/last30days Skill: AI Agent Search Across Reddit, X, YouTube, GitHub, and Polymarket

/last30days is an open-source AI agent skill that turns a coding agent into a recency-focused research engine. Instead of asking only web search what ranked, it searches recent activity across social platforms, developer communities, video transcripts, prediction markets, and GitHub, then synthesizes a brief around what people actually engaged with.

The repository, mvanhorn/last30days-skill, had about 27.7k GitHub stars, 2.4k forks, 14 releases, and an MIT license on June 5, 2026. Its latest listed release was v3.3.0 from May 17, 2026, and the README says the v3 engine has 1,012 tests passing.

This post explains what the skill does, how it fits the Agent Skills ecosystem, how to install it across Claude Code, Codex, Cursor, Copilot, Gemini CLI, and OpenClaw, what v3 changed, and where the operational risks are.

TL;DR

What /last30days does

Most search tools start with indexed pages. /last30days starts with the last month of human activity around a person, company, product, topic, or comparison.

The README describes the project as an AI agent-led search engine scored by upvotes, likes, and real money - not editors. That is the useful mental model: it is not trying to be another generic search box. It is trying to bridge a set of disconnected platforms an AI model normally cannot search together.

For example, a single /last30days Peter Steinberger run can combine recent X posts, Reddit threads, GitHub pull-request activity, YouTube transcripts, and community reactions into one brief. A tool-comparison query can pull live GitHub stars, issues, discussions, social comments, and web coverage into the same synthesis.

The value is recency plus cross-source synthesis. A model's training data is stale. Google may rank old pages. LinkedIn may show a sanitized profile. /last30days asks what the relevant communities, creators, developers, and markets have said recently.

Sources it can search

The README lists a broad source matrix. Some work with no setup; others need browser sessions, local tools, or API keys.

That source breadth is why the skill has a different shape from normal search. A Reddit thread with 1,500 upvotes, a TikTok with millions of views, and a Polymarket market with meaningful odds are all popularity signals, but they do not live in one search index. /last30days makes the agent orchestrate across them.

Install paths

The README supports several surfaces.

Claude Code

Claude Code is the recommended path because marketplace plugin installs can auto-update.

/plugin marketplace add mvanhorn/last30days-skill
/plugin install last30days

The README also supports the Agent Skills install path for Claude Code:

npx skills add mvanhorn/last30days-skill -g -a claude-code

Use one install method per machine. The README notes that Claude Code does not dedupe across plugin and npx skills installs, so installing both can expose duplicate /last30days entries.

Codex, Cursor, Copilot, Gemini CLI, and other hosts

For open Agent Skills hosts:

npx skills add mvanhorn/last30days-skill -g

The global flag makes it available across projects. To target a specific host:

npx skills add mvanhorn/last30days-skill -g -a codex
npx skills add mvanhorn/last30days-skill -g -a cursor
npx skills add mvanhorn/last30days-skill -g -a gemini-cli

Updates use:

npx skills update last30days -g

claude.ai web and OpenClaw

For claude.ai, the README says to download last30days.skill from the latest release, upload it through Settings > Capabilities > Skills, and enable code execution and file creation.

For OpenClaw:

clawhub install last30days-official

What works immediately and what needs keys

The fastest path is zero-config research:

This matters for team rollout. A skill that touches many platforms becomes an access-management project quickly. Treat every key, browser session, and output file as part of the security model.

On macOS, the README also documents Keychain support through skills/last30days/scripts/setup-keychain.sh, so keys can be stored in the system Keychain instead of only in .env files.

What v3 changed

The v3 pipeline is the most important part of the current README.

Shareable HTML briefs

The skill can emit a self-contained HTML brief:

/last30days OpenClaw --emit=html

Or in natural language:

/last30days OpenClaw, give me a shareable HTML brief

The README says the generated file is dark-mode, print-friendly, offline-capable, and saved under ${LAST30DAYS_MEMORY_DIR}/{topic}-brief.html, with the default memory directory under ~/Documents/Last30Days/.

This is useful because research output often needs to move into Slack, email, Notion, or a client update. A raw chat transcript is rarely the right artifact.

Intelligent pre-research

The v3 engine no longer searches only the literal query. It tries to resolve the relevant people, GitHub profiles, subreddits, X handles, YouTube channels, and TikTok hashtags before the main search begins.

That changes result quality. Searching "OpenClaw" as a keyword is weaker than resolving the creator, GitHub repo, relevant communities, and adjacent agent-tool discourse first.

Best Takes

The v3 engine includes a second judge for humor, wit, virality, and shareability. That matters because community research is not only about correct facts. Sometimes the most useful signal is the reaction everyone repeats.

For operators, this is strongest in briefing and content workflows. For due diligence or safety work, treat viral reactions as color, not evidence.

Cross-source cluster merging

When the same story appears on Reddit, X, and YouTube, v3 can merge it into one cluster instead of showing three isolated results. Entity-based overlap detection helps catch related stories even when different platforms use different titles.

That reduces the classic research problem where one event looks like five separate events because every platform names it differently.

