"
export OPENCLAW_WORKSPACE="$HOME/.openclaw/workspace"
export GEMINI_API_KEY="your-gemini-key"

bash scripts/setup_xbrain.sh
bash scripts/xkb_demo.sh

Manual XBrain Setup

If you prefer not to use the setup script:

# 1. Install Bun  https://bun.sh
curl -fsSL https://bun.sh/install | bash

# 2. Clone GBrain runtime
git clone https://github.com/garrytan/gbrain ~/gbrain
cd ~/gbrain && bun install && bun run src/cli.ts init

# 3. Tell XKB where to find it
# Add to ~/.openclaw/openclaw.json → "env":
#   "gbrain_dir": "/absolute/path/to/gbrain"
#   "GEMINI_API_KEY": "your-key"   ← required for embeddings

# 4. Verify
python3 scripts/xbrain_recall.py "test query"

Scripts Reference

Ingest Pipeline

All scripts share _card_prompt.py and _llm.py — one prompt, one LLM call, one card format.

Script	Source	What it does
`run_scan_worker.py`	X/Twitter	Scans bookmarks for unenriched files → cards
`run_bookmark_worker.py`	X/Twitter queue	Processes tiege-queue.json one item at a time
`fetch_youtube_playlist.py`	YouTube	Playlist subtitles → knowledge cards
`fetch_github_repos.py`	GitHub	Forks/stars → repo-level knowledge cards
`local_ingest.py`	Local / PubMed	Markdown/txt/papers → cards
`fetch_pubmed.py`	PubMed Central	Fetch open-access papers as markdown
`_card_prompt.py`	(shared)	Unified prompt, card format, summary extraction
`_llm.py`	(shared)	Unified LLM call via `openclaw capability model run`

Index & Enrichment

Script	What it does
`sync_enriched_index.py`	Backfill summaries/tags from enriched cards into search_index.json
`build_vector_index.py`	Build/update flat JSON vector index (fallback when XBrain unavailable)
`xbrain_recall.py`	XBrain search bridge — hybrid RRF (pgvector + keyword); auto-used by all recall scripts

Wiki Pipeline

Script	What it does
`sync_cards_to_wiki.py`	Cards → wiki topic pages via LLM absorb gate
`distill_memory_to_wiki.py`	Daily memory logs → wiki topic insights (stage/apply workflow)
`sync_cards_to_wiki.py --review`	Review pending absorb decisions
`lint_wiki.py`	Validate wiki structure, detect gap topics
`topic_guide_generator.py`	Generate new wiki topic stubs
`suggest_topic_map.py`	Suggest topic map updates from uncovered cards

Active Recall Layer

Script	What it does
`xkb_ask.py`	Natural-language Q&A: wiki (Layer 1) → cards via XBrain hybrid search (Layer 2)
`recall_for_conversation.py`	Conversation-triggered recall (wiki + XBrain card search)
`continuity_recall.py`	MEMORY.md + wiki lookup for session continuity
`contrarian_recall.py`	Surfaces warnings, failures, counter-examples
`action_recall.py`	Action-oriented recall (what to do next)
`xkb_recall_server.py`	MCP server exposing recall as a tool

Maintenance & Observability

Script	What it does
`health_check_pipeline.py`	Wiki symlink integrity, recall source path, index freshness
`status_knowledge_pipeline.py`	Full pipeline status in one view
`health_check.py`	Semantic conflict detection, gap analysis

Demo UI — Interactive Knowledge Graph

demo/
├── xkb-demo-ui/              Next.js app — three-column explorer
│   ├── app/page.tsx          Main layout: graph | chat | evidence
│   ├── components/
│   │   ├── KnowledgeGraph.tsx    Force-directed graph (react-force-graph-2d)
│   │   ├── ChatPanel.tsx         Natural-language Q&A via xkb_ask.py
│   │   └── EvidencePanel.tsx     Source cards + wiki references
│   └── public/
│       └── graph-data.sample.json  schema reference
└── generate_graph.py         Builds graph-data.json from search_index.json into $OPENCLAW_WORKSPACE/memory/x-knowledge-base/demo/

Run the demo:

python3 demo/generate_graph.py
cd demo/xkb-demo-ui && npm install && npm run dev
# → http://localhost:3000

graph-data.json is generated under $OPENCLAW_WORKSPACE/memory/x-knowledge-base/demo/. It is personal runtime data and should not be committed.

