Research Logger

AI research pipeline with automatic logging. Search via Perplexity, auto-save results to SQLite with topic and project metadata, full Langfuse tracing. Never...

Security Scan

The skill largely does what it says (Perplexity searches saved to SQLite with tracing) but contains undeclared/default Langfuse credentials and a missing dependency that make its behavior unclear and could leak user research to a third-party account by default.

Purpose & Capability

Name/description match the code: the script runs a 'deep search' and persists results to a SQLite DB and attempts Langfuse tracing. However, the script imports a deep_search module that is not included in the package (no deep_search.py present), so the skill as shipped is incomplete. The declared required env var (PERPLEXITY_API_KEY) aligns with the stated Perplexity integration.

Instruction Scope

SKILL.md instructs running the included script, which will write records to ~/.openclaw/workspace/.data/sqlite/agxntsix.db (the code creates that path). More importantly, the script sets/defaults Langfuse environment variables and will attempt to send trace data to a Langfuse endpoint using embedded keys unless the user overrides them — this behavior is not prominently documented in SKILL.md and results in automatic telemetry of queries/results to a third party.

Install Mechanism

No install spec (instruction-only with a single script). This minimizes installer risk because nothing is downloaded or extracted. The script will run locally when invoked.

Credentials

The declared primary credential (PERPLEXITY_API_KEY) is appropriate. But the script also uses LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, and LANGFUSE_HOST (none are declared in requires.env) and hardcodes default key values and a host. Those hardcoded secrets mean telemetry may be sent to the skill author's Langfuse account by default — an undeclared and potentially privacy-impacting external data flow.

Persistence & Privilege

The skill is not always-on and does not declare elevated platform privileges. It writes a local SQLite DB under the user's home workspace path (expected for a logging tool) and does not modify other skills or system-wide agent settings.

What to consider before installing

This skill appears to implement the described research-logging functionality, but exercise caution before installing or running it: - Privacy/telemetry: The script contains hardcoded LANGFUSE keys and a default LANGFUSE_HOST; if you don't set your own Langfuse credentials, it will attempt to send traces (including queries/results/metadata) to the author's Langfuse account by default. Treat this as potential data exfiltration of your research queries and outputs. - Missing dependency: The script imports a deep_search module that is not bundled with the skill. Verify where deep_search comes from and inspect it before running — it will handle Perplexity calls and may perform further network activity. - Local storage: The script creates/writes ~/.openclaw/workspace/.data/sqlite/agxntsix.db. If you run it, expect your queries and results to be stored locally there; consider the security of that file and whether it may contain sensitive content. - Recommended actions before use: - Inspect or obtain the deep_search implementation the script expects; review its network calls and how it uses PERPLEXITY_API_KEY. - Remove or override the embedded LANGFUSE_* defaults (unset LANGFUSE_SECRET_KEY/LANGFUSE_PUBLIC_KEY or set them to your own account) if you do not want telemetry sent to the author's account. - Run the tool in a sandbox or isolated environment if you must test it. - If you need guarantees about privacy, ask the author to remove hardcoded secrets and to document exactly what is sent to Langfuse, or decline to install. Given the undeclared telemetry credentials and missing module, treat this skill as suspicious until you verify the missing dependency and remove or control the Langfuse defaults.

Search + auto-save pipeline. Every research query is logged to SQLite with Langfuse tracing.

When to Use

• Research that you want to save and recall later
• Building a knowledge base from repeated searches
• Reviewing past research on a topic
• Creating an audit trail of research decisions

Usage

# Search and auto-log
python3 {baseDir}/scripts/research_logger.py log quick "what is RAG"
python3 {baseDir}/scripts/research_logger.py log pro "compare vector databases" --topic "databases"

# Search past research
python3 {baseDir}/scripts/research_logger.py search "vector databases"

# View recent entries
python3 {baseDir}/scripts/research_logger.py recent --limit 5

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

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