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X Research

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timscheuerai
x-research

Research X posts and conversations in the author's niche and turn the strongest ones into sourced content ideas and angles the author can support with their own evidence. Use for niche trend scans, finding strong posts, creator research or building an X creator watchlist; it produces research briefs, not published posts.

Overview

Publishertimscheuerai
Repositorylinkedin-funnel
Skill namex-research
Stars
60
Forks
20
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by timscheuerai on GitHub. Read the source before you install it.

Installation

Install the X Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/timscheuerai/linkedin-funnel/tree/main/skills/x-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/timscheuerai/linkedin-funnel.git /tmp/linkedin-funnel
mkdir -p .claude/skills
cp -r /tmp/linkedin-funnel/skills/x-research .claude/skills/x-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable X Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads X Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so X Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Workspace: all context paths below (identity, audience, strategy, voice, brand, inspiration, raw, research, output, index, log, workspace preferences and context scripts) resolve from context/ in the kit root. Read both the root AGENTS.md and context/AGENTS.md. Skill-local references/ and scripts/ resolve from this skill directory.

X research

Find useful conversations and unusually strong posts the author can contribute to from their own work. Reach is evidence of attention, not proof of truth or pipeline. The research loop: decompose the question, search, refine, follow threads, read linked sources, and synthesize with citations.

Ground the research

Read the relevant context in order:

  1. identity/positioning.md and identity/proof.md.
  2. audience/ideal-follower.md.
  3. strategy/pillars.md and strategy/funnel.md.
  4. voice/linkedin-voice.md and voice/formats.md when forming angles or drafting. These calibrate the author's voice, not X's format or hook limits.
  5. inspiration/creator-bank.md for creators the author already studies.

Search the context with rg or the optional scripts/qmd.sh for the author's receipts, previous framing and published pieces. Check dates before calling something current. The author's current instructions and supplied text take precedence over older files. Never turn another creator's result into the author's experience.

Read niche.md for how to build search seeds, selection criteria and the brief structure. The author's existing pillars remain the taxonomy.

Choose the research depth

  • For a supplied post or narrow question, look it up and follow only the context needed to answer. Do not run a broad scan automatically.
  • For a discovery request, start with 2 or 3 distinct queries and small pages. Refine based on noise, coverage and the author's time and spend limits.
  • Prefer saved results and the author's bookmarks before paying to retrieve the same material again. Cached metrics need their capture date.
  • Default to the last 7 days. State the actual window; older research needs an archive route, supplied URLs or browser search.

Retrieval routes, cheapest first:

  1. Browser search or supplied links: no API spend. X's browser search supports engagement operators, for example ("cold email" OR outbound) min_faves:50 -filter:replies plus a date range. Browser and API query dialects differ.
  2. OXYGEN's X connection, if the author has an OXYGEN workspace with X connected: read oxygen.md for discovery, preview, bounded live reads and normalization. It is billed per call; preview first.
  3. Grok or another semantic X search, if the author already uses one: good for discovering conversations, but confirm every cited post at its source before attributing facts. A chat subscription does not include API credits.

For a creator watchlist and a queue of posts to engage with, read engagement.md.

Search, inspect, select

  1. Translate the question into a topic query, a real audience problem, and optionally a known builder or concrete workflow. The seeds in the niche reference are starting points, not a mandatory batch.
  2. Start with original posts. Include replies deliberately when looking for buyer language, objections, failures or implementation detail.
  3. Inspect the actual post, media and relevant thread before explaining why it worked. Follow linked docs, repos or benchmarks for technical claims. A truncated snippet or an AI summary is not the full source.
  4. Deduplicate by post ID. Prioritize audience fit, a substantive mechanism, meaningful discussion and an angle the author can support with evidence.
  5. Compare engagement alongside age and author size. Call a post an outlier relative to that author's normal performance only when a comparable baseline was actually collected. Otherwise say "strong in this sample." Likes, bookmarks, replies and reposts are separate signals. Missing views are unknown; observed zero views are not proof of zero reach.
  6. Separate source facts, hypotheses about performance, and the author's proposed contribution. A popular opinion does not establish algorithm mechanics, product truth, conversion or causality.

Deliver the brief

Give a short recommendation and a sourced shortlist. For each selected item: post URL, author, publication date, observed metrics and capture time, relevance, format or mechanism, and the distinct angle the author could add. Mention sample and window limits. If no strong posts emerged, say so and report useful audience questions.

For content ideas, attach 1 existing pillar and 1 funnel stage, the author's actual receipt or the missing evidence, and a fitting asset. Recommend the strongest few; do not manufacture a quota. Short hooks are optional, not full drafts.

Save requested research under research/<date>-x-<topic>/. Keep raw responses separate from the synthesis, label capture time and query, and keep research distinct from the content board. File durable findings into the context pages through capture-context. Do not edit raw/ sources in place.

When the author also asks for drafts

Research alone does not imply creating or publishing posts. When drafting is requested, hand over to x-copywriter or x-planner with the brief. Preserve the author's supplied wording and proof numbers, including timeframe, units and qualifiers; use only proof from identity/proof.md. No invented drama, generic AI slogans, em dashes or obligatory calls to action.

Do not schedule, publish, reply, DM, follow or like as a side effect of research.

Source

Adapted from rohunvora/x-research-skill at commit 623cca7f41f2fb859bdd01dd7328b7b15da110ed. The research method is retained; its standalone X API client and examples are replaced with browser, OXYGEN and optional semantic-search routes and this kit's author context.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the X Research AI skill do?

Research X posts and conversations in the author's niche and turn the strongest ones into sourced content ideas and angles the author can support with their own evidence. Use for niche trend scans, finding strong posts, creator research or building an X creator watchlist; it produces research briefs, not published posts.

Why use X Research on TypingMind?

Because you install it once and use it with any model. X Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install X Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/timscheuerai/linkedin-funnel/tree/main/skills/x-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use X Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with X Research?

As many as you like. As long as a model supports skills, you can use X Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the X Research AI skill free?

Yes. It is published on GitHub by timscheuerai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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