테스트·보안 스킬

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations.

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Build, deploy and start the iOS app on a connected iPhone or in the simulator. Use proactively when user asks to test, run, or deploy iOS changes.

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Report-only QA testing. Systematically tests a web application and produces a structured report with health score, screenshots, and repro steps — but never fixes anything. Use when asked to "just report bugs", "qa report only", or "test but don't fix". For the full test-fix-verify loop, use /qa instead.

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Answer questions about Voxt — its features, what shipped and when, and how to use it — grounded in three sources: the public "Voxt Announcements" chat, the Landing pages, and the user guide. Use when asked things like "what features shipped this year", "list new features", "how do I use <feature>", "what is Voxt", or any product/usage question about Voxt. Answers are cited to the source.

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Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only.

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Use when the user wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'.

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DeFi Protocol Analysis — analyzes TVL, revenue, yields, liquidity, security, governance, and competitive positioning with a composite DeFi Score (0-100)

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Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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Use when 高教社杯/CUMCM/电工杯 mathematical modeling abstracts, keywords, highlights, first-page claims, or high-density summaries need drafting, revision, audit, or style diagnosis, especially when feedback mentions too many numbers, weak focus, process-listing, empty official prose, buzzwords, missing structure, weak keywords, unsupported values, or first-page claim verification.

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Use when 高教社杯/CUMCM mathematical modeling work needs lightweight reusable paper assets, national-prize style paragraph templates, section controls, per-question code/plot/test scaffolds, evidence registries, paper fragments, reproduction scripts, or safe reuse of prior contest project structure without copying old problem-specific content.

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Automate on-target verification of a program's logic by driving its RTT console with rttsh - write Lua scripts that exercise commands and assert responses, output and timing on real hardware. Use when asked to verify target-side behavior on board, build board-level checks for firmware features, or automate hardware test runs.

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Build UI interactions whose motion comes from a physical model instead of durations and easing curves. Turn a feel described in words ("a toggle that feels heavy", "a drawer with a magnetic snap", "a card you throw into a corner", "a knob with heavy detents", "a sticky slider") into a production component driven by mass, stiffness, damping and force fields (magnets, friction, walls), on a line, in a plane or around a dial, plus a test rig that plots position, velocity and the force field, overlays the model's prediction on the measured motion, and exports the same spring to Framer Motion, react-spring, SwiftUI, Compose or CSS linear(). Use this whenever someone asks for an animation, transition, spring, gesture, drag, flick, snap points, bottom sheet, toggle, slider, carousel or any micro-interaction that should "feel" a certain way, or wants to tune or explain a spring, even if they never say "physics".

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PHP development intelligence. 12 domains, 8 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check PHP code. Domains: pattern, package, error, perf, test, security, interface, arch, idiom, anti, tooling, dependency. Stacks: laravel, symfony, codeigniter, slim, api-platform, livewire, database, queue. Integrations: arch_system generator.

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Node.js & TypeScript development intelligence. 12 domains, 12 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Node code. Domains: pattern, package, error, perf, test, async, arch, idiom, anti, tooling, module, typescript. Stacks: web-express, web-fastify, web-hono, web-nestjs, web-nextjs, web-remix, orm-prisma, orm-drizzle, auth, database, realtime, graphql. Integrations: arch_system generator.

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Mobile development intelligence. 12 domains, 9 stacks, 250+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check mobile code. Domains: pattern, package, error, perf, test, security, interface, arch, idiom, anti, tooling, dependency. Stacks: flutter, react-native, swiftui, jetpack-compose, kotlin-multiplatform, capacitor, expo, native-ios, native-android. Integrations: arch_system generator.

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Go development intelligence. 12 domains, 10 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Go code. Domains: pattern, package, error, perf, test, concurrency, interface, arch, idiom, anti, tooling, module. Stacks: web-gin, web-echo, web-fiber, web-stdlib, cli, grpc, database, microservice, cloud-native, data-pipeline. Integrations: arch_system generator.

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Updates tdmcp docs and roadmap for newly shipped or newly merged capabilities. Use when asked to update docs, update the roadmap, reconcile release docs, document new features, sync docs with CHANGELOG/package release, refresh cookbook examples after a feature wave, re-run/continue/fix a docs-roadmap pass, or check whether docs/ROADMAP.md is current. Runs a sub-agent workflow: release audit -> roadmap/docs edit + cookbook sync -> QA.

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Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).

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Orchestrate any tdmcp test-coverage work: run the coverage harness, inspect coverage gaps, plan focused Vitest/msw or bridge tests, delegate coverage writers, verify coverage deltas, and re-run gates. Use whenever the user asks to raise coverage, add broad regression coverage, create a test harness, improve tests, re-run a coverage wave, or fix a coverage gate.

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How to author one new tdmcp MCP tool the house way — the file shape (XImpl + registerX), the Python-bridge pattern (buildPayloadScript/parsePythonReport over executePythonScript), the result helpers (errorResult/guardTd/jsonResult/structuredResult), fail-forward error handling, ESM/.js + Biome style, and the offline msw unit test that proves it. Load this whenever building, extending, reviewing, or testing a tdmcp tool file under src/tools/** (and its test under tests/unit/**). Use it for every Phase-13 / v0.5.0 feature-build tool and any re-run after a gate failure.

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Restructure and enrich the tdmcp documentation site for artists — categorized/collapsible sidebar (EN+PT), interactive goal-based guide home, new step-by-step tutorials, copy-prompt/level-badge components. Use whenever the user asks to make the docs more interactive, easier to navigate, better organized, add categories/subcategories, add tutorials, improve docs UX/IA, or for follow-ups: re-run, continue, fix QA findings, add one more tutorial, re-organize a section, or update the docs structure. NOT for syncing docs with a release (tdmcp-docs-roadmap-update) or cookbook entries (tdmcp-cookbook-examples).

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Orchestrates the tdmcp feature team end-to-end: design/wireframe → build → integrate → QA → release. Use whenever the user wants to build, implement, develop, ship, or add one or more tdmcp features/tools/effects/controls/AI-prompts for TouchDesigner, or to run them through a coordinated design→develop→QA→deploy pipeline. Also use for follow-ups: re-run, continue, update, fix, or improve a feature/batch, ship the next version, or rebuild just one part of a previous run. Single feature or a whole batch.

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Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

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