last30days: Reading the Past Month's Community Discussions with an AI Agent

When I discover a new development tool, I start with the official documentation. It is relatively easy to find out what it does and how to get started. But before deciding to adopt it, I have a different question: what do people who have tried it recently like, and where are they getting stuck?

Answering that question means moving between several places: reading user experiences on Reddit, finding developers' posts on X, watching demonstrations on YouTube, and checking related issues on GitHub. Checking publication dates and grouping similar discussions takes time, just as finding the material does.

last30days is an open-source skill for delegating this kind of research into recent trends to an AI agent. This post looks at what the tool does, how its research proceeds, where it is useful, and what its limitations are.

What Is last30days?

last30days researches a given topic with a focus on the past 30 days and summarizes material found across multiple platforms. It supports Reddit, X, YouTube, Hacker News, GitHub, and others, though the sources actually queried depend on the environment and configuration. The official GitHub repository describes the project and its support scope.

Here, a skill means a package of instructions for an AI agent and tools it can run. last30days includes both a SKILL.md describing the research procedure and Python code that performs the actual collection. The agent you use reads the instructions, runs the tools, and carries out the research. The project's explanation of its concepts

The focus is on recent community reactions. While official documentation is needed to verify features and conditions of use, the material gathered with last30days helps reveal what people experience when using those features.

For example, when considering a new library, you might read the release notes to understand the changes, then look for recurring frustrations or use cases in related discussions. Reading both can surface evaluation questions that a feature list alone does not reveal.

How Does It Conduct Research?

The workflow becomes easier to understand when you separate the AI agent's judgments from the Python engine's collection and organization work. On a host with web search and reasoning capabilities, the agent creates a search plan, passes it to the engine, and reads the collected results to write the final explanation. Depending on the environment, it may also use the engine's internal planning and ranking path. Skill execution instructions

1. 01Search plan
2. 02Collect material
3. 03Assess relevance, recency, and engagement
4. 04Deduplicate and organize evidence
5. 05Summarize results

1. Turn the Question into a Search Plan

First, clarify what is being investigated. Products or people may share a name, so the agent checks relevant accounts, repositories, and communities to narrow the target. It then divides the question into several subqueries and decides which sources each search should query.

For example, when asking how a particular library is regarded, user reviews and migration experiences are different search perspectives. Splitting the question is more effective at finding the necessary evidence than repeatedly searching the product name alone.

This step involves the model's interpretation of the user's question. That is why it helps to provide the type of product and what you want to know, rather than just a topic name.

2. Collect Material from Each Source

The Python engine queries multiple sources in parallel according to the plan. The material available differs by platform. It has to handle different forms of information, such as discussion posts and comments, video details and subtitles, and repository activity.

Installed CLIs, API keys, and authentication status affect the scope of the investigation. YouTube or X appearing on a support list and that source being successfully queried in the current run are separate facts. This is why the actual collection scope matters when reading the results.

3. Consider Relevance, Recency, and Engagement Together

Collected material is ranked using its relevance to the topic, how recent it is, and the level of response it has received. The information used for engagement also varies by platform: upvotes, comment counts, likes, and views are handled in a way suited to each source.

The initial ranking code in v3.23.0 that I checked locally assigns weights of 65% to relevance, 25% to recency, and 10% to engagement. These weights apply to the initial ranking within each search-result list. Results are subsequently merged and ranked again, so this should be distinguished from the scoring formula for the final report. Initial ranking code

The point is that engagement is one of several criteria. Even a highly viewed item is unlikely to help if it is unrelated to the question.

4. Merge Duplicates and Organize the Evidence

Different queries may find the same post. The engine merges duplicates by URL and combines their ranks across the search results. The technique used here is RRF, or Reciprocal Rank Fusion. Think of it as combining the ranking information for material that appears near the top of multiple lists. Result-merging code

Depending on the research type, it also clusters related material. Grouping posts about the same event or controversy, for example, makes it easier for the agent to identify the issues than reading scattered links one by one. Clustering code

However, similar posts across multiple platforms do not necessarily mean a fact has been independently confirmed. They may all be quoting the same announcement or original source. How widely material has spread and how independent the supporting evidence is must be judged separately.

5. The Agent Reads and Summarizes the Results

Finally, the agent reads the collected and organized evidence, then explains the main points and recurring patterns. How much was gathered from each source and which sources were inaccessible also affect how the results should be interpreted.

In addition to general research, it supports workflows such as comparisons and topic discovery. You can change the request to specify two tools to compare, for example, or ask it to find worthwhile research topics in a field of interest. Supported modes and options are described in the configuration documentation.

What Makes It Useful?

First, it can reduce the repetitive work of starting an investigation. Searching multiple sites, checking dates, and deduplicating links recur with every new topic. Once that process is delegated, the user can spend time selecting and reading the most relevant original sources.

Second, it lets you examine different kinds of material together. You can see whether a frustration from a user review is also being discussed in GitHub issues, or whether users have actually responded to a feature introduced in a video. The value of gathering multiple sources lies less in their number than in the opportunity to compare perspectives.

