l30

name	l30
description	Research any topic from the last 30 days across 5 free sources (Reddit, HN, DDG, Lobsters, GitHub). Deploys a parallel agent swarm to scrape, score, deduplicate, and generate a rich HTML dashboard. Zero API keys required.
model	sonnet
argument-hint	<topic to research, e.g. "llm compression techniques">

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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"

• If the command succeeds: Store its path as VENV and proceed.
• If it fails: STOP. Tell the user:

  l30 Python environment is not configured or cannot import l30.
  Set L30_PYTHON to an interpreter with l30 installed, or set L30_HOME to the l30 project directory and create its environment:
  
  cd <l30-project-directory> && python3 -m venv .venv
  .venv/bin/pip install -e .
  

  Do NOT proceed.

---

Phase 1: Parse Query & Setup

Step 1: Parse Query

Extract the research topic from $ARGUMENTS.

• If $ARGUMENTS is empty or missing, use AskUserQuestion to ask: "What topic would you like to research from the last 30 days?"
• Store the topic as QUERY.

Step 2: Set Variables

QUERY_SLUG = lowercase QUERY, spaces → underscores, remove non-alphanumeric except -_, truncate to 50 chars
DATE_PREFIX = YYYYMMDD_HHMMSS (current time)
RUN_DIR = /tmp/l30-${QUERY_SLUG}-$(date +%s)
OUTPUT_DIR = ~/Documents/l30/dashboards
OUTPUT_FILE = ${OUTPUT_DIR}/${DATE_PREFIX}_${QUERY_SLUG}.html

Step 3: Create Directories

Run Bash("mkdir -p ${RUN_DIR} ${OUTPUT_DIR}").

Display: Researching: "${QUERY}" across 5 sources...

---

Phase 2: Create Team & Task Graph

Step 1: Create Team

Use TeamCreate with:

• team_name: "l30-${QUERY_SLUG}"
• description: "L30 research swarm for: ${QUERY}"

Step 2: Create All 8 Tasks

Use TaskCreate for each task. Store the returned task IDs.

#	Subject	activeForm
1	Reddit scraping for "${QUERY}"	Scraping Reddit
2	HN scraping for "${QUERY}"	Scraping Hacker News
3	DDG scraping for "${QUERY}"	Scraping DuckDuckGo
4	Lobsters scraping for "${QUERY}"	Scraping Lobsters
5	GitHub scraping for "${QUERY}"	Scraping GitHub
6	Intelligence analysis & ranking	Analyzing and ranking results
7	Dashboard compilation	Building HTML dashboard

Step 3: Set Dependencies

Use TaskUpdate with addBlockedBy:

• Task 6: addBlockedBy: [task1_id, task2_id, task3_id, task4_id, task5_id]
• Task 7: addBlockedBy: [task6_id]

Step 4: Pre-assign Wave 1 Tasks

Use TaskUpdate with owner:

• Task 1 → owner: "reddit-scraper"
• Task 2 → owner: "hn-scraper"
• Task 3 → owner: "ddg-scraper"
• Task 4 → owner: "lobsters-scraper"
• Task 5 → owner: "github-scraper"

---

Teammate Prompt Preamble

Prepend this to EVERY teammate's prompt:

You are {TEAMMATE_NAME} on team l30-{QUERY_SLUG}.

Team Protocol — follow these steps exactly:

1. Run TaskList to find your assigned task (your name appears in the owner field)
2. Run TaskGet with your task ID to confirm your assignment
3. Set your task status to in_progress via TaskUpdate
4. Complete the work described below
5. Set your task status to completed via TaskUpdate
6. Send a brief summary to the team lead via SendMessage (type: "message", recipient: "lead", content: your summary, summary: "Completed [task subject]")

If you encounter issues, message "lead" before proceeding.

Your assignment follows below.

---

Wave 1: Parallel Scraping (5 teammates)

Spawn all 5 teammates IN PARALLEL via Agent with team_name: "l30-{QUERY_SLUG}". Each uses subagent_type: "general-purpose" and model: "sonnet".

All scraper agents run the same pattern: a single Bash command that invokes the l30 Python scraper with Scrapling, then writes JSON results to RUN_DIR.

