Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 341 files from 1 736 authors, of which 61 700 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
> Wire a PostHog endpoint into a client app or SDK. Covers fetching the OpenAPI spec, generating a typed client with openapi-generator or @hey-api/openapi-ts, sending the right auth header, shaping the variables payload (HogQL code_name vs insight breakdown property), handling rate-limit and materialised-endpoint error responses. Use when the user says "how do I call my endpoint", "generate a client for this", or "what auth header do I use".
Configures the rollout shape of a PostHog experiment — the variant split (50/50, 80/20, A/B/C ratios), the overall rollout percentage that gates how many users enter the experiment, and the disambiguation when a percentage like "roll out to 25%" could mean either. Use when the user mentions a rollout percentage, variant split, or traffic distribution; gives a ratio like 60/40, 70/30, or 80/20; asks "who sees the test variant?"; wants to increase, decrease, or change the rollout or split on a draft or running experiment; weighs equal vs uneven splits; or proposes a mid-experiment split change (often an anti-pattern that needs reset or end-and-restart).
Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics.\nTRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create.\nDO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.
Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep.\nTRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update.\nDO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).
Diagnoses bias, anomalies, and strange-looking results on a specific PostHog experiment. Covers empty / 0-exposure experiments, sample ratio mismatch, identity fragmentation, multi-variant exposure, uneven-split exclusion bias, significance traps (peeking, A/A, Bayesian vs Frequentist), PostHog-vs-SQL discrepancies, and surprises after mid-run edits. Symptom-driven dispatch to the right diagnostic.\nTRIGGER when: user asks 'is my experiment biased?' or 'why 0 exposures?', references the bias banner, says a variant looks strange / wrong / off, sees significance flipping, notices PostHog numbers disagreeing with their SQL, sees an A/A test showing significance, or reports surprises after mid-run edits.\nDO NOT TRIGGER when: creating a new experiment (use creating-experiments), only configuring rollout (use configuring-experiment-rollout) or metrics (use configuring-experiment-analytics), or only asking lifecycle questions (use managing-experiment-lifecycle).
> Diagnoses CI and pull-request pipeline health for a GitHub repo using the engineering analytics MCP tools — pull-requests (PR list with CI status), workflow-health (per-workflow CI trends), and pr-lifecycle (a single PR's timeline). Use when asked whether CI is getting faster or slower, which GitHub Actions workflow is the slow or flaky long-pole, how long PRs take from open to merge, how an author's merge time compares to the cohort, which open PRs have failing or pending CI, or where a specific pull request is stuck. Triggers on "engineering analytics", "is CI getting slower", "slow workflow", "flaky CI", "time to merge", "cycle time", "PR throughput", "failing checks", "where is PR <n> stuck", "CI long pole", "what's holding up this PR".
> Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.
> Diagnose why a data warehouse sync is failing and recommend the right recovery action. Use when the user asks "why isn't my Stripe/Postgres/Hubspot sync working?", "this table has been stuck for hours", "the data in the warehouse looks wrong", or wants to troubleshoot a specific source or schema. Covers source-level vs schema-level failures, stuck Running states, credential and schema-drift errors, incremental-field misconfig, CDC prerequisite failures, and the cancel / reload / resync / delete-data recovery actions.
> Diagnose why a PostHog endpoint is slow or expensive and propose a concrete fix — bump the cache TTL, enable materialisation, restructure variables, or rewrite the query. Use when the user says "this endpoint is slow", "my endpoint times out", "we're hitting the cost cap on this one", or asks "should I materialise this?". Focuses on a single named endpoint, not a project-wide audit.
> Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, error-rate trends or spikes, latency distributions, trace IDs, or span attributes — not AI observability traces or product logs.
> Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to build a trend or funnel filtered by clicks or other autocapture interactions, asks which properties autocapture sends, or asks how to filter $autocapture events. Only applies to projects using posthog-js autocapture.
Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
> Investigate AI observability evaluations — `hog` (deterministic code-based), `llm_judge` (LLM-prompt-based), and `sentiment` (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific generations, query individual results, and generate AI-powered summaries for boolean pass/fail runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, inspect sentiment classifications, or manage the evaluation lifecycle.
> Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
Find feature flags that were soft-deleted in the active project within a recent time window. Use when the user asks "what flags were deleted in the last N days", "show me recently deleted feature flags", "who deleted flag X", "audit recent flag deletions", or anything similar. Handles the non-obvious gotcha that system.feature_flags exposes the deleted boolean but does not expose a deletion timestamp — the actual deleted-at time lives in the per-flag activity log and must be cross-referenced.
