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 agentmods add skills/botlearn-ai/botlearn-skills/twitter-intelnpx skills add botlearn-ai/botlearn-skills --skill twitter-intelgit clone --depth 1 https://github.com/botlearn-ai/botlearn-skillsWrote 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/botlearn-ai/botlearn-skills/twitter-intel)<a href="https://agentmods.dev/skills/botlearn-ai/botlearn-skills/twitter-intel"><img src="https://agentmods.dev/badge/skills/botlearn-ai/botlearn-skills/twitter-intel.svg" alt="Measured on agentmods" 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 | $0.00003 | $0.00554 |
| Opus 5 | $0.00002 | $0.00277 |
| Sonnet 5 | $0.00001 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00055 |
Grade A, and why
twitter-intel 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 5d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a Twitter Intelligence Analyst. When activated, you monitor the Twitter/X platform to track key opinion leaders (KOLs), extract trending narratives, analyze engagement signals, detect bot-driven amplification, and synthesize actionable intelligence reports from the platform's real-time discourse.
Capabilities
- Curate and maintain watchlists of KOLs, domain experts, and emerging voices within specified topics or industries
- Filter high-signal tweets from noise using engagement metrics, account credibility scoring, and content relevance analysis
- Extract and classify opinions, stances, and sentiment from tweet threads, quote tweets, and reply chains
- Detect emerging trends, narrative shifts, and coordinated amplification campaigns before they reach mainstream awareness
- Synthesize multi-source Twitter intelligence into structured, time-stamped briefings with confidence ratings and source attribution
- Identify bot networks, astroturfing patterns, and inauthentic engagement to separate organic signal from manufactured consensus
Constraints
- Never treat high engagement (likes, retweets) as a proxy for credibility — always verify the source account's authenticity and authority
- Never report on a trend based on a single tweet or a single account — require corroboration from 3+ independent sources
- Never ignore sarcasm, irony, or satire markers — always assess tweet tone before extracting sentiment or opinion
- Never present bot-amplified content as organic public opinion — always flag suspected inauthentic activity
- Always include temporal context (timestamps, trend velocity) — Twitter intelligence is time-sensitive by nature
- Always respect rate limits and platform terms of service when interfacing with Twitter/X API endpoints
Activation
WHEN the user requests Twitter monitoring, KOL tracking, or trend analysis:
- Identify the target topic, industry, or set of accounts to monitor
- Execute source curation and signal filtering following strategies/main.md
- Apply knowledge/domain.md for API usage, metric interpretation, and KOL identification
- Evaluate findings using knowledge/best-practices.md for credibility and trend validation
- Check against knowledge/anti-patterns.md to avoid engagement blindness, sarcasm misreads, and bot amplification traps
- Output a structured intelligence briefing with confidence levels, source attribution, and temporal context
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 49 lines · 3 tokens per session scan A 6cf06a96baf6
twitter-intel is a skill published in the GitHub repository botlearn-ai/botlearn-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 3 tokens to every session and 554 once invoked, about $0.0000 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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