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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).

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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".

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Inspects and visualises a ROS-2-enabled CARLA server from Docker containers, with no local ROS 2 install — list/echo/rate-check topics (the "are the topics actually on the wire" check), run the bundled map-and-lidar demo stack (hero vehicle with camera/lidar/GNSS/IMU on autopilot), and open RViz2 with the lane-network preset. Use when the user asks to "check the ROS topics", "echo /carla/map", "is CARLA publishing to ROS", "visualise the lidar in RViz", "open rviz", or "run the ROS 2 demo".

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Installs CARLA's ScenarioRunner (the official scenario engine) and picks the branch that matches the CARLA in use — master for 0.9.14-0.9.16 (UE4), ue5-master for 0.10.0 (UE5), leaderboard-1.0/2.0/2.1 when driving the Leaderboard. Clones or checks out the branch, installs requirements, wires PYTHONPATH (including the `agents` package the carla wheel does not ship), and verifies the import. Use when the user asks to "install scenario runner", "set up SR", "run CARLA scenarios", "use OpenSCENARIO", or has a scenario_runner that fails to import.

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Downloads CARLA so nothing has to be fetched by hand — an official release package for any published version (resolved live from the GitHub releases, with AdditionalMaps), the rolling nightly Dev build, a shallow source checkout of a branch or tag, or the carlasim/carla Docker image. Reports exactly what was obtained and where, plus the environment variables the other skills read. Use when the user asks to "download CARLA", "get CARLA 0.9.16", "install CARLA", "clone the carla repo", "fetch the latest CARLA", or has no CARLA at all yet.

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Runs a route through ScenarioRunner's RouteScenario mode (--route / --route-id) with an autonomous agent driving the ego — the same mechanism the CARLA Leaderboard uses, and the only way to reach the ~34 route-only scenario classes (Accident, ParkedObstacle, HazardAtSideLane, InvadingTurn, HardBreakRoute, YieldToEmergencyVehicle, junction and actor-flow scenarios). Inspects route files, lists route ids and their scenarios, and runs one or all of them. Use when the user asks to "run a route", "drive a route with my agent", "run leaderboard scenarios without the leaderboard", or names a route-only scenario type.

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Installs the CARLA Autonomous Driving Leaderboard (leaderboard.carla.org) at a chosen version — 1.0, 2.0 or 2.1 — together with the exact scenario_runner branch and CARLA build each one requires, then wires the four-root PYTHONPATH and verifies the whole stack imports. Detects which version an existing checkout is by reading its code, not its branch name. Use when the user asks to "install the leaderboard", "set up the CARLA challenge", "evaluate my driving agent", "reproduce my leaderboard score", or names a leaderboard version.

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Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.

日本語の概要は準備中です。原文の説明を表示しています。

Listens to a CARLA sensor and either saves its data to files, shows it live in a window, prints a one-shot summary, or (ros-info) reports the native ROS 2 topics, QoS and enabled-for-ROS state so you can echo it from ROS instead. Cameras save as PNG (depth/semantic auto-colourised) and display in a pygame window; lidar saves as .ply and shows as a top-down scatter; IMU/GNSS/radar/collision stream to JSONL or the console. Use when the user asks to "show/view the camera", "display the lidar", "save the sensor data / capture a dataset", or "what is this sensor reading". Select the sensor by id, type, or the actor it's attached to.

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Installs the CARLA Python client (the `carla` package) into a chosen interpreter so the other python-api skills work — from a wheel bundled with your release or checkout, from PyPI (`pip install carla==X.Y.Z`), or by putting an older release's .egg on PYTHONPATH. Detects what is already present, picks the source that matches your simulator's version, pins numpy<2, and verifies `import carla` plus the client/server version match. Use when the user asks to "install the CARLA Python API", "pip install carla", "set up the carla python package", or when a skill reports "cannot import carla".

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Writes an autonomous agent for the CARLA Leaderboard — an AutonomousAgent subclass with setup/sensors/run_step/destroy, a sensor suite inside the track's budget, and the right constructor signature for the leaderboard version (1.0 and 2.x differ). Generates a working skeleton, validates the sensor configuration against the real per-track limits offline, and covers the ROS1/ROS2 agent base classes. Use when the user asks to "write a leaderboard agent", "make my agent work with the leaderboard", "add sensors to my agent", "port my agent to 2.1", or hits SensorConfigurationInvalid.

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Prepare for a Japanese job interview by working from the documents already submitted: find where an interviewer will dig in (claims the applicant cannot back up, work described in the first person plural, outcomes with no measure, gaps or overlaps in the employment history, must-have requirements the documents never answer), and turn them into questions to prepare evidence for. Use when a user has an interview scheduled and wants to know what will be asked and what to have ready, or wants to prepare reverse questions from earlier company research; do not use to predict whether they will pass, to write answers containing experience they did not report, or to schedule or contact anyone.