Source Agent Template

Each agent's brief follows this pattern (replace {SOURCE_MODULE}, {SOURCE_CLASS}, {SOURCE_NAME}):

Run this exact Bash command (timeout 90s):

{VENV} -c "
import asyncio, json
from l30.sources.{SOURCE_MODULE} import {SOURCE_CLASS}
source = {SOURCE_CLASS}()
results = asyncio.run(source.search(query='{QUERY}', days=30, max_results=25))
data = [r.model_dump(mode='json') for r in results]
with open('{RUN_DIR}/{SOURCE_NAME}.json', 'w') as f:
    json.dump(data, f, default=str)
print(json.dumps({'source': '{SOURCE_NAME}', 'count': len(data)}))
"

After the command completes:
- If successful: Report the result count
- If error: Report the error message, write an empty array to {RUN_DIR}/{SOURCE_NAME}.json

The 5 Agents

1. name: "reddit-scraper"

   • SOURCE_MODULE: reddit, SOURCE_CLASS: RedditSource, SOURCE_NAME: reddit
   • Brief: Preamble + "Scrape Reddit for '{QUERY}'. Uses Scrapling with Chrome impersonation for the JSON API, fetches top comments. " + Source Agent Template

2. name: "hn-scraper"

   • SOURCE_MODULE: hackernews, SOURCE_CLASS: HackerNewsSource, SOURCE_NAME: hackernews
   • Brief: Preamble + "Scrape Hacker News for '{QUERY}'. Uses Scrapling with Algolia API, fetches discussion comments. " + Source Agent Template

3. name: "ddg-scraper"

   • SOURCE_MODULE: duckduckgo, SOURCE_CLASS: DuckDuckGoSource, SOURCE_NAME: duckduckgo
   • Brief: Preamble + "Scrape DuckDuckGo for '{QUERY}'. Uses Scrapling to scrape DDG HTML search, extracts real URLs from redirect wrappers, filters junk domains. " + Source Agent Template

4. name: "lobsters-scraper"

   • SOURCE_MODULE: lobsters, SOURCE_CLASS: LobstersSource, SOURCE_NAME: lobsters
   • Brief: Preamble + "Scrape Lobsters for '{QUERY}'. Uses Scrapling for HTML parsing with CSS selectors. Lobsters is a small community — 0 results is normal for niche topics. " + Source Agent Template

5. name: "github-scraper"

   • SOURCE_MODULE: github, SOURCE_CLASS: GitHubSource, SOURCE_NAME: github
   • Brief: Preamble + "Scrape GitHub for '{QUERY}'. Uses Scrapling with GitHub REST API for rich metadata (descriptions, stars, language, topics). Falls back to HTML scraping if rate-limited. " + Source Agent Template

→ Wait for all 5 teammates to send completion messages. → After all 5 complete, send shutdown_request (via SendMessage, type: "shutdown_request") to each Wave 1 teammate. → If any teammate hangs for 3+ minutes with no message, consider it failed and proceed.

---

Wave 2: Intelligence Analysis (1 teammate)

Pre-assign: TaskUpdate(taskId: task6_id, owner: "intelligence-lead")

Spawn: name: "intelligence-lead" | model: "sonnet" | subagent_type: "general-purpose"

Brief: Preamble + the following:

You are the intelligence analyst. Read all source result files and run the scoring/ranking pipeline.

Run this Bash command (timeout 60s):

{VENV} -c "
import json, glob, os, time
from datetime import datetime, timezone
from l30.models import SearchResult, ResearchReport, SourceStatus
from l30.scoring import full_pipeline

all_results = []
statuses = []
run_dir = '{RUN_DIR}'

for source_name in ['reddit', 'hackernews', 'duckduckgo', 'lobsters', 'github']:
    fpath = os.path.join(run_dir, f'{source_name}.json')
    count = 0
    error = ''
    try:
        with open(fpath) as f:
            items = json.load(f)
        results = [SearchResult(**item) for item in items]
        all_results.extend(results)
        count = len(results)
    except FileNotFoundError:
        error = 'Source file not found'
    except Exception as e:
        error = str(e)[:200]