Step-by-Step Setup

1. Ingest content

# Local notes
python3 scripts/local_ingest.py notes/ --category learning

# X/Twitter bookmarks
python3 scripts/run_scan_worker.py --limit 20

# YouTube playlists
python3 scripts/fetch_youtube_playlist.py --playlist "URL"

# GitHub repos
python3 scripts/fetch_github_repos.py --forks --stars

# PubMed papers
python3 scripts/fetch_pubmed.py "antimicrobial resistance" --limit 20 --out /tmp/papers
python3 scripts/local_ingest.py /tmp/papers/ --category research --tag pubmed

2. Enrich the index

# Backfill summaries from enriched cards into search_index.json (always run this)
python3 scripts/sync_enriched_index.py

# Only needed if XBrain is not configured (fallback mode)
python3 scripts/build_vector_index.py --incremental

XBrain (primary): every ingest script auto-pushes cards to XBrain on write. xbrain_recall.py is used automatically by all recall scripts — no extra steps. Set gbrain_dir in ~/.openclaw/openclaw.json to point at your GBrain runtime directory.

Fallback: if XBrain is unavailable, recall falls back to search_index.json keyword search automatically.

3. Sync to wiki

# Sync external knowledge (bookmark cards → wiki topics)
python3 scripts/sync_cards_to_wiki.py --apply --limit 20

# Distill conversation memory into wiki topics
python3 scripts/distill_memory_to_wiki.py --stage --days 3
python3 scripts/distill_memory_to_wiki.py --apply \
  --staging-file $OPENCLAW_WORKSPACE/memory/x-knowledge-base/wiki/_staging/YYYY-MM-DD-candidates.md --approve-all

4. Ask

python3 scripts/xkb_ask.py "What are the alternatives to RAG?"

5. Check pipeline health

python3 scripts/health_check_pipeline.py

Expected output:

✅  wiki_canonical      workspace/wiki → memory/x-knowledge-base/wiki (symlink correct)
✅  recall_wiki_source  Recall reads from canonical wiki
✅  index_freshness     summary coverage: 212/270 (79%) | enriched: 218 | vectors: 471

Automated Pipeline (OpenClaw Cron)

When running with OpenClaw, the full pipeline runs automatically:

Schedule	Job	What it does
13:30 UTC+8	`daily:xkb-ingestion-batch`	Ingest new X/Twitter bookmarks → cards → auto-push to XBrain → sync_enriched_index
15:30 UTC+8	`daily:wiki-distill-afternoon`	Distill today's memory into wiki candidates
21:30 UTC+8	`daily:wiki-distill-evening`	Second distillation pass, apply high-confidence candidates

The pipeline ensures that after each ingestion run:

Each card is auto-pushed to XBrain on write — hybrid RRF search immediately available
sync_enriched_index.py backfills summaries into the fallback search index
New insights from conversations are automatically staged for wiki inclusion

Requirements

Python 3.10+
Node.js 18+ (demo UI only)
OpenClaw (recommended) — handles all LLM auth and cron automation
GEMINI_API_KEY — required for XBrain semantic embeddings; set in ~/.openclaw/openclaw.json
Bun + GBrain runtime (optional) — powers XBrain hybrid search (pgvector/PGLite + RRF); set gbrain_dir in openclaw.json to activate. Falls back to keyword search if not configured.