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INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by breakdown-issues. It formats a scope GitHub Issue body (label cc-triage-scope, used before code analysis), runs the pre-posting checklist, and executes `gh issue create`. Invoke this skill ONLY from one of the parent skills via the Skill tool, after the parent has finalized the task breakdown. If a user asks to 'create a scope issue' or similar, route them to the parent skill (/breakdown-issues) rather than invoking this one directly.

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Query the Lyrion Music Server over its JSON-RPC API from the Flask app. Use this whenever a task needs live data from Lyrion — the now-playing track, player list, transport state, album art, or any `slim.request` command — or when adding a feature that reads from the music server. Covers the request format, the tag system, the shared cache, and the self-signed-TLS rules, so new calls match how `services/lyrion.py` already talks to the server.

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Write or fix a test in the Lyrion Dashboard's suite. Use this whenever a task adds a test, changes one, or asks why the suite fails — anything touching `tests/`. Covers the standalone-per-file scaffolding, the env-before-import rule, where to patch (the route/service the code under test imports), the cache-and-clock testing pattern, and how to run one test vs the whole suite. Reach for it any time you touch `tests/` so new tests match the existing shape instead of re-inventing setup.

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Live developer experience audit. Uses the browse tool to actually TEST the developer experience: navigates docs, tries the getting started flow, times TTHW, screenshots error messages, evaluates CLI help text. Produces a DX scorecard with evidence. Compares against /plan-devex-review scores if they exist (the boomerang: plan said 3 minutes, reality says 8). Use when asked to "test the DX", "DX audit", "developer experience test", or "try the onboarding". Proactively suggest after shipping a developer-facing feature. (gstack) Voice triggers (speech-to-text aliases): "dx audit", "test the developer experience", "try the onboarding", "developer experience test".

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Capture actionable learnings that emerge during conversation — when the agent or user discovers that a skill, a set of skills, or a process needs to be updated based on what's happening in the current chat. Sub-skill of the learn-from orchestrator. Load when the user says "we should update the skill for this", "this should be a skill rule", "add this as a gotcha", "the skill should know about this", "update the process for this", "remember this for next time", "this is important for the skill". Also triggers when the agent notices a skill's guidance was wrong or incomplete, a process step failed or was unnecessary, a new pattern emerged, a guardrail was missing, a workaround became a pattern, or a debugging session reveals a gap.

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Install Port Daddy's bounded interactive coordination hooks ("tentacles", from the Giant Squid Harness) into the INTERACTIVE sessions of agentic coding CLIs (Claude Code, Codex, Gemini, Antigravity/agy) by auto-detecting which CLIs are installed and writing PER-PROJECT hook config behind a runtime gate, so coordination fires only inside a Port Daddy project while the daemon is running — never machine- wide-always-on. Use when the user says "install port daddy hooks", "agent CLI hooks", "interactive session hooks", "make coordination hooks fire in interactive sessions", "codex/gemini/agy hooks", or "auto-detect agent CLIs". NOT for generic software/package installation; NOT for installing the Port Daddy daemon or `pd` CLI itself (delegate to the `port-daddy` skill / `pd setup`); NOT for MCP server setup (delegate to `pd mcp install`); NOT for authoring new skills (delegate to `skill-architect` / `skill-creator`); NOT for editing the tentacle scripts themselves (owned by the Giant Squid Harness program; this installer layer is pd-adr-090).

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Agent fallback for CCSPlayerPawn_Event_KilledOther (auto-generated, category: vfunc). Locate CCSPlayerPawn::Event_KilledOther in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CCSPlayerPawn_Event_KilledOther, CCSPlayerPawn::Event_KilledOther

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Find and identify the CBaseTrigger::StartTouch and CBaseTrigger::EndTouch overridden virtual functions in the CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering server.dll or libserver.so to locate the trigger-volume touch-list add/remove handlers by RTTI-walking CBaseTrigger's and CBaseEntity's primary vtables and diffing them slot-by-slot to find CBaseTrigger's overrides, then identifying which two overridden slots implement "add pOther to the touching-entities list" (StartTouch) versus "remove pOther from the touching-entities list" (EndTouch). Trigger: CBaseTrigger_StartTouch, CBaseTrigger_EndTouch

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Interactive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually integrating an API end-to-end (use integrate-context-matic instead).

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Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus the music file the finished video must carry. Your audio never reaches the API (measured) — the visuals are written to the track's tempo, mood and sections, then your own file is laid on locally at zero cost. One job caps at 30 seconds, so a three-minute song is six jobs minimum and the cost table says that before anything is spent. Use when a user has a specific piece of music and wants visuals for it, e.g. "make a music video for this track", "visuals for my song", "an MV for this instrumental", "generate footage cut to this beat". Do not use for cheap vertical social drafts (shorts-reels), a brand film that happens to have a music bed (seedance-ad-creative), or when there is no particular audio file the finished video has to carry.

日本語の概要は準備中です。原文の説明を表示しています。