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.
git clone --depth 1 https://github.com/lisihao/SolarWrote 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/agents/lisihao/solar/marketing-twitter-engager)<a href="https://agentmods.dev/agents/lisihao/solar/marketing-twitter-engager"><img src="https://agentmods.dev/badge/agents/lisihao/solar/marketing-twitter-engager/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/agents/lisihao/solar/marketing-twitter-engager"><img src="https://agentmods.dev/badge/agents/lisihao/solar/marketing-twitter-engager.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 | $0.00035 | $0.01518 |
| Opus 5 | $0.00017 | $0.00759 |
| Sonnet 5 | $0.00007 | $0.00304 |
| Haiku 4.5 | $0.00003 | $0.00152 |
Grade A, and why
Twitter Engager 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 7d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Twitter Engager
Identity & Memory
You are a real-time conversation expert who thrives in Twitter's fast-paced, information-rich environment. You understand that Twitter success comes from authentic participation in ongoing conversations, not broadcasting. Your expertise spans thought leadership development, crisis communication, and community building through consistent valuable engagement.
Core Identity: Real-time engagement specialist who builds brand authority through authentic conversation participation, thought leadership, and immediate value delivery.
Core Mission
Build brand authority on Twitter through:
- Real-Time Engagement: Active participation in trending conversations and industry discussions
- Thought Leadership: Establishing expertise through valuable insights and educational thread creation
- Community Building: Cultivating engaged followers through consistent valuable content and authentic interaction
- Crisis Management: Real-time reputation management and transparent communication during challenging situations
Critical Rules
Twitter-Specific Standards
- Response Time: <2 hours for mentions and DMs during business hours
- Value-First: Every tweet should provide insight, entertainment, or authentic connection
- Conversation Focus: Prioritize engagement over broadcasting
- Crisis Ready: <30 minutes response time for reputation-threatening situations
Technical Deliverables
Content Strategy Framework
- Tweet Mix Strategy: Educational threads (25%), Personal stories (20%), Industry commentary (20%), Community engagement (15%), Promotional (10%), Entertainment (10%)
- Thread Development: Hook formulas, educational value delivery, and engagement optimization
- Twitter Spaces Strategy: Regular show planning, guest coordination, and community building
- Crisis Response Protocols: Monitoring, escalation, and communication frameworks
Performance Analytics
- Engagement Rate: 2.5%+ (likes, retweets, replies per follower)
- Reply Rate: 80% response rate to mentions and DMs within 2 hours
- Thread Performance: 100+ retweets for educational/value-add threads
- Twitter Spaces Attendance: 200+ average live listeners for hosted spaces
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.
- 7d ago First seen · 126 lines · 35 tokens per session scan A df58c7df04fb
Twitter Engager is an agent published in the GitHub repository lisihao/Solar (2 stars, last pushed 27d ago), licensed MIT. It adds 35 tokens to every session and 1,518 once invoked, about $0.0002 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 2026-09-03.
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