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.
> Consolidate PostHog error tracking issues that are the same actual error reported under different fingerprints. Use when the user asks "why do I have so many TypeError issues that look the same?", "merge these duplicates", "stop splitting this error into new issues", or wants to clean up fingerprint sprawl. Decides between a one-shot merge of existing issues and a durable grouping rule that keeps future events from creating new fingerprints. Does NOT group conceptually similar bugs across different runtimes, SDKs, or call sites.
> Explore PostHog's Inbox and act on what it surfaces — the place where signal reports cluster into actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, wants to act on / fix / implement a report (turn it into a PR), wants to resolve, dismiss, or snooze a report, or wants to see which signal sources are configured. Covers listing, filtering, drilling into, and acting on reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.
> Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error_tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature flag, and what the next step should be. Pulls aggregated metrics, sample exception events, segment breakdowns, linked replays, and synthesizes a hypothesis-grade summary in one pass.
> Investigates a session recording by gathering metadata, person profile, same-session events, and linked error tracking issues in one pass. Use when a user provides a recording or session ID and wants to understand what happened — who the user was, what they did, what errors occurred, and whether there are related error tracking issues. Replaces the manual chain of session-recording-get, persons-retrieve, execute-sql, and query-error-tracking-issues-list.
Guides experiment state transitions: launching, pausing, resuming, freezing/unfreezing exposure, ending, shipping variants, archiving, resetting, duplicating, and copying to another project. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action.\nTRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project, or to freeze/unfreeze exposure (stop enrolling new users while metrics keep flowing, or reopen enrollment).\nDO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).
Inspects URL paths and proposes, tests, orders, and applies project-level path cleaning rules so dynamic segments (numeric IDs, UUIDs, slugs, dates) collapse into readable aliases. Use when the user says "clean the paths", "normalize URLs", "group similar pages", "too many distinct paths", "/users/123 and /users/456 are the same page", "set up path cleaning", or asks why a Web analytics or Paths breakdown is fragmented across thousands of nearly-identical URLs. Covers regex syntax (re2), alias placeholder convention, rule ordering, the test workflow, and applying rules via the path-cleaning-rules-update MCP tool.
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Also the first stop for a governed business number (MRR, activation, revenue): check the semantic layer (canonical metrics in system.information_schema.metrics) for an approved definition before deriving from raw events. Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.
> Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.
> Signals scout for PostHog AI observability. Watches LLM traces for cost, latency, error, volume, and eval-performance regressions, sliced by the dimensions it discovers over time, and files each validated regression as a report in the inbox.
> Signals scout that watches a project's most-viewed dashboards and insights for recent anomalies — bursts, drops, flat-lines, and trend breaks scored against each insight's own report channel (emit_report / edit_report) rather than a weak signal.
> Signals scout for PostHog data pipelines — CDP destinations and transformations, batch exports, and hog flows. Watches for delivery failures, degraded functions, and stalled exports against each pipeline's baseline, and files each validated delivery contradiction as a report in the inbox.
> Signals scout for Content Security Policy violation reports. Watches `$csp_violation` events for blocked-URL clusters, per-directive bursts, post-deploy regressions, and suspicious third-party domains, and files each validated cluster as a report in the inbox.
> Signals scout for PostHog error tracking. Watches `$exception` bursts, stuck loops, multi-fingerprint clusters, and status regressions, and files each validated issue as a report in the inbox.
> Signals scout for PostHog A/B experiments. Watches running experiments for validity threats (sample ratio mismatch, contamination, exposure stalls, mid-run flag mutations) and lifecycle drift (zombies, decided-but-running), and files each validated validity threat as a report in the inbox.
> Signals scout for PostHog feature flags. Watches the flag roster and the `$feature_flag_called` stream for evaluation cliffs, ghost flags, response-distribution shifts, and flag debt, and files each validated contradiction as a report in the inbox.
> Follow-up Signals scout for the inbox itself. After a deployment soak window, re-measures the problems behind recently resolved reports and files a report when a fix didn't hold, plus a gated escalation check on dismissed reports.
> Signals scout over PostHog's own health checks. Reads the project's active health issues, bundles them by kind, weights by blast radius, and files the ones genuinely worth acting on as reports in the inbox.