Single-pass comparisons

The README says comparison runs such as "CLI vs MCP" moved from serial multi-pass behavior to a single pass with entity-aware subqueries. The stated effect is the same depth in roughly 3 minutes instead of much longer serial runs.

This makes the skill more useful for product and tool comparisons, where the user wants a side-by-side answer rather than three disconnected topic briefs.

Competitor discovery

/last30days OpenAI --competitors can ask the host reasoning model to identify peer entities, then run a comparison plan across them. The README's example discovers Anthropic and xAI, runs parallel pipelines, saves raw markdown per entity, and merges a three-way comparison.

This is a useful pattern for founders, analysts, and product marketers. It is also a place where verification matters: competitor choice can bias the entire run.

GitHub person-mode

When the topic is a person, the engine can shift into author-scoped GitHub queries. Instead of searching for mentions of a name, it asks what that person shipped recently and where it landed.

For engineer, founder, or open-source maintainer research, that is a better signal than stale bios.

ELI5 mode

After a research run, users can ask for "eli5 on" to rewrite the synthesis in plain language while keeping the same sources and data. This is useful when a brief needs to move from an expert user to a broader audience.

Use cases that fit

Meeting prep

Before a sales call, investor meeting, founder interview, or customer conversation, /last30days can assemble what the person or company has actually done recently: posts, interviews, GitHub activity, community debate, product issues, and public reactions.

This overlaps directly with the skill-library idea in Hiten Shah's AI Skill Library Strategy: agents become more useful when they know not just the data, but the repeated preparation workflow.

Tool comparisons

For "X vs Y" questions, the skill can compare live GitHub stats, community complaints, Reddit comments, release notes, YouTube reviews, and web coverage. This is stronger than a generic blog comparison when tools are moving weekly.

Content and prompt research

Creators and prompt engineers can use it to find recent examples, community-tested formats, prompt patterns, failure reports, and creator reactions. The README explicitly frames this as a way to avoid months-old training data when AI workflows change daily.

Market and public-opinion checks

Polymarket odds, social reactions, and developer forums provide different kinds of signal. None is definitive alone. Together, they can surface what is being believed, debated, or priced in.

Trend monitoring

The README documents a storage mode with SQLite plus watchlist and briefing scripts for scheduled runs. That turns one-off research into a recurring monitoring loop for topics, clients, competitors, or communities.

How it fits Agent Skills

/last30days is a concrete example of a skill that is more than a prompt. It packages instructions, scripts, source configuration, output behavior, and host-specific install surfaces into one reusable agent capability.

That maps cleanly to the broader Agent Skills pattern:

For the general mental model, read What are Agent Skills?. For the company strategy angle, read Hiten Shah's skill-library thesis.

Strengths

The strongest part of /last30days is that it treats research as an agent workflow, not a single search call.

Key advantages:

• Recency: it is designed for the last month, where models and static articles are weak.
• Cross-source coverage: social, developer, video, market, and web signals in one run.
• Engagement-aware ranking: upvotes, likes, comments, odds, and activity matter.
• Person and product resolution: v3 can resolve handles, communities, repos, and related entities.
• Useful artifacts: HTML briefs and stored snapshots are easier to share than raw chat.
• Open-source posture: MIT license, no tracking claim, visible code, tests, and changelog.

It is especially strong when the answer depends on what communities recently noticed, not just what official docs say.

Limits and risks

Any engagement-ranked research tool inherits the flaws of engagement.

This is not a replacement for primary-source verification. It is a way to find what deserves verification faster.

Practical setup advice

If you are installing it for one person:

1. Start with the default free sources.
2. Run three topics you already know well.
3. Inspect the raw output and citations.
4. Add YouTube via yt-dlp.
5. Add paid or browser-backed sources only after you know the workflow helps.

If you are installing it for a team:

1. Decide whether the skill is personal, global, or project-scoped.
2. Create separate API keys for research.
3. Document allowed topics and prohibited sensitive use.
4. Set LAST30DAYS_MEMORY_DIR intentionally.
5. Review generated HTML briefs before sharing externally.
6. Pin or review updates instead of blindly auto-updating in sensitive environments.

For most teams, the right first use is low-risk external research: public company monitoring, tool comparison, launch reaction, or open-source ecosystem scans.

Source links

• mvanhorn/last30days-skill GitHub repository
• Runtime skill spec: skills/last30days/SKILL.md
• Configuration guide
• Changelog
• Latest releases
• What are Agent Skills? Complete Guide
• Hiten Shah's AI Skill Library Strategy
• Agent Skills Security Threats
• Browse AI agent skills

Bottom line

/last30days is useful because it does something normal search and normal chat both struggle with: it pulls recent public signal from fragmented platforms, scores it by engagement, resolves the right entities before searching, and turns the result into an agent-usable brief.

It is not a truth machine. It is a recency and signal-discovery layer. Used well, it tells you where the conversation moved in the last month, which sources are worth reading, and what claims need verification before you act.

Status note: repository stars, release count, latest release date, source list, install commands, and setup details were checked against the public GitHub README on June 5, 2026. Verify upstream before installing, citing live stats, or using paid source integrations.