Third, it helps generate the next questions to investigate. Recurring installation problems, expectations for a feature, or conflicting assessments can give the research a clearer direction. When writing a blog post, this material can also help identify issues readers care about and check them again against official documentation.

It fits tasks that need recent context: researching a tool's reputation before adoption, checking reactions after a product update, or exploring discussion points before writing. It is especially useful as a starting point when you know the topic but do not know which original sources to read first.

Limitations to Keep in Mind

Popularity and Accuracy Are Different Criteria

Engagement shows which discussions attract attention. But many likes do not make a technical explanation accurate. Provocative claims and promotional content can also draw strong reactions.

Before citing a feature, performance figure, or support claim from a summary in your writing, check the official documentation or original source again. Community reactions are better read as material for understanding experiences and opinions.

An Inaccessible Source Is Not a Quiet Source

Expired authentication, rate limits, or network problems can prevent collection. If the result is zero items under those conditions, you cannot conclude that no relevant discussion exists.

The project instructs the agent to distinguish a successful query with no results from authentication failures, timeouts, and partial collection. doctor diagnoses configuration and source status, while doctor --postmortem helps identify problems using the last run's results. --preflight shows configuration origins and planned access and storage operations before the actual investigation. Diagnostics and execution instructions

Readers should check not only which claims a report makes, but also what material was available when those claims were formed.

The Material Varies by Topic and Language

There may not be enough useful material if the supported platforms have few relevant posts or the product name is ambiguous. Even when you ask in Korean and receive a Korean summary, the collected originals may lean toward English-language platforms.

If reactions from Korean-language communities matter, those communities also need to be investigated directly. A result written in Korean should not be interpreted as representative of Korean users' opinions merely because of its language.

The same caution applies to the 30-day window. Longstanding frustrations and long-term assessments are hard to capture in a short research window alone. Check the period setting alongside the actual dates of the material, and compare older sources separately if you want to understand longer-term changes.

Setup and Execution Have Costs

The cost of installing the skill and the cost of conducting research should be considered separately. Some access paths are free, but particular sources may require separate API keys or a logged-in session, and may incur usage-based charges. The agent also consumes the host's allowance when creating search plans and summaries. Source-specific configuration guide

If you choose a path that uses a browser session, you also need to understand which account it accesses. Starting with only the sources you need, checking the actual collection scope, and expanding from there is easier to manage.

Waiting time grows as more sources, comments, and subtitles are examined. For a question about a single fact, going straight to official documentation may be faster. The tool becomes more useful for questions that require gathering and reading many people's recent reactions.

Installation and Simple Usage Examples

For installation by host and runtime requirements, see the installation guide in the official README. Source-specific API keys and output options are covered in CONFIGURATION.md.

After installation, select the skill and provide both the research topic and its purpose. The following are examples of how to frame a question, not quotations from actual research results.

Use last30days to research recent migration experiences with Next.js App Router. Separate recurring problems from examples of solutions, and tell me which sources could not be checked.

Use last30days to compare recent user reactions to two development tools. Focus on team collaboration and extensions.

Naming the products precisely and narrowing the comparison criteria also clarifies which original sources you should verify once the results arrive.

An Actual Research Example: INTENT.md

A supplementary research report on INTENT.md saved on September 6, 2026, illustrates how last30days can be used. It examined how the practice of documenting intent and constraints connects to actual code changes, verification, and agent handoffs. Recent trends were scoped to August 7 through September 6, with older material classified as background.

Checking the original sources found in the results revealed different ways of applying the idea.

• X-GIS: A PR merged on September 5 added a new GitHub issue template for recording intent, constraints, rejected approaches, and completion checks, and linked it to the existing guidance. This captures intent when an issue is created instead of adding a separate document. PR and changed files
• callback: PR #81 replaced the collector based on requirements in INTENT.md and added verification material. It had not yet been merged when checked on September 6. The before-and-after collector performance figures need to be distinguished from measurements of the intent document's own effects. Original PR
• Pathmode: On August 30, it announced a fix for handoff records not reaching the next agent; on September 3, it announced a review feature tied to PR commits and intent-document versions. This review is advisory and does not block merging. Changelog

These three cases are evidence of attempts to connect intent documents to development workflows. However, finding adoption examples and proving productivity gains are different things. Effects described by a PR author or product vendor must not be read as results from an independent comparative experiment.

In this process, the last30days results provided a starting point for finding original sources to read and issues to compare. To turn them into evidence usable in an article, the actual changed files, merge status, and scope of the feature still need to be checked in those sources. Collection counts likewise describe the volume of search results, not the number of independent adoption cases.

Using It for Blog Research

For blog research, a suitable workflow is to find discussion points in the summary, then verify key claims against the linked originals and official documentation. Used that way, it reduces the effort of starting research while allowing you to examine the article's evidence yourself.

The feature descriptions are based on official documentation checked on September 6, 2026, and local v3.23.0 code. The case study was prepared by comparing an INTENT.md research report saved that day with its linked original sources. While supplementing this post, I did not rerun the research engine or measure productivity improvements. Supported sources and configuration methods may vary by version.