Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
ai-twitter-digest
v1.0.1Monitor a curated list of AI/tech Twitter accounts, summarize the day's key posts using an LLM, and deliver a formatted digest to a Discord channel. Use when...
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by@fredhjc
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
The code and SKILL.md match the stated purpose: it uses AISA for Twitter data, an LLM for summarization, and posts to chat channels. However the registry metadata claims no required environment variables or primary credential while the skill clearly requires AISA_API_KEY and at least one LLM API key plus DELIVERY_* variables. This metadata mismatch is misleading.
Instruction Scope
The setup instructions and setup.py go beyond a minimal wizard: they auto-detect API keys from the environment and explicitly attempt to read OpenClaw's auth.json and call the 'openclaw' CLI to list configured channels/targets. The runtime instructs writing a .env next to the scripts and reading/writing a state file in the home dir. Reading other agent/host config files (OpenClaw auth.json) and invoking local CLIs is scope creep relative to a simple Twitter->Discord digest and may expose unrelated secrets.
Install Mechanism
No external install spec is provided (instruction-only skill). Files are delivered with the skill; there is no remote download or archive extraction. This is lower installation risk, but the included scripts will run network calls and local file I/O when executed.
Credentials
Requiring AISA_API_KEY and at least one LLM key (Anthropic/OpenAI/Gemini) is proportionate to the stated function. However the setup attempts to read OpenClaw config files and environment variables to harvest keys from other local agent configuration files, which can expose unrelated credentials. The skill also writes a plaintext .env containing API keys into the skill directory by default (risk of accidental leakage if directory is backed up or committed).
Persistence & Privilege
The skill does not request 'always:true' and does not alter other skills' configs, but it will create persistent local artifacts: scripts/.env (project-local) and a state file (default ~/.ai-twitter-sent.json). It also reads OpenClaw state paths and may enumerate configured channels via the openclaw CLI. These are persistent and require user consent but are not elevated OS privileges.
What to consider before installing
Before installing or running this skill: (1) Expect to provide AISA_API_KEY and at least one LLM API key (Anthropic/OpenAI/Gemini) plus DELIVERY_CHANNEL/TARGET. The skill's registry metadata incorrectly omits these required env vars. (2) The setup wizard will attempt to read OpenClaw's auth.json and run the local 'openclaw' CLI to auto-detect keys and channels — only run setup if you trust the skill and you are comfortable it may read other agent configs. (3) The wizard writes a plaintext .env in the scripts folder with your keys; consider using a dedicated/minimally-privileged API key or manually editing a .env in a safe location instead of using the auto-writer. (4) The monitor writes a state file in your home directory to track sent tweet IDs. (5) If you want to reduce risk: run monitor.py manually after creating your own .env, avoid running the setup wizard, inspect the files yourself, and create/per-use keys scoped to this integration (revocable keys). If you are unsure, do not run the setup that auto-reads OpenClaw configs and avoid giving the skill access to high-value credentials.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.