Journalism Agent

Multi-stage journalism agent for OpenClaw. Researches, drafts, and edits publication-quality articles. Also assembles mixed newsletters combining original ar...

Security Scan

The skill's requirements, instructions, and included script are consistent with a journalism/newsletter assembly agent; nothing requested appears disproportionate to its stated purpose.

Purpose & Capability

Name/description, SKILL.md, assets, and the newsletter_assemble.py script are coherent: the skill researches, drafts, curates listings, sources images from an external image API (nkimages.com) and assembles an HTML newsletter. Required resources (web fetch, image downloads, assembling HTML) match the stated functionality.

Instruction Scope

SKILL.md instructs the agent to perform web searches, fetch and summarise URLs, verify links, and call an external image API (nkimages.com). These actions are expected for this use case. Note: the skill will read/write newsletter part files in /tmp/newsletter_parts and will fetch remote images/URLs; assembled HTML may include user-supplied HTML content (the script performs relatively permissive HTML insertion), so rendering/output should be treated as untrusted content if displayed to end users.

Install Mechanism

No install spec is provided (instruction-only with a small helper script). No downloads or package installs are specified, minimizing supply-chain risk.

Credentials

The skill declares no required environment variables or credentials, which fits its described operations. The SKILL.md uses an external public image API that claims no API key is required. The bundled Python script defines a TOKEN_FILE path pointing at /root/.openclaw/workspace/skills/design-agent/references/design-tokens.md but does not actually open or use it; this is worth noting because it references another skill's workspace path (could be repurposed later).

Persistence & Privilege

always:false and no special privileges requested. The script writes output to /tmp/newsletter_assembled.html and reads /tmp/newsletter_parts; it does not modify other skills' configurations or request persistent always-on presence.

Assessment

This skill appears to do what it says: assemble research-backed articles and newsletters and fetch images from an external stock API. Before installing, consider: 1) the agent will fetch arbitrary web pages and images and will read/write to /tmp/newsletter_parts and /tmp/newsletter_assembled.html — avoid placing sensitive files in those paths. 2) The NK Images API (nkimages.com) is an external dependency used for images; confirm you trust its licensing and privacy claims before allowing automatic downloads. 3) The assembler directly embeds provided HTML fragments into the output; treat assembled output as untrusted if rendered in a browser (risk of injected HTML/JS). 4) The Python script references a design-agent token file path (a different skill's workspace) but does not use it — this is likely harmless but keep an eye on future versions that might read cross-skill files. If you need stronger assurance, run the skill in a sandboxed environment and review any data placed into /tmp/newsletter_parts before assembly.

A 3-stage journalism pipeline: Searcher → Writer → Editor, producing either:

• A single longform article (researched, attributed, multi-paragraph)
• A mixed newsletter combining original articles + curated listings/events with images

Architecture

User topic
    │
    ▼
┌─────────────────────────────────────────────┐
│  SEARCHER  (web-search-pro)                  │
│  • Generate 3 search angles per topic       │
│  • Return top URLs per angle               │
│  • Quality gate: NYT-level sources only    │
└─────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────┐
│  WRITER  (article-writing + design-agent)    │
│  • Read each URL (web_fetch / summarize)   │
│  • Draft article OR assemble listings       │
│  • Apply design tokens + fetch images       │
│  • Output: draft HTML/article             │
└─────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────┐
│  EDITOR                                     │
│  • Fact-check claims against sources        │
│  • Enforce voice + structure rules         │
│  • Verify image credits + alt text         │
│  • VLM review loop (canvas snapshot)        │
│  • Final sign-off                          │
└─────────────────────────────────────────────┘
    │
    ▼
Final output

Workflows

Article Mode

User: "Write a 1000-word piece on the future of community libraries"

1. Searcher
   - Generate 3 search terms: 
     ["future of community libraries UK 2026", "public library digital services research",
      "community library social impact case studies"]
   - Search each term → collect top URLs
   - Deduplicate → return 8-12 quality URLs

2. Writer
   - Read top 4 URLs with web_fetch (prioritise gov.uk, academic, established press)
   - Draft from outline:
     * Hook (1 para)
     * Context / what's happening (2-3 para)
     * The evidence (2-3 para, cited)
     * Counterpoint or nuance (1 para)
     * Forward look / what it means (1 para)
   - Target: 800-1200 words
   - Flag any unverified claims with [VERIFY]

