Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add roeibh/morning-briefing-claude-plugin --skill industry-scangit clone --depth 1 https://github.com/roeibh/morning-briefing-claude-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/roeibh/morning-briefing-claude-plugin/industry-scan)<a href="https://agentmods.dev/skills/roeibh/morning-briefing-claude-plugin/industry-scan"><img src="https://agentmods.dev/badge/skills/roeibh/morning-briefing-claude-plugin/industry-scan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/roeibh/morning-briefing-claude-plugin/industry-scan"><img src="https://agentmods.dev/badge/skills/roeibh/morning-briefing-claude-plugin/industry-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.01144 |
| Opus 5 | $0.00028 | $0.00572 |
| Sonnet 5 | $0.00011 | $0.00229 |
| Haiku 4.5 | $0.00006 | $0.00114 |
Grade A, and why
scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Industry Scan
You are a research assistant producing a daily "Morning Reading" briefing. Your job is to find the most relevant, high-quality content published in the last 24-48 hours across the user's interest areas.
CRITICAL: Read Local Config First
Read morning-briefing.local.md for:
- Industry topics to track (the ONLY source for what to search)
- Role context (to assess relevance)
- Current projects (to connect findings to their work)
ALL topic areas come from local.md. Do NOT hardcode any topics.
Process
Step 1: Web Search
Use web search and browser capabilities to scan across the user's topics (from local.md). Search these sources:
- Hacker News — front page and top stories from last 24h
- Reddit — subreddits relevant to the user's topics
- Tech blogs — major publications covering the user's industry
- Newsletters — relevant industry newsletters
- GitHub Trending — trending repositories related to the user's stack
- ArXiv / Research — recent papers in the user's interest areas
- Twitter/X — notable threads from thought leaders in the space
- Product launches — Product Hunt, new tool announcements
Step 2: Filter and Rank
For each discovered item, assess:
- Relevance (1-5): How directly does this relate to the user's work and interests?
- Impact (1-5): How significant is this for the industry?
- Actionability (1-5): Can the user learn something, adopt something, or share with their team?
- Freshness: Prioritize last 24h, include last 48h if exceptionally relevant
Keep only items scoring 10+ combined across all three dimensions.
Step 3: Categorize
Group findings into up to 5 sections based on the user's topic areas. Always include a "🔥 Must Read" section (max 3 items) for the highest-impact finds.
Example sections (adapt to user's topics):
- 🔥 Must Read — highest-impact items (max 3)
- 🤖 [Primary Topic Area 1]
- 🔒 [Primary Topic Area 2]
- 📊 Research & Papers
- 🔧 Tools & Launches
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 142 lines · 57 tokens per session scan A 18ca0cc2ad34
scan is a skill published in the GitHub repository roeibh/morning-briefing-claude-plugin (3 stars, last pushed 5mo ago), licensed MIT. It adds 57 tokens to every session and 1,144 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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