Roadmap

Version	Status	What it delivered
v0.1	✅	Bookmark ingestion, knowledge cards, keyword search
v0.2	✅	Multi-layer extraction, enrichment worker, vector index
v0.3	✅	Wiki pipeline: absorb gate, topic pages, memory distillation
v0.4	✅	Local notes ingest, ask layer, demo mode, auto topic-map
v0.5	✅	Absorb gate explainability, review-decisions log
v0.6	✅	Active Recall Layer: proactive recall, MCP server, telemetry
v0.7	✅	Claim levels, False Friends, bilingual summaries, academic PDF pipeline
v0.8	✅	Unified ingest pipeline (_card_prompt.py); demo UI (graph + chat)
v0.9	✅	Two-layer recall (wiki first); unified LLM config; memory→wiki distillation pipeline; single canonical wiki; pipeline health check
v1.0	✅	XBrain hybrid search (pgvector + RRF) fully integrated across all ingest scripts; unified path resolution; graceful fallback to keyword search
v1.1	🔜	Active Recall quality upgrade — soft-trigger re-ranking; Claim level surfaced in recall output; trigger strategy expansion beyond rule-based regex
v1.2	🔜	Agent-to-Agent knowledge exchange — standardized card format (9-section + Claim level) as exchange unit over A2A protocol; `receive_card` MCP tool; XBrain as local digestion layer for received cards

Design Principles

One card format, many sources. Every source produces the same 9-section card.
Layers, not one database. Working memory, consolidation, capture, and output are separate problems.
Quality gates over quantity. The absorb gate keeps the wiki as a distilled output layer.
Understanding over summarization. Cards answer what question this solves, not what it says.
Single source of truth. One canonical wiki path, one LLM config file — no scattered settings.
OpenClaw handles auth. Scripts call openclaw capability model run; token management is not their problem.
Graceful degradation. XBrain hybrid search is the primary retrieval path; keyword fallback activates automatically when XBrain is unavailable. Nothing breaks.
Personal data stays local. Graph data, cards, and wiki are gitignored.

Contributing

Start with SKILL.md and docs/xkb-wiki-architecture.md.

PRs and issues welcome. Your knowledge deserves to be remembered.

Name		Name	Last commit message	Last commit date
Latest commit History 91 Commits
archive/scripts-20260504		archive/scripts-20260504
assets		assets
config		config
demo		demo
docs		docs
evals		evals
references		references
scripts		scripts
tools		tools
wiki		wiki
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
README.zh.md		README.zh.md
SKILL.md		SKILL.md
chunk-worker.pm2.json		chunk-worker.pm2.json
consolidate-worker.pm2.json		consolidate-worker.pm2.json
distill-worker.pm2.json		distill-worker.pm2.json
infer-consumer.pm2.json		infer-consumer.pm2.json
xkb-worker.pm2.json		xkb-worker.pm2.json

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X Knowledge Base (XKB)

The Problem

Start Simple: Three Operating Modes

10-Minute Lite Quick Start

Privacy & Public Repo Hygiene

How It Works

The Full Pipeline

Every Card Uses the Same 9-Section Structure

LLM Configuration

config/llm.json — change this one file to switch all scripts

Standalone / non-OpenClaw setup

Minions Pipeline

Deployed components

Why this replaced the old cron scan-worker pattern

Smoke-tested behavior

Monitoring

Wiki Layer

Single Canonical Source

Memory → Wiki Distillation

Health Check

Active Recall Layer

As an MCP Tool (Claude Code / any MCP client)

vNext Direction

Upgrade Path: OpenClaw + XBrain

With OpenClaw

Standalone with XBrain

Manual XBrain Setup

Scripts Reference

Ingest Pipeline

Index & Enrichment

Wiki Pipeline

Active Recall Layer

Maintenance & Observability

Demo UI — Interactive Knowledge Graph

Step-by-Step Setup

1. Ingest content

2. Enrich the index

3. Sync to wiki

4. Ask

5. Check pipeline health

Automated Pipeline (OpenClaw Cron)

Requirements

Roadmap

Design Principles

Contributing

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Languages

`config/llm.json` — change this one file to switch all scripts

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