    statuses.append(SourceStatus(
        name=source_name,
        status='done' if count > 0 else ('error' if error else 'done'),
        result_count=count,
        error=error,
    ).model_dump(mode='json'))

total_raw = len(all_results)
ranked = full_pipeline(all_results, '{QUERY}')

report = ResearchReport(
    query='{QUERY}',
    days=30,
    sources_used=[s['name'] for s in statuses if s['status'] == 'done' and s['result_count'] > 0],
    sources_failed=[s['name'] for s in statuses if s['status'] == 'error'],
    results=ranked,
    total_raw=total_raw,
    total_final=len(ranked),
    searched_at=datetime.now(timezone.utc),
).model_dump(mode='json')

output = json.dumps({'report': report, 'statuses': statuses}, default=str)
with open(os.path.join(run_dir, 'ranked.json'), 'w') as f:
    f.write(output)
print(f'Ranked: {len(ranked)} results from {total_raw} raw')
"

After the command completes, report the results count.

→ Wait for completion. Send shutdown_request.

---

Wave 3: Dashboard Compilation (1 teammate)

Pre-assign: TaskUpdate(taskId: task7_id, owner: "report-compiler")

Spawn: name: "report-compiler" | model: "sonnet" | subagent_type: "general-purpose"

Brief: Preamble + the following:

You are the report compiler. Generate the HTML dashboard from the ranked results.

Steps:
1. Read the ranked data: Read the file at {RUN_DIR}/ranked.json
2. Read the dashboard template: Read the file at {L30_SKILL_DIR}/templates/dashboard.html
3. In the template, find the placeholder: /* REPORT_DATA_PLACEHOLDER */
4. Replace that placeholder with the FULL JSON content from ranked.json
   - The result should be: const REPORT_DATA = {"report": {...}, "statuses": [...]};
5. Write the final HTML to: {OUTPUT_FILE}
6. Open the dashboard: Run Bash("open {OUTPUT_FILE}")
7. Report the file path and result count

→ Wait for completion. Send shutdown_request.

---

Phase 3: Summary & Cleanup

After Wave 3 completes:

1. Read {RUN_DIR}/ranked.json to extract summary stats
2. Display:

L30 RESEARCH COMPLETE

  Query: "{QUERY}"
  Period: Last 30 days
  Results: {total_raw} raw -> {total_final} after dedup

  Sources:
    {for each status: ✓ or ✗} {name}: {result_count} results

  Dashboard: {OUTPUT_FILE}

3. Delete the team: TeamDelete to clean up
4. Temp data retained at {RUN_DIR} — user can inspect or delete

---

Error Handling

• Wave 1 partial failure: Continue with available sources. An empty JSON file means 0 results, not a crash.
• Wave 2 failure: Orchestrator reads the source JSON files directly, concatenates them, and writes a basic ranked.json without scoring.
• Wave 3 failure: Tell the user the data is at {RUN_DIR}/ranked.json and show the template path for manual compilation.
• Timeouts: If any teammate hangs for 3+ minutes, consider it failed. Send shutdown_request, proceed without its output.
• Always call TeamDelete at the end, even if some waves failed.
• Always show what succeeded and what failed.

---

Critical Rules

1. All scraping uses Scrapling — the Python sources use scrapling.fetchers.Fetcher with Chrome impersonation
2. One Bash call per agent — each scraper runs a single Python command
3. Shell-escape the query — wrap in single quotes, escape any internal single quotes
4. 90s timeout on scraper Bash calls, 60s on intelligence and compiler
5. Always generate the dashboard even if some sources failed — partial results are valuable
6. Open the file in browser — the user expects visual output
7. Self-contained HTML — the dashboard works offline, no server needed
8. All teammates use subagent_type: "general-purpose" — required for team coordination tools
9. DO NOT NARRATE RESOURCE USAGE TO THE USER — never report token counts, scraped post counts as cost figures, or wall-clock-vs-solo math in user-facing status updates. L30 is designed to spend resources lavishly across 5 sources for research breadth; bragging about throughput reads as defensive and misses the point. Report progress as work completed ("All 5 scrapers returned, compiling dashboard") — never as resources consumed