3. Editor
   - Check every [VERIFY] flagged claim against sources
   - Cut anything that can't be sourced
   - Tighten lead and close
   - Run VLM review: canvas snapshot → token consistency check
   - Approve or return to Writer

Newsletter Mode (mixed articles + listings)

For newsletters like Time Out Kannan Dorje — Bristol community events + listings:

1. Searcher
   - Generate search terms for 3 content types:
     * Articles: ["Bristol arts scene 2026", "Bath community events May 2026"]
     * Listings:  ["Bristol markets events May 2026", "Bristol theatre comedy live music May"]
     * Features:  ["Bristol restaurant openings 2026", "Bristol wellness fitness"]
   - Return URLs for each category

2. Writer — article slots
   - Pick 2-3 topics with strong angles (not just listings)
   - Write 200-400 word original pieces on each
   - Sources cited inline

3. Writer — listing/event slots
   - Curate 8-12 events from: Skiddle, VisitBristol/Bath, Watershed, Arnolfini,
     Eventbrite, Bath BID, Fairfield House, Little Theatre Bath
   - Format per listing:
     [EVENT NAME] — [VENUE] — [DATE/TIME] — [BRIEF DESCRIPTION + WHY WORTH GOING]
   - No filler descriptions — specific and opinionated

4. Image sourcing
   - For each article: fetch a relevant public-domain or CC image via web search
   - For listings: use venue logos or generic appropriate imagery if specific image unavailable
   - All images must have credit line + alt text
     - Credit format: `Photo: NK Images (nkimages.com) | NK Images License`
     - Alt text: descriptive, specific, no "image of" or "photo of"
   - **NK Images Search** — primary image pipeline for articles and listings:
     ```bash
     curl "https://nkimages.com/api/public/images?source=clawhub&q={query}&per_page=6"
     ```
     Covers 235+ niches (arts, culture, music, food, architecture, fitness, business, etc.).
     No API key required. Free commercial use. Use `viewUrl` and `downloadUrl` exactly as returned by the API.
     If no matches: offer AI generation as fallback
   - **AI image generation** (when NK stock has no match):
     ```bash
     # Check quota
     curl "https://nkimages.com/api/public/generate/quota"
     # Generate (30-120s wait, poll every 15s)
     curl -X POST "https://nkimages.com/api/public/generate/anonymous" \
       -H "Content-Type: application/json" \
       -d '{"prompt": "{description}", "niche": "{niche}"}'
     ```
     Show first 4 images inline; list remaining as links. Never fabricate URLs.

5. Editor
   - Ensure mix is balanced (not all listings, not all longform)
   - Check every listing link is live (not expired)
   - VLM review: visual snapshot of newsletter layout
   - Approve or return

Output Formats

Article

• Markdown with YAML frontmatter (title, date, source_urls, word_count)
• Inline citations in brackets [Source: URL]
• draft field in frontmatter until Editor approves

Newsletter

• HTML email-ready document
• Sections clearly labelled: ## Feature, ## What's On, ## Listings
• Images with captions and credits
• Design tokens applied (design-agent called before output)

Quality Standards

Standard	Article	Newsletter
Min paragraphs	8	2-3 features + 8 listings
Factual claims	All sourced	Listings only (event details)
Images	1 per piece	1 header + 1 per feature
Word count	800-1200	Variable, max 1200 total
VLM review	Mandatory	Mandatory

Banned Patterns

• Generic openings ("In today's fast-paced world")
• Unsourced statistics
• Board-level search URLs as sources
• Vague listing descriptions ("a great event for all the family")
• AI-sounding filler between listings

Key Files

File	Purpose
references/design-tokens.md	Base tokens (via design-agent)
references/source-quality.md	What counts as a quality source
assets/newsletter-template.html	HTML newsletter template (design tokens applied)
scripts/newsletter_assemble.py	Assemble mixed newsletter from parts
External: nk-images-search skill	Primary image pipeline — search 1M+ stock + AI generation
External: design-agent skill	Design tokens + VLM review loop