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.
Works with
--- name: auditing-experiments-flags description: 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. license: MIT --- # 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](./references/experiment-checks.md) or [flag checks](./references/flag-checks.md). 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](./references/synthesis-patterns.md) to identify patterns across multiple findings. 4. If there are more than 5 entities with findings, output as a notebook artifact via `notebooks-create` for easier navigation. Otherwise report inline. ## 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](./references/remediation-actions.md)) Example: > 🟡 **WARNING** — Flag integration · [Experiment: checkout-redesign](/experiments/42) > 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](./references/experiment-checks.md) — experiment configuration checks - [Flag checks](./references/flag-checks.md) — feature flag checks - [Finding types](./references/finding-taxonomy.md) — severity and category definitions - [Recurring patterns](./references/synthesis-patterns.md) — patterns across multiple findings - [Remediation actions](./references/remediation-actions.md) — what to do about each finding
More Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

