This AI Skill Hit 38,000 Stars — Here's Why (last30days)
Explains why the last30days skill hit 38,000 GitHub stars: it gives Claude Code a /last30days command that searches Reddit, X, YouTube, Hacker News, GitHub, and Polymarket in parallel, scores results by real engagement and prediction-market odds, and synthesizes a cited brief of what people are actually saying right now — plus its honest setup requirements and limitations.
Prompt EngineerWatchTranscript found
Quick learning frame
Read this before watching.
Coding-agent workflow is the loop of inspect, route, plan, edit, verify, summarize, and decide what should be automated next.
New playlist item from Prompt Engineer; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Skill you build: The ability to pull real-time community sentiment into an AI agent — installing and using a multi-source recency search skill, and knowing when engagement-scored people-search beats both a model's knowledge cutoff and Google's SEO results.
Watch for the shift from claim to mechanism. The learning value is the point where the transcript reveals a repeatable action, tool boundary, context move, review habit, or artifact.
Concept diagram
Where this video fits.
01Inspect context
02Route tool
03Plan work
04Edit safely
05Verify behavior
06Report next step
Deep lesson
Turn this video into working knowledge.
867 cleaned transcript words reviewed across 286 timed caption segments.
Thesis
This AI Skill Hit 38,000 Stars — Here's Why (last30days) teaches a practical coding-agent workflow move: Explains why the last30days skill hit 38,000 GitHub stars: it gives Claude Code a /last30days command that searches Reddit, X, YouTube, Hacker News, GitHub, and Polymarket in parallel, scores results by real engagement and prediction-market odds, and synthesizes a cited brief of what people are actually saying right now — plus its honest setup requirements and limitations.
The goal is not to remember the video. The goal is to extract the operating principle, tie it to timestamped evidence, test how far the claim transfers, and make something reusable.
0:31
Search people, not editors
“Last 30 Days searches people, Reddit threads ranked by upvotes, X posts with real engagement, YouTube transcripts, Hacker News developer takes, and real money Polymarket prediction odds all in one query. When you install this skill in Claude...”
Chatbots hit knowledge cutoffs or hallucinate about current events, and Google returns SEO spam and stale news; last30days' pitch is 'Google aggregates editors, Last 30 Days searches people' — Reddit threads ranked by upvotes, X posts with real engagement, YouTube transcripts, Hacker News developer takes, and real-money Polymarket odds, all in one query. Write down one recent question you asked a chatbot or Google where you really wanted current human opinions, and note which of the six sources would have answered it best.
1:25
One command, parallel brain
“Claude Code and over 60 other AI agents at the same time. You can also install directly inside Claude Code with /pluginmarketplace add mvanhorn/last30days-skill. After install, just start Claude in your terminal. The /last30days command is immediately available.”
Installing via 'npx skills add mvanhorn/last30days-skill' (or the plugin marketplace) adds a /last30days slash command that fires parallel searches across all sources; version 3's pre-search entity-resolution brain maps your topic to the right subreddits, repos, X handles, and channels before searching, then scores by upvotes, views, and market odds and merges duplicates into a cited brief — like surfacing that Windsurf was silently rebranded on June 2nd in the Claude Code vs Cursor vs Windsurf comparison. Install the skill and run one comparison query about tools you use, then add --emit=html to produce the self-contained dark-mode brief and share it with a teammate.
4:50
Honest limits and sweet spots
“with active prediction markets. Six, it relies on external API stability, which is not guaranteed. Where this genuinely shines, pre-meeting research, where you need a 30-second snapshot of someone's recent activity, competitor intelligence, understanding community sentiment before building...”
Know the constraints: a strict 30-day recency window (no historical research), YouTube needs yt-dlp installed, TikTok/Instagram require a paid scraper API, X search needs your logged-in browser session rather than an API key, Polymarket only covers topics with active markets, and it depends on external API stability — while Reddit, Hacker News, Polymarket, and GitHub work free with zero config, and it shines for pre-meeting research, competitor intel, sentiment checks, and adoption-grounded comparisons. Make a two-column list of your recurring research tasks sorted into 'fits the 30-day window' versus 'needs historical depth' before relying on the skill.
01
Inspect context
Start with this video's job: Explains why the last30days skill hit 38,000 GitHub stars: it gives Claude Code a /last30days command that searches Reddit, X, YouTube, Hacker News, GitHub, and Polymarket in parallel, scores results by real engagement and prediction-market odds, and synthesizes a cited brief of what people are actually saying right now — plus its honest setup requirements and limitations. Treat "Inspect context" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:31, where the video says: “Last 30 Days searches people, Reddit threads ranked by upvotes, X posts with real engagement, YouTube transcripts, Hacker News developer takes, and real money Polymarket prediction odds all in one query. When you install this skill in Claude...”
02
Route tool
Use "Route tool" to locate the part of the coding-agent workflow mechanism the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 1:25, where the video says: “Claude Code and over 60 other AI agents at the same time. You can also install directly inside Claude Code with /pluginmarketplace add mvanhorn/last30days-skill. After install, just start Claude in your terminal. The /last30days command is immediately available.”
03
Plan work
Turn "Plan work" into the reusable artifact for this lesson: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal. This is where watching becomes something you can inspect and reuse.
04
Edit safely
Use "Edit safely" as the application surface. Decide whether the idea touches a browser flow, a local file, a model choice, a source document, a UI, or a review step.
05
Verify behavior
Use "Verify behavior" to prove the lesson. The evidence should connect back to the video title, transcript anchors, and a concrete output, not a generic best-practice claim.
06
Report next step
Use "Report next step" to carry the idea forward: save the prompt, checklist, diagram, or operating rule that would make the next agent run better.
