agentleFS
patinaprojectpatinaproject/skills/.agents/skills/finding-experiments/SKILL.md

finding-experiments

Skill1 starsChanged 20 days agoMITView on GitHub

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.

2 other projects use a version of this, 2 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

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

    line 13
  2. Distinctive:

    Exactly one match: Use it. Confirm with the user by name before destructive actions (delete, ship, end).

    line 29
  3. Very specific:

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

    line 35

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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: match the feature_flag_key field

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

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-results — investigate the experiment you found

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.