botlearn-twitter-intel

Monitor Twitter to identify key opinion leaders, track emerging trends, analyze sentiment, detect bots, and deliver verified real-time intelligence reports.

Security Scan

The skill's instructions describe real-time Twitter/X monitoring and use of authenticated API endpoints, but the package declares no credentials, environment variables, or install requirements — this mismatch is unexplained and worth caution.

Purpose & Capability

The skill's stated purpose is live Twitter/X monitoring (search, filtered stream, full-archive) and bot detection, which normally requires authenticated access (API keys/tokens) and possibly elevated API tiers. The manifest and SKILL.md reference v2 endpoints and rate limits, yet the skill declares no required environment variables or primary credential. That is disproportionate: a Twitter-intel skill should at minimum declare a bearer token or instructions to use a platform-provided connector.

Instruction Scope

SKILL.md provides detailed runtime instructions: building queries, fetching tweets, retrieving threads (conversation_id), running bot heuristics, and synthesizing briefings. Those instructions are scoped to the stated purpose and do not ask the agent to read unrelated system files or exfiltrate secrets. However they assume the agent can execute API calls and paginate/stream results without specifying how authentication or network access are provided.

Install Mechanism

This is an instruction-only skill with no install spec and no code files to execute — low disk/write risk. Files are documentation (knowledge, strategies, tests). No remote downloads or install scripts are present.

Credentials

The skill requires access to Twitter API endpoints in its instructions (Recent Search, Filtered Stream, Full-Archive) but declares zero required env vars or credentials. Typical required variables (e.g., TWITTER_BEARER_TOKEN, TWITTER_API_KEY/SECRET, or a platform connector config) are missing. This gap is a proportionality/integrity issue: the skill either omits declaring needed sensitive credentials or expects implicit platform-level credentials (not documented).

Persistence & Privilege

The skill does not request 'always: true', does not declare system config paths, and does not modify other skills. Autonomous invocation is allowed by default, which is normal; there are no extra persistence/privilege flags in the manifest.

What to consider before installing

This skill's documentation shows how to use Twitter/X APIs (including streaming and full-archive access) and gives a detailed analyst workflow, but it does not declare how it will authenticate to Twitter. Before installing or enabling this skill: (1) ask the publisher how authentication is provided and whether the skill expects you to supply API credentials (Bearer token, API key/secret, or elevated Academic/Pro access); (2) confirm where and how the skill will make network calls and whether it will store or transmit collected tweets outside your environment; (3) if you must provide credentials, prefer using short-lived, least-privilege tokens and ensure the skill uses a secure secret store rather than embedding secrets in plaintext; (4) if your platform provides a vetted Twitter connector, verify the skill is explicitly configured to use that connector. The mismatch between required capabilities and declared credentials is the main red flag — it may be an oversight, but treat it as unresolved until clarified.

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

1. Curate and maintain watchlists of KOLs, domain experts, and emerging voices within specified topics or industries
2. Filter high-signal tweets from noise using engagement metrics, account credibility scoring, and content relevance analysis
3. Extract and classify opinions, stances, and sentiment from tweet threads, quote tweets, and reply chains
4. Detect emerging trends, narrative shifts, and coordinated amplification campaigns before they reach mainstream awareness
5. Synthesize multi-source Twitter intelligence into structured, time-stamped briefings with confidence ratings and source attribution
6. Identify bot networks, astroturfing patterns, and inauthentic engagement to separate organic signal from manufactured consensus

Constraints

1. Never treat high engagement (likes, retweets) as a proxy for credibility — always verify the source account's authenticity and authority
2. Never report on a trend based on a single tweet or a single account — require corroboration from 3+ independent sources
3. Never ignore sarcasm, irony, or satire markers — always assess tweet tone before extracting sentiment or opinion
4. Never present bot-amplified content as organic public opinion — always flag suspected inauthentic activity
5. Always include temporal context (timestamps, trend velocity) — Twitter intelligence is time-sensitive by nature
6. 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:

1. Identify the target topic, industry, or set of accounts to monitor
2. Execute source curation and signal filtering following strategies/main.md
3. Apply knowledge/domain.md for API usage, metric interpretation, and KOL identification
4. Evaluate findings using knowledge/best-practices.md for credibility and trend validation
5. Check against knowledge/anti-patterns.md to avoid engagement blindness, sarcasm misreads, and bot amplification traps
6. Output a structured intelligence briefing with confidence levels, source attribution, and temporal context