Auditing Experiments Flags logo

Auditing Experiments Flags

Organization
PostHog
auditing-experiments-flags

Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.

Overview

PublisherPostHog
Repositoryskills
Skill nameauditing-experiments-flags
Stars
77
Forks
8
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 PostHog on GitHub. Read the source before you install it.

Installation

Install the Auditing Experiments Flags 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/auditing-experiments-flags
  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/auditing-experiments-flags .claude/skills/auditing-experiments-flags
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auditing Experiments Flags 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 Auditing Experiments Flags 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 Auditing Experiments Flags 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.

Auditing experiments and feature flags

This skill teaches you how to run configuration audits on experiments and feature flags. All checks use the experiment and feature flag read tools (experiment-get, experiment-list, feature-flag-get-definition, feature-flag-get-all) — no SQL queries are needed for Phase 1 checks.

Usage modes

Quick check (single entity)

When the user asks about a specific experiment or flag:

  1. Fetch the entity via experiment-get (experiment ID) or feature-flag-get-definition (numeric flag ID).
  2. Apply the relevant checks from experiment checks or flag checks.
  3. Report findings inline as markdown, grouped by severity (CRITICAL first, then WARNING, then INFO).
  4. Include entity links as [Experiment: name](/experiments/id) or [Flag: key](/feature_flags/id).

Scoped audit (one domain)

When the user asks to audit all experiments or all flags:

  1. Bulk-fetch via experiment-list or feature-flag-get-all.
  2. Run all checks for that domain against each entity.
  3. Group findings by severity, then by entity.
  4. Report as inline markdown.

Full audit (comprehensive)

When the user asks for a comprehensive audit of both experiments and flags:

  1. Fetch all experiments via experiment-list and all flags via feature-flag-get-all.
  2. Run all experiment checks and all flag checks.
  3. Apply recurring patterns to identify patterns across multiple findings.
  4. If there are more than 5 entities with findings, write them to a notebook for easier navigation. Otherwise report inline. Create the notebook from the project's own notebook tools. Run search notebooks?- to load them and read the titles.

Output format

For each finding, include:

  • Severity badge: 🔴 CRITICAL, 🟡 WARNING, or 🔵 INFO
  • Check name: Which check produced this finding
  • Entity link: Markdown link to the entity
  • What's wrong: One-sentence description
  • Action: What to do about it (see remediation actions)

Example:

🟡 WARNING — Flag integration · Experiment: checkout-redesign The linked feature flag is inactive (paused). Traffic is not being split. Action: Re-enable the flag or end the experiment.

Handling unavailable data

Some checks require activity logs (feature-flags-activity-retrieve for flags), which may not be available in every session. If activity log data is unavailable:

  • Skip checkActivityHistory (experiment check) entirely.
  • Skip the "toggle instability" and "never activated" sub-checks in flag lifecycle checks.
  • In your report, note which checks were skipped and why:

    Skipped: Activity history checks (activity logs not available via current tools)

Partial failures

If a fetch call fails for some entities:

  • Continue with the entities you could fetch.
  • Report which entities could not be assessed and why.
  • Do not silently omit entities from the audit.

Reference files

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 Auditing Experiments Flags AI skill do?

Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.

Why use Auditing Experiments Flags on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PostHog/skills/tree/main/skills/omnibus/auditing-experiments-flags. 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 Auditing Experiments Flags?

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 Auditing Experiments Flags?

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

Is the Auditing Experiments Flags 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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