Code Review
Automated code review and analysis
Uses and selection guide
Use these skills to move from a reproducible issue or change request to a reviewable patch. Match the language, framework and test runner to your repository; compare the proposed diff and run your own checks before accepting edits.
pr-reviewer
by SpillwaveSolutions
Comprehensive GitHub Pull Request code review skill. Use when asked to "review this PR", "code review", "review pull request", "check this PR", or when a GitHub PR URL is provided. Fetches PR metadata, diff, comments, commits, and related issues using gh CLI. Creates organized review workspace, analyzes code against industry-standard criteria, and optionally adds inline comments to the PR.
isolating-product-facade-contracts
by PostHog
Plan and execute product isolation migrations to a facade plus contract layer in PostHog, following the Visual review architecture. Use when a product still exposes internals (models/logic/views) across boundaries and needs migration toward contracts.py + facade/api.py + presentation separation, with a PR strategy that minimizes review latency and conflicts with parallel work.
reviewing-personhog-protocol
by PostHog
The full review process for personhog coordination-protocol changes — leases, fencing, handoffs, supervisors, failure budgets, warming, and changelog semantics. Use before pushing or requesting review on any personhog protocol changeset, when asked for an exhaustive or careful review of personhog code, and after any reviewer finds a gap the author missed. Covers the adversarial two-pass process, the review lens dimensions, the model-checking and test layers a change must clear, and the red-check-every-fix discipline.
implementing-agent-modes
by PostHog
Guidelines to create/update a new mode for PostHog AI agent. Modes are a way to limit what tools, prompts, and prompt injections are applied and under what conditions. Achieve better results using your plan mode.
writing-dataclasses
by PostHog
House rules for Python dataclasses in PostHog: when to reach for one instead of a tuple or dict[str, Any], which decorator to use (@frozen from posthog.dataclasses), how to name, construct, consume and evolve them, how to keep secrets out of repr, and when a function should accept a dataclass instead of its unpacked fields. Use when adding or changing a dataclass, returning or passing several values from a function, converting a tuple or dict payload, deciding frozen=/slots=/kw_only=, or passing a facade contract DTO through internal layers. Not for pydantic models used as HogQL/query schema, DRF serializers, or Django models.
exploring-llm-traces
by PostHog
Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against events.properties.$ai_input / $ai_output_choices returns empty — message content lives only on the dedicated posthog.ai_events table.
authoring-ci-workflows
by PostHog
Use when adding or editing a GitHub Actions workflow, composite action, or reusable workflow under .github/ — new CI jobs, triggers, matrices, checkout/clone tuning, action pinning, GitHub App token auth, concurrency groups, timeout-minutes, paths filters, caching, or runner choice. Covers PostHog's workflow-authoring conventions and the reasons behind them: the 500-runs/10s dispatch cap, shallow vs full clone, per-SHA push concurrency, dedicated App-token rate-limit buckets, and fork-safe secrets on a public repo. Points to the linters (bin/hogli lint:workflows, actionlint) that enforce the mechanical rules, and to the narrower skills for production deploys, secrets, and Depot runners. Not for debugging red CI (use debugging-ci-failures) or wiring a new secret end to end (use managing-github-actions-secrets).
exploring-llm-clusters
by PostHog
'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.'
django-startup-time
by PostHog
Keep heavy imports off the django.setup() path that every process (web, celery, temporal, migrate, shell, CI) pays for. Use when touching AppConfig.ready(), wiring signal receivers, editing the lazy API router (posthog/api/rest_router.py or its init.py shim), deferring a heavy import, when the startup-import-budget guard fails, or when merging master into a long-lived branch that made the router lazy.
finding-llm-gateway-migration-candidates
by PostHog
Finds and ranks callers that could move from services/llm-gateway to PostHog/ai-gateway. Use when asked what to migrate next, to find low-risk gateway migration candidates, to audit remaining Python gateway callers, or to identify callers blocked by Go gateway parity. Searches code and deployment wiring, inventories each caller's required contract, filters out unsupported migrations, and returns an evidence-backed shortlist without changing callers.
