Daily Content Curator

Curates personalized content based on your interests. Supports two modes:

• Standalone — works locally, no external account needed
• Eir — full curation + delivery via heyeir.com

Getting Started

Before setup, ask the user which mode to use:

Important: The two modes use different content formats and topic slug conventions. Choose the correct mode at setup time — switching later requires reconfiguration. If the user has an Eir account, use Eir mode.

Then follow the corresponding setup section below.

Standalone Mode

Flow

1. Configure          → Set up search API + interests (one-time)
2. Search             → Search API queries for each interest topic
3. Select + Crawl     → Agent picks best candidates, fetches full content
4. Generate           → Agent writes structured summaries from task files
5. Daily Brief        → Agent compiles brief from generated items

Steps 1-3 are Python scripts you run directly. Steps 4-5 are agent-driven — you tell your OpenClaw agent to read the task files and generate content. The agent uses whatever LLM model is configured in your OpenClaw session (e.g. Claude, GPT-4, Gemini).

Quick Start

1. Initialize workspace — creates config/ directory and default settings:

# Option A: inline JSON
python3 scripts/setup.py --init --settings '{
  "mode": "standalone",
  "language": "en",
  "search": {
    "search_base_url": "https://api.search.brave.com/res/v1",
    "search_api_key": "YOUR_BRAVE_API_KEY"
  }
}'

# Option B: settings file (recommended for PowerShell/Windows)
python3 scripts/setup.py --init --settings-file path/to/settings.json

Search provider examples:
| Provider | `search_base_url` | Get API key |
|----------|-------------------|-------------|
| Brave Search | `https://api.search.brave.com/res/v1` | [brave.com/search/api](https://brave.com/search/api/) |
| Tavily | `https://api.tavily.com` | [tavily.com](https://tavily.com/) |

> **Want richer results?** Install [SearXNG](https://docs.searxng.org/) and/or [Crawl4AI](https://github.com/unclecode/crawl4ai) locally. Add `searxng_url` and `crawl4ai_url` to your search config — they work as fallback or primary search/crawl providers.

