Auditing Experiments Flags
PostHogOfficialPublished by PostHog
- Skill
- Review and audit
- Data and AI
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to auditing PostHog experiments and feature flags. It enables users to perform quick checks on individual entities, scoped audits of specific domains, or comprehensive full-project audits. The skill uses defined check taxonomies to identify configuration errors, waste, and process violations, providing clear remediation actions for each finding.
What it does
- Performs configuration audits on PostHog experiments and feature flags.
- Identifies critical correctness issues, potential waste, and process violations.
- Supports single-entity checks, scoped domain audits, and full-project reviews.
- Categorizes findings by severity (CRITICAL, WARNING, INFO) and domain.
- Provides actionable remediation steps for every identified issue.
- Handles data gaps gracefully by noting skipped checks when activity logs are unavailable.
When to use it
- When you need to verify the health and setup of a specific experiment or feature flag.
- When performing routine hygiene checks to identify stale or misconfigured flags.
- When preparing for a launch to ensure experiment metrics and flag rollouts are correctly configured.
- When cleaning up project resources to reduce technical debt and waste.
Advantages
- Standardizes the auditing process across different experiments and flags.
- Prioritizes findings by severity to help focus on the most impactful issues first.
- Provides clear, actionable guidance for resolving configuration problems.
- Maintains consistency in reporting through a structured output format.
Usage examples
- “Audit the experiment with ID 42.”
- The agent fetches the experiment, runs all relevant configuration checks, and reports findings grouped by severity with actionable remediation steps.
- “Perform a health check on all active feature flags.”
- The agent retrieves all flags, evaluates them against best-practice criteria, and provides a summary report of any issues found.
- “Run a comprehensive audit of all experiments and flags in this project.”
- The agent performs a full audit, synthesizes recurring patterns across the project, and presents the findings in a structured report or notebook.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/posthog/auditing-experiments-flags/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip auditing-experiments-flags.zip -d .agents/skills
Documentation by PostHog
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:
Fetch the entity via
experiment-get(experiment ID) orfeature-flag-get-definition(numeric flag ID).Apply the relevant checks from experiment checks or flag checks.
Report findings inline as markdown, grouped by severity (CRITICAL first, then WARNING, then INFO).
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:
Bulk-fetch via
experiment-listorfeature-flag-get-all.Run all checks for that domain against each entity.
Group findings by severity, then by entity.
Report as inline markdown.
Full audit (comprehensive)
When the user asks for a comprehensive audit of both experiments and flags:
Fetch all experiments via
experiment-listand all flags viafeature-flag-get-all.Run all experiment checks and all flag checks.
Apply recurring patterns to identify patterns across multiple findings.
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🔵 INFOCheck 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
Experiment checks — experiment configuration checks
Flag checks — feature flag checks
Finding types — severity and category definitions
Recurring patterns — patterns across multiple findings
Remediation actions — what to do about each finding
Files
- ATTRIBUTION.mdAdded by AutoGineer871 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md3.8 KiB
- references/experiment-checks.md8.6 KiB
- references/finding-taxonomy.md2.6 KiB
- references/flag-checks.md5.4 KiB
- references/remediation-actions.md5.2 KiB
- references/synthesis-patterns.md1.5 KiB
references/remediation-actions.md
Remediation actions
For each finding type, recommend the appropriate action. Phase 1 is read-only — all actions require the user to make changes manually.
Experiment actions
| Finding | Action |
|---|---|
| No metrics configured | Add at least one primary metric before launching. Link to the experiment's metrics tab. |
| Secondary metrics only | Promote one metric to primary or add a new primary metric. |
| Missing feature flag | Create and link a feature flag to this experiment. |
| Inactive (paused) flag | Re-enable the linked feature flag, or end the experiment if it's no longer needed. |
| Deleted flag | The experiment's flag was deleted. Create a new flag and re-link, or archive the experiment. |
| Incomplete variant split | Adjust the variant rollout percentages on the linked flag so they total 100%. |
| Too few variants | Add a test variant to the linked flag, so it has a baseline and at least one variant to compare. |
| Conclusion contradicts shipped variant | Review the experiment conclusion and the flag's current state. Either update the conclusion or change the flag to match. |
| Concluded but still splitting | The experiment has a conclusion but the flag is still splitting traffic. Roll out the winning variant or disable the flag. |
| Stale draft | This experiment has been in draft for over 7 days. Either launch it or delete it. |
| No hypothesis | Add a hypothesis to document what you expect to learn. |
| Stopped with active flag | The experiment has ended but its flag is still active. Roll out the winning variant or disable the flag. |
| Running less than 7 days | Wait for at least 7 days of data before drawing conclusions. |
| Long-running experiment (>30 days) | Review whether this experiment still needs to run. Consider concluding it or adjusting the timeline. |
Flag actions
| Finding | Action |
|---|---|
| Fully rolled out (100%, no conditions) | This flag always evaluates to the same value. Remove it from code and hardcode the value. |
| Stale by usage (not called in 30+ days) | This flag isn't being evaluated. Remove it from code or investigate why it's not being called. |
| Stale draft (inactive, 30+ days old) | This flag was created but never activated. Delete it or activate it. |
| Orphaned experiment flag | All linked experiments are completed. Roll out the winning variant or disable the flag. |
| Variant sum != 100% | The multivariate rollout percentages don't add up to 100%. Adjust the variant percentages. |
| Dead variant (0% rollout) | A variant has 0% rollout on a non-experiment flag. Either give it traffic or remove it. |
| Dead condition (0% rollout) | A release condition has 0% rollout. Either increase it or remove the condition. |
| Manual rollout on experiment flag | An experiment flag has manual rollout overrides. This can invalidate experiment results. Remove manual overrides and let the experiment control the split. |
| Toggle instability (>3 toggles) | This flag has been toggled on/off many times. Consider whether the flag is being used as intended. |
| High config churn | This flag is being modified very frequently. Consider stabilizing the configuration. |
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- PostHog
- Publisher
- PostHog (Official)
- Source
- PostHog/skills · skills/omnibus/auditing-experiments-flags · commit a5d30e7
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by PostHog or PostHog.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download auditing-experiments-flags.zip