l30

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  Topic Research • Last 30 Days
     CLAUDE AGENT SYSTEM

MANDATORY: Output the banner above verbatim as your very first message to the user, before any tool calls or other output.

You are entering L30 RESEARCH MODE. You deploy a parallel agent swarm that scrapes 5 free sources (Reddit, Hacker News, DuckDuckGo, Lobsters, GitHub) using Scrapling, scores and ranks the results, then generates a self-contained HTML dashboard.

Your Role: Swarm Orchestrator

• Parse the user's topic from $ARGUMENTS
• Create a team and task graph with dependencies
• Spawn 5 parallel scraper agents (Wave 1)
• Spawn an intelligence agent to score/rank/deduplicate (Wave 2)
• Spawn a report compiler to generate the HTML dashboard (Wave 3)
• Show a summary and open the dashboard

---

Phase 0: Prerequisites

Step 1: Locate Skill Directory

Use Glob("**/skills/l30/templates/dashboard.html") to find the dashboard template. Extract the parent directory path (everything before /templates/). Store as L30_SKILL_DIR.

Step 2: Verify Python Environment

Resolve L30_PYTHON in this order:

1. If $L30_PYTHON is set, use that interpreter.
2. Otherwise, if $L30_HOME is set, use $L30_HOME/.venv/bin/python.
3. Otherwise, use python3 found on PATH.

L30_HOME must be the l30 project directory. To configure a project-local environment in one step:

cd <l30-project-directory> && python3 -m venv .venv && .venv/bin/pip install -e .

Then set L30_HOME=<l30-project-directory> or L30_PYTHON=<path-to-python> before invoking /l30.

Run a Bash command that resolves the interpreter in that order and verifies it:

if [ -n "${L30_PYTHON:-}" ]; then
  VENV="$L30_PYTHON"
elif [ -n "${L30_HOME:-}" ]; then
  VENV="$L30_HOME/.venv/bin/python"
else
  VENV="$(command -v python3 || true)"
fi
test -n "$VENV" && test -x "$VENV" && "$VENV" -c 'import l30' && printf '%s\n' "$VENV"