adding-mcp-store-servers
by PostHog
Add a third-party MCP server (Linear, Notion, GitHub, ...) to the PostHog MCP store catalog. Use when asked to "add X to the MCP store", expand the MCP server marketplace, or fix a broken catalog entry. Covers finding the vendor's remote MCP endpoint, probing it (handshake, OAuth discovery, DCR), authoring the catalog entry in products/mcp_store/backend/catalog.py, verification tiers, and the operator handoff for servers without Dynamic Client Registration.
adding-inbox-sources
by PostHog
Add a new warehouse-backed source to the PostHog Desktop Self-driving Inbox (the feature that ships GitHub, Linear, Zendesk, pganalyze, Jira). A source syncs one warehouse table (issues/tickets/conversations) and a cloud "signals scout" watches it and emits findings. Use when asked to "add a new inbox/self-driving source", "wire up <Jira/GitLab/Sentry/Intercom/Freshdesk/Front/Gorgias/etc> as a signal source", or to extend the source-toggle grid. Covers all three surfaces (posthog/posthog scout emitter + posthog/code UI wiring + the context-mill self-driving wizard skill that offers the source in npx @posthog/wizard self-driving), the deploy ordering between them, and created_via attribution.
auditing-llm-gateway-parity
by PostHog
Audits services/llm-gateway against PostHog/ai-gateway and updates services/llm-gateway/PARITY.md from current implementation evidence. Use when either gateway changes auth, attribution, billing, endpoints, providers, models, routing, or metadata; when reviewing a Python gateway change; or when asked to refresh, verify, or report gateway parity. This skill updates the parity record but does not migrate callers.
running-ci-preflight
by PostHog
Catch the deterministic CI failures reachable from your diff before pushing, with hogli ci:preflight. Use when the pre-push hook blocks a push, before reporting a task done, or after editing Python, serializers, migrations, workflows, or dependency manifests — to avoid burning a CI matrix on a failure you could catch locally (formatting, lint, broken lockfiles, OpenAPI drift, migration conflict, stale branch). Trigger terms: ci:preflight, preflight, pre-push checks, pre-push hook failed, "will this break CI".
extending-hobby-smoke-tests
by PostHog
Design, extend, review, or debug PostHog Hobby end-to-end smoke tests in bin/hobby-ci.py and .github/workflows/ci-hobby.yml. Use when adding an ingestion round trip, deciding whether a product belongs in Hobby CI, changing the CI Hobby service topology or API-key scopes, or diagnosing a smoke test that captures data but cannot query it.
writing-pr-descriptions
by PostHog
Shapes a PR body into something a reviewer understands at a glance. Use ALWAYS before writing or editing a PR description, before gh pr create or gh pr edit --body, and when asked to improve an existing description. Puts the effect a person sees in the first line and the mechanism under it, routes each remaining fact to the form that carries it fastest (bullet, table, diagram, screenshot, collapsed block), cuts everything a reviewer does not need, then holds what survives to a checkable shape: one fact per bullet, sentences under 25 words, active voice, no idioms. Makes the body stand alone, so a reader who opens no files still knows why the PR is necessary and what it does, sizes the body to the change so a small PR reads as small, and makes every claim either linked to its evidence or labeled as unchecked. Ends with a scan test over the title and the first lines of Problem and Changes. Not for commit messages (see AGENTS.md, "Commit types") or user-facing product copy (see /writing-user-facing-copy).
analyze-market
by langchain-ai
Perform a market analysis for a product category or segment. Trigger on: market analysis, market size, TAM SAM SOM, market opportunity, industry analysis.
planning
by langchain-ai
Break down a coding task into a structured implementation plan with clear steps, file identification, and risk assessment.
competitor-analysis
by langchain-ai
Analyze competitors in a given market segment. Trigger on: competitive landscape, competitor analysis, market comparison, competitive positioning.
code-review
by langchain-ai
Perform a structured code review of changes, checking for correctness, style, tests, and potential issues.
workflows:work
by brycewang-stanford
Execute research implementation plans efficiently while maintaining estimation quality and finishing features
deploy
by brycewang-stanford
Render Quarto slides and sync to docs/ for GitHub Pages deployment. Use when deploying lecture slides after making changes.
slide-excellence
by brycewang-stanford
Multi-agent slide review (visual, pedagogy, proofreading). Use for comprehensive quality check before milestones.
scientific-critical-thinking
by brycewang-stanford
"Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims."