1.4천건 · 관련도순
Turn a feature request into an approved demo narration, then use that script to guide implementation in an isolated workspace. Verify each demo frame against the real app and open a pull request with the supporting results.
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Review a pull request using repository checks, verified code references, and current official documentation. Assess correctness, security, structure, and unnecessary code, then post one consolidated GitHub review with inline findings and an evidence-based verdict.
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Start the introductory guidance for Ouroboros by sending `ooo` or asking to begin. This entry point passes control to the welcome skill, including before runtime-specific setup has been completed in the Marketplace plugin.
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Validate substantial Rust changes by running the repository’s prescribed checks in order. Handle check failures, review formatting changes, and report which commands ran and whether validation passed before handing the work back.
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Help text-only models work with images by converting visible text, layout, meaning, and visual clues into structured JSON. The skill also guides configuring image analysis providers and reporting unclear details from the returned evidence.
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Build, refactor, or review macOS menubar apps using Tuist and SwiftUI. Separate data models, networking, state management, and views, then align project definitions and launch scripts for consistent local builds and restarts.
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Write or revise technical documents in plain English for readers outside the field. The skill uses short sentences, active voice, consistent terms, and brief definitions while preserving facts, code, commands, and product names.
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Coordinates a named team of Codex workers with concrete responsibilities and shared progress records. It selects available collaboration tools, manages communication and separate working copies when needed, and handles integration, team archival, and cleanup after the work ends.
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Implement approved tasks through test-driven development, writing tests before code. Run all pending tasks sequentially with subagents or select tasks for direct execution, then track completion using reviews and verification evidence.
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Run the PAIDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.
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Design the first-run experience — activation path, progressive disclosure, and time to first value. Use for a user's very first session. For the mechanics of the signup form itself, use `form-design`.
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Run your first Apify Actor and retrieve results via apify-client. Use when starting a new Apify integration, testing connectivity, or learning the Actor call/dataset retrieval pattern. Trigger with "apify hello world", "apify example", "run an apify actor", "apify quick start", "first apify scrape".
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Guided journey from an app people sign up for and then quietly abandon to a sealed retention engine with a habit loop, an activated first run, and one metric the whole team trusts. Orchestrates eight skills phase by phase - hooked-ux, improve-retention, continuous-discovery, lean-ux, inspired-product, lean-analytics, microinteractions, drive-motivation - asking the user questions at every decision point and recording results in the project docs/ folder (PRODUCT.md, METRICS.md, GROW-APP-PLAN.md) so the journey resumes across sessions. Use when the user wants to lift activation and retention, design a habit loop, fix a leaky onboarding funnel, or says 'users sign up then disappear'. Do not use to fix broken UX or performance that no engagement mechanic can paper over - run improve-app first; if there is no app yet, use create-app. For one framework in isolation, invoke that skill directly.
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When the user wants to improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-strategy.
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First-time Archcore setup. Wires the host (MCP config, hooks, CLAUDE.md/AGENTS.md managed block), measures the authored context the repo already holds, then composes a first-day seed — stack rule, run guide, data-model, integrations, config, entry points, public surface, a linked architecture overview, and specs for the top hotspot modules — shown in ONE preview and created on a single confirm. Modes, named as the first word: init import converts the repo's authored context — CLAUDE.md, AGENTS.md, .cursor/rules and other agent instructions, ADR and RFC folders, contributor docs, internal pages of a published docs site, recoverable git history — into native typed documents with no import marks, staged by a plan when the volume is large; init refresh adds facts that appeared since, or drills into one domain with init refresh <domain>. Use on a fresh clone, empty `.archcore/`, 'set up archcore', 'migrate our CLAUDE.md to archcore', 'import our docs and ADRs', or to wire host configs. Not for individual docs or planning.
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Guide a brand-new SearchCLI/vs user from first-time usage questions to sign-up, purchase of Viking AI Search, AK/SK setup, and a working authenticated CLI. Use when the user says they downloaded or installed SearchCLI/vs and asks how to use it, how to start, sign up, buy, or onboard.
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Research and prepare an outside contribution to an open-source repo, then stop at a queue item for a human to approve: policy check, intake, duplicate check, reproduction, patch, the repo's own checks, an independent critic rerun, and a disclosed, hashed draft of the exact text to post. Use this skill whenever the user wants to contribute to someone else's repo, fix an upstream bug, pick up a good-first-issue, draft an AI-assisted pull request for an outside project, follow up on their own outside PR, or run a gated contribution pipeline. Triggers on open source contribution, contribute to this repo, upstream fix, outside PR, external PR, good first issue, fix this issue in their repo, contribution research, AI-assisted PR, disclose AI in PR, reply to review on my PR. Also triggers when the user names an issue in a repo they do not maintain and asks for a fix, even if they do not say contribute.
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Generate detailed service restoration runbooks with step-by-step procedures. Use when someone asks to "create recovery procedures", "restoration runbook", "recovery steps", "how to restore service", or needs tactical step-by-step procedures for recovering from disaster scenarios. This is the execution companion to the DR plan — DR defines what and when, recovery-plan defines exactly how. Requires a DR plan to exist (will prompt to run /pmstudio-dr first if missing).
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When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.
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Hire one of the eight open source AI Employees (GTM Engineer, SEO/AEO, Web Dev, Social Media, Ad Manager, Sales, Customer Satisfaction, Chief of Staff) into a local folder, prove it runs, and hand over its install prompt. Also answers which one to hire first, how a hired employee runs on a schedule, how to direct it, how it improves from corrections, and how to upgrade it. Use when the user says hire an employee, install an AI employee, set up the GTM Engineer, names one of the eight, or asks how AI Employees work. It copies files and runs self tests only. It registers no schedule and sends nothing.
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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.
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Autonomous CTF / boot-to-root campaign driver. Runs a box end to end with no operator approvals - the deterministic driver (scripts/campaign.py) owns pass state, generates the killchain board from recon, and prints the exact next action (Skill + tool) every turn. Use when handed a box/IP to own end to end, "run the ctf workflow", "root this box", "foothold to root". Single agent, wiki-first, tool-first. OSINT is OFF unless you invoke with an osint argument. Delegates box recipes to ctf-box.
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Autonomous pentest campaign driver. Runs a scoped engagement end to end with no operator approvals - the deterministic driver (scripts/campaign.py) owns pass state, generates the killchain board from recon, and prints the exact next action (Skill + tool) every turn. Use when starting or resuming a pentest, "run the pt workflow", "work this CIDR/domain", or when handed a client SoW/scope to reach a stated objective or domain admin. Single agent, refuter-verified, wiki-first, tool-first. Deliverable is a client report.
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Fix black camera/microphone preview in Flutter macOS apps launched via `flutter run` from a terminal emulator (cmux, iTerm, Terminal.app, VS Code integrated terminal, Cursor, etc.). Use when: (1) camera plugin reports initialized but preview is black with no frames, (2) logs show "initialization completed via timeout" or similar first-frame timeout fallback, (3) AVCaptureSession.startRunning() returns without error but zero frames arrive, (4) the app's Info.plist has NSCameraUsageDescription and entitlements (com.apple.security.device.camera, audio-input) are correctly set, (5) no permission dialog ever appears, (6) same failure mode for microphone. Root cause is macOS TCC attributing camera/mic requests to the *responsible process* (the parent terminal) rather than the Flutter app itself. TCC refuses to prompt because the terminal is hardened-runtime without a camera entitlement, and silently denies. Not a bug in the camera plugin — would reproduce with ANY camera code.
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