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Finding Experiments

Organization
PostHog
finding-experiments

Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment ("split test demo", "the File engagement boost experiment", "onboarding retention test", "landing page hero experiment", "pricing experiment"), describes it loosely ("the signup experiment", "my pricing test", "the one with the new checkout"), uses a relative reference ("latest", "most recent", "the one I created yesterday"), filters by status (running, draft, paused, exposure frozen, stopped, archived), or otherwise refers to an experiment by anything other than its concrete ID.

Overview

PublisherPostHog
Repositoryskills
Skill namefinding-experiments
Stars
77
Forks
8
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Finding Experiments 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/PostHog/skills/tree/main/skills/omnibus/finding-experiments
  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/PostHog/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/omnibus/finding-experiments .claude/skills/finding-experiments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Experiments 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 Finding Experiments 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 Finding Experiments 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.

Finding experiments

Users refer to experiments by name, description, or relative references — not by ID. This skill resolves natural language references to concrete experiment IDs.

How to find an experiment

Use the experiment-list tool from the Posthog-local MCP server.

IMPORTANT: Do NOT use feature-flag-get-all or any feature flag tool to find experiments. Use the dedicated experiment list tool: experiment-list.

This tool returns experiments with their id, name, status, feature_flag_key, start_date, end_date, and created_at. Browse the returned list to find the experiment matching the user's reference:

  • By name: scan the name field for matches
  • By recency: results are ordered newest first by default
  • By status: match the status field (draft, running, paused, exposure_frozen, stopped)
  • By flag key: skip the list and call experiment-get-by-flag-key with the key. It returns the full experiment, or found: false with a reason. no_flag and no_experiment mean the key is free, so this is also the existence check to run before experiment-create. ambiguous means several experiments share the flag and none is the single live one: show the candidates and pick one with experiment-get rather than creating another.
  • By partial flag key: call experiment-list with search set to the fragment. search matches substrings of the experiment name and the flag key, ignoring case.

After finding matches

  • Exactly one match: Use it. Confirm with the user by name before destructive actions (delete, ship, end).
  • Multiple matches: List them with name, status, and creation date. Ask the user to pick.
  • No matches: Tell the user. Suggest checking archived experiments or different terms.

Get full details if needed

After resolving to an ID, call experiment-get for the full object (metrics, flag details, parameters).

Examples

text
User: "pause my signup experiment"

Agent:
1. Calls experiment-list
2. Scans results, finds "New signup process" (ID: 1371, status: running)
3. Proceeds to pause experiment 1371

When NOT to search

  • You already have the experiment ID from earlier in the conversation
  • The user just created the experiment — you have the ID from the create response
  • The user provided the ID directly

Related skills

  • managing-experiment-lifecycle — act on the experiment once you've resolved its ID
  • diagnosing-experiment-health — investigate the experiment you found

Frequently asked questions

What does the Finding Experiments AI skill do?

Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment ("split test demo", "the File engagement boost experiment", "onboarding retention test", "landing page hero experiment", "pricing experiment"), describes it loosely ("the signup experiment", "my pricing test", "the one with the new checkout"), uses a relative reference ("latest", "most recent", "the one I created yesterday"), filters by status (run...

Why use Finding Experiments on TypingMind?

Because you install it once and use it with any model. Finding Experiments 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 Finding Experiments in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PostHog/skills/tree/main/skills/omnibus/finding-experiments. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finding Experiments?

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 Finding Experiments?

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

Is the Finding Experiments AI skill free?

Yes. It is published on GitHub by PostHog 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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