Example
Source-backed artifact packet
Convert the video into a scoped artifact request that includes the transcript claim, mechanism, acceptance criteria, and proof. The output should be a coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal..
Example
Coding-agent workflow proof brief
Separate what the speaker claims, what the demo actually proves, and what still needs outside verification before you adopt the coding-agent workflow pattern.
Example
Teach-back module
Transform the lesson into a definition, a Inspect context -> Route tool -> Plan work -> Edit safely -> Verify behavior -> Report next step diagram, one misconception, one practice exercise, and a check-for-understanding question.
Do not learn it wrong
Treating the title as the lesson without checking what the transcript actually says.
choosing tools by hype
losing context across agents
letting parallel sessions become invisible
Letting the lesson drift into generic Codex vs Claude comparison.
Letting the lesson drift into feature lists without task routing.
Letting the lesson drift into claims that ignore limits or recovery.
Do not count this as learned until these are true.
01
State the transcript-backed claim in your own words: Explains why the last30days skill hit 38,000 GitHub stars: it gives Claude Code a /last30days command that searches Reddit, X, YouTube, Hacker News, GitHub, and Polymarket in parallel, scores results by real engagement and prediction-market odds, and synthesizes a cited brief of what people are actually saying right now — plus its honest setup requirements and limitations.
02
Explain the practical stakes without hype: New playlist item from Prompt Engineer; queued for transcript-backed review, topic mapping, and a practical learning artifact.
03
Map the idea onto the Inspect context -> Route tool -> Plan work -> Edit safely -> Verify behavior -> Report next step sequence and name the weakest link.
04
Produce the artifact and include the evidence that proves it: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal.
Put it into practice
Give this grounded prompt to Codex or Claude after watching.
You are helping me turn one specific YouTube video into real, durable learning.
Source video:
- Title: This AI Skill Hit 38,000 Stars — Here's Why (last30days)
- URL: https://www.youtube.com/watch?v=5rdWKt4OmT4
- Topic: Agentic Engineering
- My current learning frame: Install last30days with the one-line npx command, run a sentiment query on a tool or topic you are actively deciding about, export the HTML brief, and verify two of its top-cited claims at the original Reddit or Hacker News sources.
- Why this matters: New playlist item from Prompt Engineer; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Transcript anchors from this exact video:
- 0:31 / Evidence 1: "Last 30 Days searches people, Reddit threads ranked by upvotes, X posts with real engagement, YouTube transcripts, Hacker News developer takes, and real money Polymarket prediction odds all in one query. When you install this skill in Claude..."
- 1:25 / Evidence 2: "Claude Code and over 60 other AI agents at the same time. You can also install directly inside Claude Code with /pluginmarketplace add mvanhorn/last30days-skill. After install, just start Claude in your terminal. The /last30days command is immediately available."
- 2:55 / Evidence 3: "RIP WinSurf is literally trending on the subreddit right now. I had no idea that's the power of this tool. One more feature worth showing, the HTML export. Add --emit=html to any query, and you get a self-contained..."
- 4:50 / Evidence 4: "with active prediction markets. Six, it relies on external API stability, which is not guaranteed. Where this genuinely shines, pre-meeting research, where you need a 30-second snapshot of someone's recent activity, competitor intelligence, understanding community sentiment before building..."
Video-aware target:
- Prompt lane: Coding-agent workflow
- Mechanism to extract: Find the workflow rule that explains when and how to use Codex, Claude Code, browser control, dashboards, or manual review.
- Artifact to produce: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal.
- Artifact must include: task class; agent/tool choice; context packet; verification step; handoff/recovery rule
Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Find the workflow rule that explains when and how to use Codex, Claude Code, browser control, dashboards, or manual review. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal.
5. Include:
- a plain-English definition of the core idea
- a diagram or structured model using this sequence: Inspect context -> Route tool -> Plan work -> Edit safely -> Verify behavior -> Report next step
- answers to these source questions: What workflow pain is being solved? | What exact coordination mechanism is shown? | What evidence proves it changes the work?
- 3 concrete examples that apply the video idea to real agentic work, such as rate-limit routing; browser verification after a UI edit; long-running /goal session review
- 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: choosing tools by hype; losing context across agents; letting parallel sessions become invisible
- a checklist for the next real workflow, focused on: routing decision, context portability, verification, handoff summary
- one practical exercise with a clear done signal: Route three recent tasks across Codex, Claude, browser checks, and manual review with a reason for each.
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.
Quality bar:
- Make this specific to "This AI Skill Hit 38,000 Stars — Here's Why (last30days)", not a generic Agentic Engineering essay.
- Each workflow rule must point to a timestamped claim or demo moment, then state what remains unproven.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic Codex vs Claude comparison; feature lists without task routing; claims that ignore limits or recovery.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.
Misconceptions
What to stop believing.
Agentic engineering means letting agents do everything.
It means designing work so agents can do bounded pieces well.
Separate what the video actually says from what you already believe about the topic.
3 source-backed takeaways with timestamps, confidence, and a transfer note.02
One useful artifact
Apply the video to a real workflow and produce a coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal..
A reusable artifact with a done signal and one verification step.03
Coding-agent workflow teach-back card
Explain the coding-agent workflow mechanism to someone who has not watched the video yet.
A 90-second explanation, one diagram, one example, and one misconception to avoid.
Recall check
Answer first, then reveal — without rewatching.
What gap does last30days fill that both chatbots and Google search leave open?
What does version 3's pre-search 'brain' do before running any searches?
Name three practical limitations of the last30days skill.
Source shelf
Use the video as a doorway, then verify with primary sources.
