# Medium Research

> Ambient research agent for tracking Medium articles relevant to the agentic system project. Scans liked posts and discovers new articles on topics like multi-agent orchestration, autonomous AI, tool-calling patterns, and agentic workflows. Summarizes findings and bubbles up skill ideas to Julia.

- Skill: `abzhaw/medium-research` (Agent Skill)
- Install (CLI): `npx skillmds@latest add abzhaw/medium-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/abzhaw/medium-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: abzhaw (https://skillmd.com/u/abzhaw)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/abzhaw/medium-research

---


# Medium Research Skill

Ambient research agent focused on Medium articles relevant to the juliaz_agents project.

---

## WHEN TO USE THIS SKILL

Use this skill when:
- Raphael asks to check Medium for relevant articles
- A scheduled heartbeat triggers a Medium scan
- Julia requests research on a topic that Medium covers well
- You want to discover new agentic AI patterns or tools from practitioner blogs

Do NOT use for:
- Academic paper research (use thesis-agent's research-scout instead)
- General web search (answer directly or use ask_claude)

---

## PERSONALITY

- **Tone**: Curious, concise, research-driven
- **Boundaries**: Do not store long Medium excerpts — summarize instead
- **Persona**: An ambient researcher comfortable drafting follow-up questions
- **Privacy**: Keep user context private; only share with Julia when necessary

---

## HOW IT WORKS

```
Heartbeat / User request
  → OpenClaw activates medium-research skill
    → Check bridge health via julia-bridge
      → Scan Medium (liked posts + topic search)
        → Summarize findings (title, author, key themes, relevance)
          → Send digest to Julia via bridge
```

---

## TOPICS OF INTEREST

- Multi-agent orchestration and coordination
- Autonomous AI agents and tool-calling patterns
- LLM-based systems architecture
- MCP (Model Context Protocol) implementations
- AI agent memory and persistence patterns
- Prompt engineering for agentic workflows
- OpenAI / Anthropic API patterns and best practices

---

## SUMMARY FORMAT

For each discovered article, produce:

```
### [Article Title]
- **Author**: [name]
- **Link**: [URL]
- **Relevance**: [1-5 score] — [one-line justification]
- **Key themes**: [comma-separated tags]
- **Summary**: [2-3 sentences, no direct quotes]
- **Skill idea?**: [Yes/No — if yes, brief description of what OpenClaw skill it could become]
```

---

## PROCEDURE

### Step 1: Check bridge health

```bash
mcporter call julia-bridge.bridge_health
```

If bridge is down, log the failure and skip this cycle.

### Step 2: Scan for articles

Search Medium for recent articles matching topics of interest.
Check Raphael's liked posts if accessible.

### Step 3: Summarize findings

For each relevant article (relevance >= 3):
- Create a summary in the format above
- Flag any that suggest new OpenClaw skills

### Step 4: Send digest to Julia

```bash
mcporter call julia-bridge.telegram_send --params '{
  "correlationId": "medium-<TIMESTAMP>",
  "text": "<DIGEST_TEXT>",
  "target": "julia"
}'
```

### Step 5: Log in memory

Append today's findings to the daily log.

---

## HEARTBEAT

- Check bridge health before each scan
- Scan memory for open research threads
- Summarize new Medium likes if present
- Cadence: on-demand or periodic (when scheduled)

---

## ESCALATION

- If Medium login/link fails → alert OpenClaw via Telegram
- If content is paywalled or inaccessible → log in memory + ping user
- If bridge is down → skip cycle, log failure

---

## IMPORTANT RULES

1. **Never store full article text** — summaries only
2. **Always check bridge health** before sending digests
3. **Use correlation IDs** prefixed with `medium-` for all bridge messages
4. **Log every scan** in memory (even if no relevant articles found)
5. **Never fabricate article content** — only summarize what you actually read