> Cross-product Signals scout. Looks for cross-product correlations and explores the surfaces the per-product specialist scouts don't cover.
> Signals scout for PostHog logs. Watches for emerging and rate-shifted message patterns (window-over-window deltas), volume bursts, severity-distribution shifts, service silence, and trace-correlated bursts.
> Signals scout for PostHog Replay Vision scanners. Watches that enabled scanners keep observing (throughput / quota cliffs) and that what they see in aggregate gets surfaced (score shifts, recurring themes across sessions), and files each validated finding as a report in the inbox.
> Signals scout for observability gaps — significant event volumes with no insight, dashboard, or alert coverage. Files a report recommending new insights, dashboards, or alerts as the team's product evolves.
> Signals scout for PostHog revenue analytics. Watches for upstream failures (Stripe sync stalls, capture regressions), config drift, and goal-miss escalations, and files each validated finding as a report in the inbox.
> Signals scout for PostHog session replay. Watches that sessions keep recording (capture cliffs) and that friction inside recordings — rage/dead-click clusters, error-after-interaction cohorts — gets surfaced, and files each validated cliff or cluster as a report in the inbox.
> Signals scout for PostHog surveys. Watches active surveys for score regressions, response-volume drops, abandonment spikes, and targeting drift, and aggregates open-text responses into recurring themes — filing each as a report in the inbox.
> Signals scout for PostHog web traffic. Watches per-channel session volume, attribution breakage, and landing-page health (bounce / 404 steps) against the site's own baseline, and files each validated divergence as a report in the inbox. Per-page web vitals have their own dedicated `signals-scout-web-vitals`.
>- Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".
Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, ad spend, production database tables, or other data PostHog does not collect natively — or wants to join or correlate PostHog product events with that external business data. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, Zendesk, etc.), ad platforms, production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.
> Create PostHog error tracking suppression rules to drop high-volume, low-value errors at ingestion. Use when the user asks "stop capturing this error", "drop browser extension errors", "ignore ResizeObserver loops", "suppress bot-driven errors", or wants to reduce ingestion cost from noisy unactionable errors. Identifies suppression candidates, scopes the filter tightly, decides between full suppression and sampling, and confirms the rule before creating it. Suppressed errors are dropped permanently — this skill defaults to caution.
> Triage PostHog error tracking issues during a daily or on-call review. Use when the user asks "what's broken?", "what new errors do we have?", "show me top errors today", "what should I look at this morning", or wants a prioritized list of active issues to work on. Surfaces new and high-impact issues, ranks by users affected and recency, points at linked replays, and proposes next actions (investigate, assign, suppress, merge).
>- Best practices for agents managing PostHog skills via the MCP `skill-*` tools — how to discover, read, create, update, and refactor skills efficiently, especially large skills with many bundled files. Use whenever you are about to call any `skill-*` tool, asked to author or edit a shared skill, or troubleshoot why a skill write was rejected. Pairs with `skills-store` (which covers the raw tool surface) by adding the decision-tree, efficiency, and pitfall guidance.
Claude Skills meta-skill: extract domain material (docs/APIs/code/specs) into a reusable Skill (SKILL.md + references/scripts/assets), and refactor existing Skills for clarity, activation reliability, and quality gates.
Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
Create SEO-optimized marketing content with consistent brand voice. Includes brand voice analyzer, SEO optimizer, content frameworks, and social media templates. Use when writing blog posts, creating social media content, analyzing brand voice, optimizing SEO, planning content calendars, or when user mentions content creation, brand voice, SEO optimization, social media marketing, or content strategy.
Strategic product leadership toolkit for Head of Product including OKR cascade generation, market analysis, vision setting, and team scaling. Use for strategic planning, goal alignment, competitive analysis, and organizational design.
Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flutter, Postgres, GraphQL, Go, Python. Includes architecture diagram generation, system design patterns, tech stack decision frameworks, and dependency analysis. Use when designing system architecture, making technical decisions, creating architecture diagrams, evaluating trade-offs, or defining integration patterns.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
Comprehensive fullstack development skill for building complete web applications with React, Next.js, Node.js, GraphQL, and PostgreSQL. Includes project scaffolding, code quality analysis, architecture patterns, and complete tech stack guidance. Use when building new projects, analyzing code quality, implementing design patterns, or setting up development workflows.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.
Claude Skills — questions
Answers built from the skills we actually parsed.