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Guided first-run that produces a real verified win in under five minutes using the skill library on a seeded offline fixture. Load when a new user asks how to start, run the demo, try agent-loom, or get a quick win. Also triggers on "quickstart", "first run", "demo agent-loom", "try the skills", or onboarding to the library. Zero external credentials required. Idempotent — safe to run multiple times.
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Compare saved benchmark runs over time to track win rate, response times, escalation rate, and LLM baseline costs. Review changes between the first and latest runs, with flags for declining win rates or rising average latency.
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Control a running iTerm2 workspace from the command line. See what each terminal pane is doing, send commands, read current and past output, arrange windows and tabs, manage tmux integration, and change preferences.
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Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.
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Trace software artifacts from source and dependencies through builds, publishing, and runtime use in CTF sandboxes. Compare intended versions with shipped files to identify the earliest divergence and document the evidence linking each stage.
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Set up an agent that replies in Slack or Microsoft Teams using CopilotKit's guided CLI workflow. It covers project preparation, agent creation, channel configuration, a persistent host process, and verification with a real message.
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Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.
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Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.
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Improve the experience after signup so new users reach their first useful result sooner. The skill reviews setup steps, checklists, empty screens, and follow-up messages, then proposes prioritized changes and metrics for measuring activation and retention.
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Set up the first NanoClaw agent on a configured messaging channel. The skill identifies the operator and DM destination, selects or creates an agent group, and sends a welcome message to verify delivery through the service.
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Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.
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Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.
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Build a runnable prototype from a plain feature request, then add capabilities one step at a time. The skill records consequential assumptions before coding, checks each step, and reports remaining placeholders, deferred work, and review limits.
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Diagnose failed GitHub Actions runs by finding the first meaningful error, separating code problems from infrastructure issues and intermittent failures, and comparing the base branch. Get a focused report explaining the PR’s involvement and recommended next steps.
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Set up recurring tasks in Superset with an agent prompt, a schedule or event trigger, and a chosen execution workspace. After explicit confirmation, create the automation, check its saved settings, and review a real first run.
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Test Omnigent’s setup, help, and terminal interactions by driving the real CLI in an isolated environment. Capture rendered screens and compare checks before and after a change to establish whether a reported interface problem was fixed.
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Run a quick first review of code changes with six specialist agents working in parallel. They check logic, input and output, shared state, resources, missing behavior, and API completeness, then combine duplicate findings into a report ranked by confidence.
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Set up a privacy legal plugin through an interview and existing documents. Capture applicable regulations, data processing agreement positions, and privacy impact assessment formats in shared settings, with options to resume setup or recheck integrations.
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Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.
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How episodic television actually gets made by a group (编剧室与制作现实) — merged from Goldberg & Rabkin (pitch as audition, leave-behind, cracking the story, beat sheet, notes discipline, production rewrites), Pamela Douglas (John Wells's room, staff-from-hell mistakes, the seven-rung ladder, step deal, the 14-day draft), Neil Landau's showrunner interviews (Lindelof, Mazzara, Katims, Bays), Evan Smith on sitcom staffing and roundtables, Richard Blum (dated), Kam Miller, and first-hand accounts by David Chase, Jesse Armstrong, Lucy Prebble, Aaron Sorkin, Julian Fellowes and Phoebe Waller-Bridge. Use when joining or running a writers' room, breaking an episode with a group, writing a leave-behind, beat sheet or outline on deadline, pitching episode ideas to an existing series, writing a spec episode, taking or giving notes, rewriting because of money, actors or locations, choosing between a staffed room and a single-author process, or asking 什么是 showrunner／编剧室怎么运作／怎么接 note.
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Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Use when a test fails intermittently in CI but passes on rerun or locally, when `hogli ci:insights` or the debugging-ci-failures skill classifies a failure as a flaky test, when given a GitHub Actions URL for a flaky job, when asked to check Trunk Flaky Tests for a test, PR, or master, or when asked to deflake, stabilize, or fix a flaky Jest, pytest, or Playwright test. Core discipline: reproduce locally before changing anything, fix the root cause (never mask it with sleeps, retries, or bigger timeouts), and prove the fix with an N-run validation loop sized to the observed failure rate. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.
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Install or verify the oma CLI and its runtimes (bun, uv, serena) before any oma-* skill, agent, or workflow runs an `oma` command. Use when `oma` is missing from PATH, when a skill says to run `oma ...` and the shell reports "command not found", or on the first task in a fresh Cursor or Grok Bot workspace.
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Use when generating or editing video with Runway models on Scenario via MCP: text-to-video or image-to-video from a first frame with Gen4.5 (cinematic motion, sequenced actions, camera choreography), or video-to-video editing of real footage with Aleph 2 (remove objects, swap products, restyle or relight shots, keyframe-pinned reference images, motion and audio preserved). Keywords: Runway ML, Gen4.5, Gen-4.5, Aleph, T2V, I2V, V2V, first frame.
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Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.
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