Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/tweet-rl-tracker/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/tweet-rl-tracker)<a href="https://agentmods.dev/skills/different-ai/agent-bank/tweet-rl-tracker"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/tweet-rl-tracker/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/different-ai/agent-bank/tweet-rl-tracker"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/tweet-rl-tracker.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 | $0.00024 | $0.02872 |
| Opus 5 | $0.00012 | $0.01436 |
| Sonnet 5 | $0.00005 | $0.00574 |
| Haiku 4.5 | $0.00002 | $0.00287 |
Grade A, and why
tweet-rl-tracker 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 9d 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 — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I Do
Set up a Notion database to track tweet performance and enable a feedback loop for improving tweet quality over time. This is "poor man's reinforcement learning" - manually logging outcomes to discover what works.
NEW: Screenshot Capture - Automatically capture screenshots from tweet links (including video frames at 10 seconds) using Chrome DevTools MCP.
The RL Loop
1. WRITE -> Draft tweet with a hypothesis (hook type, topic, etc.)
2. POST -> Publish to Twitter/X
3. LOG -> Record in Notion after 24-48hrs with metrics
4. SCORE -> Mark "Worked?" based on engagement rate
5. REVIEW -> Weekly: compare winners vs losers, extract patterns
6. REPEAT -> Apply learnings to step 1
Database Schema
Core Fields (Outcomes)
| Property | Type | Purpose |
|---|---|---|
| Tweet | title | The tweet text |
| Likes | number | Primary engagement signal |
| Impressions | number | Reach/views |
| Score | formula | Likes / Impressions * 100 (engagement %) |
| Worked? | checkbox | Binary gut-check - was this a win? |
Input Features (What You Controlled)
| Property | Type | Options |
|---|---|---|
| Hook | select | Question, Bold Claim, Story, List, How-To, Contrarian, Data/Stats |
| Topic | select | (customize to your niche) |
| Posted | date | When you posted |
Optional Extensions
| Property | Type | Purpose |
|---|---|---|
| Replies | number | Conversation signal |
| Retweets | number | Amplification signal |
| Link | url | Link to original tweet |
| Notes | rich_text | Why did it work/fail? |
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.
- 9d ago First seen · 378 lines · 24 tokens per session scan A 6a219b87b3b4
tweet-rl-tracker is a skill published in the GitHub repository different-ai/agent-bank (249 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 2,872 once invoked, about $0.0001 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 2026-08-30.
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