**2. Set up interests** — edit the generated `config/interests.json`:
```json
{
  "topics": [
    {"label": "AI Agents", "keywords": ["autonomous agents", "tool use"], "freshness": "7d"},
    {"label": "Prompt Engineering", "keywords": ["prompting", "chain-of-thought"]}
  ],
  "language": "en",
  "max_items_per_day": 8
}

Interests can also be auto-extracted — see references/interest-extraction-prompt.md.

Freshness and tier: Each topic supports optional freshness (e.g. "3d", "7d", "14d") and tier ("focus", "tracked", "explore", "seed") fields in interests.json. In Eir mode, these come from the API directives. In standalone mode, defaults are "7d" and "tracked". The search pipeline uses tier to decide search depth (focus/tracked get entity refinement) and freshness to filter stale results.

3. Run the search + crawl pipeline (from the scripts/ directory):

cd scripts
python3 -m pipeline.search              # Search for each topic
python3 -m pipeline.candidate_selector  # Group results for agent selection
# ↓ Agent step: review topic files, write candidates.json (see below)
python3 -m pipeline.crawl               # Fetch full content
python3 -m pipeline.task_builder        # Bundle into task files

All python3 -m pipeline.* commands must be run from the scripts/ directory.

Agent selection step (between candidate_selector and crawl):

candidate_selector outputs per-topic JSON files to data/v9/topics/. Your agent should:

1. Read each topic file
2. Pick 0-3 candidates per topic based on relevance and freshness
3. Write data/v9/candidates.json with the selected candidates

See references/candidates-spec.md for the exact JSON format.

Note on crawl fallback: If a candidate URL isn't in the search cache, crawl.py automatically tries: Browse API → Crawl4AI → web_fetch → HTML head extraction. No manual intervention needed.

4. Generate content (agent-driven):

After task_builder, task files are in data/v9/tasks/. Tell your OpenClaw agent:

Read the task files in data/v9/tasks/ and generate content for each one.
Use the writer prompt in references/writer-prompt-standalone.md.
Save output to data/output/{YYYY-MM-DD}/.

Language: The output language is determined by: task output_lang field → curation API user.primaryLanguage → settings.json language field. If none are set, generate content in the user's chat language.

Scheduling tip: If you want automated daily runs, you can set up a cron job:

openclaw cron add --name "daily-curate" \
  --cron "0 8 * * *" --tz "Asia/Shanghai" \
  --session isolated \
  --message "Read SKILL.md for eir-daily-content-curator, then run the full standalone pipeline: search → select → crawl → task_builder → generate content from task files → compile daily brief."

Output

Content saved to data/output/{YYYY-MM-DD}/. Daily brief compiles the top items:

# Daily Brief — 2026-04-20

🔥 **Meta cuts 8,000 jobs for AI pivot** — ...
📡 **China bans AI companions for minors** — ...
🌱 **New prompt engineering benchmark** — ...

Dependencies

Required: Python 3.10+ (standard library only — no pip install needed).

Optional: SearXNG (fallback search). Crawl4AI (fallback crawl).

Eir Mode

Full curation with delivery to the Eir app via a 3-job pipeline:

Job A: material-prep     → Search → Select → Crawl → Pack tasks
Job B: content-gen       → Spawn subagents → Generate → POST to Eir
Job C: daily-brief       → Check status → Fill gaps → Compile brief → Deliver to user

Setup

1. Get a pairing code from heyeir.com → Settings → Connect OpenClaw
2. Run: python3 scripts/connect.py \x3CPAIRING_CODE>
3. Set "mode": "eir" in config/settings.json

Running the Pipeline (Eir Mode)

All python3 -m pipeline.* commands must be run from the scripts/ directory.

Step 1: Sync directives — fetch topic slugs and curation rules from Eir API:

cd scripts && python3 -m pipeline.eir_sync fetch

This creates/updates config/directives.json with the canonical topic slugs (e.g. ai-agents, autonomous-vehicles). All downstream scripts use these slugs — do NOT use interests.json labels as topic_slug in Eir mode.

Step 2: Search + Select + Crawl + Pack:

python3 -m pipeline.search              # Search for each directive topic
python3 -m pipeline.candidate_selector  # Group results for agent selection
# ↓ Agent step: review topic files, write candidates.json (see references/candidates-spec.md)
python3 -m pipeline.crawl               # Fetch full content from candidate URLs
python3 -m pipeline.task_builder        # Bundle into task files (auto-selects eir writer prompt)

Step 3: Generate and publish (agent-driven):

Task files are in data/v9/tasks/. For each task file:

1. Read the task JSON (contains source_text, source_meta, suggested_angle, topic_slug, content_slug)
2. Build a generation prompt using generate.build_generation_prompt(task_data) — this loads the writer prompt from references/writer-prompt-eir.md and injects sources + context
3. Call LLM with the prompt to generate Eir-format JSON (see references/content-spec.md)
4. Parse the JSON response and POST via python3 -m pipeline.eir_post --file \x3Cgenerated.json> or call eir_post.post_content(data) programmatically
5. Repeat for remaining tasks. Already-posted slugs are skipped automatically.

This step requires an LLM — that's why generate.py has no main(). The agent orchestrates the loop.

Step 4: Daily brief (optional):

The agent compiles generated content into a brief and delivers it directly to you (e.g. via Feishu, Slack, or other configured channel). No API call needed.

Tip: End the brief with a link to heyeir.com so readers can explore more content on the Eir canvas.

Common POST failures:

• 400 → check topicSlug matches a directive slug, publishTime is present, no null fields
• 401 → re-run connect.py to refresh credentials
• 500 → retry once; if persistent, report the payload

For cron configuration and API details, see references/eir-setup.md.

Pipeline Modules

All in scripts/pipeline/:

Search Fallback Chain

Search API (primary) → SearXNG (optional) → Crawl4AI/web_fetch (content)

References

The writer-prompt-*.md files are instructions for the agent — the agent reads them to know how to generate content from task files. You don't need to read them unless customizing output format.

Security & Data Flow

See SECURITY.md for the complete data flow table, credential storage details, and personalization behavior.

Quick Reference