yigitkonur

Audit macOS SwiftUI accessibility for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI animation motion for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI api currency for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI appearance color for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI appkit interop for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI appkit overuse for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI async data for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Audit macOS SwiftUI availability gating for correctness, current APIs, and production conventions. Use for that domain or as part of a full audit.

Use skill if you are exhaustively testing or release-gating martool CLI commands in a source checkout or deployed Coolify container over SSH, without local Docker or provider spend.

Use if driving agent-browser for Chrome/CDP automation, @ref snapshots, tabs, or verification.

Use if testing or debugging an iOS app via agent-device CLI — simulator flows, evidence, bug triage.

Use if supervising Jean agents through MCP and Computer Use for monitoring, recovery, or closure.

Use if auditing or designing a CLI for agent/LLM use — JSON output, exit codes, non-interactive.

Use if auditing or designing an MCP server for agent-readiness — framework, security, context.

Use if verifying claimed-done work or auditing session/plan/branch completion with evidence.

Use if hardening a SKILL.md by running a fresh subagent on a real task and fixing where it snags.

Delegate substantial implementation, refactor, debug, and review work to OpenAI Codex through a hook-driven runtime. Use when the user says "have Codex…", "run this by Codex", asks for an adversarial review, or wants a plan→execute→review→merge loop. The plugin's hooks intercept native Agent spawns (Explore-class), auto-arm Monitor on background dispatches, isolate write-mode tasks in a per-task git worktree, and surface running-job state into context — so the orchestrator rarely has to teach itself how to drive the bridge.

Delegate substantial implementation, refactor, debug, and review work to OpenAI Codex through a hook-driven runtime. Use when the user says "have Codex…", "run this by Codex", asks for an adversarial review, or wants a plan→execute→review→merge loop. The bridge returns Monitor-ready event envelopes and keeps Codex execution separate from the orchestrator context.

Use skill when verifying, smoke-testing, or describing the live mcp-researchpowerpack server (https://research-mcp.yigitkonur.com/mcp) — the exact 3-tool surface, post-deploy contract check, schema/health/SSRF/Reddit/PDF acceptance, or any "does the deploy actually expose X" question. Query the live server with mcpc; never describe its tools from memory.

Use skill when adding, changing, or extending mcp-researchpowerpack server code — a tool/schema/resource, an Effect service or client, an env var, or a deploy — or when reviewing such a change. Encodes this repo's build mechanics + invariants (Effect boundary, never-throw handlers, strict Zod, SSRF) and the deploy/verify realities that bite here (manual version, restart-not-deploy, CI-only local verification, wire-level schema tolerance).

Use skill if you are listing, reading, searching, resuming, or pruning AI coding sessions via the sessionr CLI — `sessionr list/read/search/info/stats/doctor/send/job/wait/cancel/context/diff/tag/prune` across claude, codex, gemini, copilot, cursor-agent, commandcode, goose, opencode, kiro, zed, factory/droid sources. Triggers on "find sessions", "what did Claude do yesterday", "resume session abc123", "session of another agent", a UUID-shaped session id pasted in chat, npx sessionr, or any inspect/replay/handoff/cleanup of an AI coding-tool transcript.

Arm a background watcher on CI/CD after any push or deploy trigger — GitHub Actions runs, EAS builds, deploys. MUST be used whenever you push a commit that triggers CI, re-run a workflow, or kick off any build/deploy you need to verify. Never watch CI with foreground Bash or `gh run watch`/`gh pr checks --watch`.

This skill should be used whenever the user asks to inspect, query, debug, audit, or operate PostHog from the terminal: "check our PostHog data", "what are users doing", "investigate this metric", "run a HogQL query", "list feature flags", "find a session replay", "check errors", "manage an endpoint", "upload source maps/dSYMs/ProGuard mappings", "symbolicate this crash", "audit PostHog setup", "is analytics ingestion working", or mentions `posthog-cli`, a PostHog project URL, event names, funnels, trends, retention, persons, recordings, releases, or symbol sets. Prefer this over guessing about product behavior: PostHog contains the real events, identities, sessions, flags, experiments, errors, and release metadata.

Investigate production errors, crashes, stacktraces, logs, performance, and session replays through the Sentry CLI (the new `sentry` binary — NOT legacy `sentry-cli`). Reach for this whenever you need ground truth about what is failing in production: triaging or debugging a real error/crash/exception, pulling a stacktrace or root cause for a reported bug, checking what broke after a deploy, inspecting production logs, finding slow endpoints/transactions, watching a live error stream, confirming a fix actually stopped an error, or whenever the user mentions Sentry, an issue short-id (e.g. PROJ-1234, API-7), a sentry.io URL, "check prod errors", "what's erroring", "why is X crashing/timing out in prod", "is the deploy healthy". Strongly prefer this over guessing about runtime failures you cannot reproduce locally — Sentry holds the real stacktrace, tags, breadcrumbs, frequency, and AI root-cause analysis.

Fixture Claude skill for zero-setup discovery.