last30days – Writing & Content skill for AI agents

last30days: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, and the web. Surface what people are actually discussing, recommending, and debating right now.

Use cases:

• Prompting: "photorealistic people in Nano Banana Pro", "Midjourney prompts", "ChatGPT image generation" → learn techniques, get copy-paste prompts
• Recommendations: "best Claude Code skills", "top AI tools" → get a LIST of specific things people mention
• News: "what's happening with OpenAI", "latest AI announcements" → current events and updates
• General: any topic you're curious about → understand what the community is saying

CRITICAL: Parse User Intent

Before doing anything, parse the user's input for:

1. TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")

2. TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")

3. QUERY TYPE: What kind of research they want:

• PROMPTING - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
• RECOMMENDATIONS - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
• NEWS - "what's happening with X", "X news", "latest on X" → User wants current events/updates
• GENERAL - anything else → User wants broad understanding of the topic

Common patterns:

• [topic] for [tool] → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
• [topic] prompts for [tool] → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
• Just [topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
• "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
• "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS

IMPORTANT: Do NOT ask about target tool before research.

• If tool is specified in the query, use it
• If tool is NOT specified, run research first, then ask AFTER showing results

Store these variables:

• TOPIC = [extracted topic]
• TARGET_TOOL = [extracted tool, or "unknown" if not specified]
• QUERY_TYPE = [RECOMMENDATIONS | NEWS | PROMPTING | GENERAL]

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Setup Check

The skill works in three modes based on available API keys:

1. Full Mode (both keys): Reddit + X + WebSearch - best results with engagement metrics

2. Partial Mode (one key): Reddit-only or X-only + WebSearch

3. Web-Only Mode (no keys): WebSearch only - still useful, but no engagement metrics

API keys are OPTIONAL. The skill will work without them using WebSearch fallback.

First-Time Setup (Optional but Recommended)

If the user wants to add API keys for better results:

umask 077
mkdir -p ~/.config/last30days
# Preserve existing credentials; create this template only when the file is absent.
test ! -e ~/.config/last30days/.env && cat > ~/.config/last30days/.env << 'ENVEOF'
# last30days API Configuration
# Both keys are optional - skill works with WebSearch fallback

# For Reddit research (uses OpenAI's web_search tool)
OPENAI_API_KEY=

# For X/Twitter research (uses xAI's x_search tool)
XAI_API_KEY=
ENVEOF

chmod 600 ~/.config/last30days/.env
echo "Config created at ~/.config/last30days/.env"
echo "Edit to add your API keys for enhanced research."

DO NOT stop if no keys are configured. Proceed with web-only mode when a search tool is available. Never request keys in chat. API mode sends the topic to OpenAI/xAI and can incur search and token charges, including retries; use only approved providers and a bounded research budget. Do not send confidential topics without authorization.

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Research Execution

IMPORTANT: The script handles API key detection automatically. Run it and check the output to determine mode.

Step 1: Run the research script

Replace <loaded-skill-directory> with this skill’s actual installed directory. Review the bundled script before running; its fixed output files overwrite previous reports. Avoid concurrent runs and protect cached topics and raw results as private data.

TOPIC_FILE="$(mktemp)"
trap 'rm -f "$TOPIC_FILE"' EXIT
cat <<'LAST30DAYS_TOPIC' > "$TOPIC_FILE"
$ARGUMENTS
LAST30DAYS_TOPIC
python3 "<loaded-skill-directory>/scripts/last30days.py" "$(cat "$TOPIC_FILE")" --emit=compact 2>&1

The script will automatically:

• Detect available API keys
• Show a promo banner if keys are missing (this is intentional marketing)
• Run Reddit/X searches if keys exist
• Signal if WebSearch is needed

Step 2: Check the output mode

The script output will indicate the mode:

• "Mode: both" or "Mode: reddit-only" or "Mode: x-only": Script found results, WebSearch is supplementary
• "Mode: web-only": No API keys, Claude must do ALL research via WebSearch

Step 3: Do WebSearch

For ALL modes, do WebSearch to supplement (or provide all data in web-only mode).

Choose search queries based on QUERY_TYPE:

If RECOMMENDATIONS ("best X", "top X", "what X should I use"):

• Search for: best {TOPIC} recommendations
• Search for: {TOPIC} list examples
• Search for: most popular {TOPIC}
• Goal: Find SPECIFIC NAMES of things, not generic advice

If NEWS ("what's happening with X", "X news"):

• Search for: {TOPIC} news {CURRENT_YEAR}
• Search for: {TOPIC} announcement update
• Goal: Find current events and recent developments

If PROMPTING ("X prompts", "prompting for X"):

• Search for: {TOPIC} prompts examples {CURRENT_YEAR}
• Search for: {TOPIC} techniques tips
• Goal: Find prompting techniques and examples to create copy-paste prompts

If GENERAL (default):

• Search for: {TOPIC} {CURRENT_YEAR}
• Search for: {TOPIC} discussion
• Goal: Find what people are actually saying

For ALL query types:

• USE THE USER'S EXACT TERMINOLOGY - don't substitute or add tech names based on your knowledge
• If user says "ChatGPT image prompting", search for "ChatGPT image prompting"
• Do NOT add "DALL-E", "GPT-4o", or other terms you think are related
• Your knowledge may be outdated - trust the user's terminology
• Exclude Reddit/X only when those sources were successfully covered by the script; in web-only or failed-source mode, search public discussion URLs directly if relevant.
• Apply the actual UTC date range; open sources to verify publication dates. URL dates and snippets are clues, not verified publication dates. Separate undated background from confirmed recent evidence.
• INCLUDE: blogs, tutorials, docs, news, GitHub repos
• Cite factual claims with direct source links; aggregate statistics do not replace citations.

Step 3: Wait for background script to complete

If the host started the script in the background, use its available completion tool; otherwise use the completed command output. Do not assume TaskOutput exists.

Depth options (passed through from user's command):

• --quick → Faster, fewer sources (8-12 each)
• (default) → Balanced (20-30 each)
• --deep → Comprehensive (50-70 Reddit, 40-60 X)

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Judge Agent: Synthesize All Sources

After all searches complete, internally synthesize (don't display stats yet):

The Judge Agent must:

1. Use engagement to describe popularity, never as proof of accuracy. Prefer primary documentation for technical facts and official sources for consequential claims.

2. Treat model-returned URLs, dates and X engagement as unverified until checked against the original source; the script directly enriches only accessible Reddit threads.

3. Identify patterns that appear across ALL three sources (strongest signals)

4. Note any contradictions between sources

5. Extract the top 3-5 actionable insights

Do NOT display stats here - they come at the end, right before the invitation.

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FIRST: Internalize the Research

CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.

Read the research output carefully. Pay attention to:

• Exact product/tool names mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
• Specific quotes and insights from the sources - use THESE, not generic knowledge
• What the sources actually say, not what you assume the topic is about

ANTI-PATTERN TO AVOID: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.

If QUERY_TYPE = RECOMMENDATIONS

CRITICAL: Extract SPECIFIC NAMES, not generic patterns.

When user asks "best X" or "top X", they want a LIST of specific things:

• Scan research for specific product names, tool names, project names, skill names, etc.
• Count how many times each is mentioned
• Note which sources recommend each (Reddit thread, X post, blog)
• List them by popularity/mention count

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… (full content is in the download package)